Sina Ahmadi
email: sina0@acm.org

Open AI and its Impact on Fraud Detection in Financial Industry

Keywords: OpenAI, machine learning, fraud, fintech

As per the Nilson report, fraudulent activities targeting cards amounted to a loss of $32.34 billion globally in 2021, a 14 % increase from the previous year. Such practices can be combated by harnessing OpenAI's powerful machine learning and automation capabilities. Such advanced technologies help financial companies avoid any potential fraud and protect their esteemed clients' interests. Through the adoption and utilization of such innovative technologies., financial institutions will be better placed to protect their customers and entities from financial losses. Digital fraudsters are skilful in identifying loopholes and have developed cunning techniques like phishing for unsuspecting victims and wittingly swindling money off them. They are also updated in using OpenAI to develop deceitful information to scam people. This has seen the emergence of names like WormGPT and FraudGPT, reliant on generative AI models used by tech corporations with fraud intents. As a result, fraud detection techniques have to evolve with time as fraudsters progressively devise new techniques that bypass old and rigid banking security protocols and learn how to convince unsuspecting individuals to dispatch their money to them.

Introduction

Due to its vast implications, fraud is one of man's biggest problems. It refers to intentionally using false details to swindle an organization, individual property or money. The COVID-19 pandemic saw an upsurge in fraudulent malpractices due to the economic recession that most unemployed individuals faced (G. [START_REF] Colvin | The pandemic may be the greatest environment for business fraud in decades[END_REF]. The pandemic had rendered thousands of individuals jobless, while those privileged to be working faced cuts in their salaries. Fraud experts say fraud springs from three elements: opportunity, pressure and rationalism (A. [START_REF] Littman | The Fraud Triangle: Fraudulent Executives, Complicit Auditors and Intolerable Public Injury[END_REF]. Fraud also occurs when a person develops an unshakeable urge or motive to commit fraud.

The likely fraud perpetrator needs to feed an unmet urge with limited resources. These unmet needs continue to grow as they are endless and different among people. They may include gambling debts, reduced household income or burgeoning medical bills. Once the person encounters such unmet needs and has limited resources to meet them, they may opt for fraud. Opportunities are expressed as a lack of internal controls or reckless management within a person that may present fraud as an easy activity. Lastly, the person rationalizes their intent to engage in fraudulent activities by assuring themselves that they urgently needed the money or would eventually pay it back. With harsh economic spells, there is increased motives and pressure to engage in fraud, pushing the fraudsters to rationalize their actions.

Fraud is common in finance and comprises money laundering, financial statements fraud, email phishing, cyber fraud, and credit card fraud. The advent of digital banking exposed financial institutions to digital fraud. It therefore becomes necessary to acknowledge that fraud management is essential in the finance docket, though it is an excruciating venture. Digital fraudsters are skilful in identifying loopholes and have developed cunning techniques like phishing for unsuspecting victims and wittingly swindling money off them. As a result, fraud detection techniques have to evolve with time as fraudsters progressively devise new techniques that bypass old and rigid banking security protocols and learn how to convince unsuspecting individuals to dispatch their money to them.

Traditional fraud detection approaches within the finance docket are rule-based, meaning that humans make the rules. Most financial institutions use such approaches. As more people opt for emerging digital technologies, fraud scenarios are projected to increase, rendering the existing rule-based approaches unsustainable and unscalable. Additionally, false positives (non-fraudulent practices termed as fraudulent) impose substantial financial losses in terms of customer complaints and transactions in the finance sector. Ciabanu's 2020 study on 1000 adult customers discovered that almost 25 % of the customers whose transactions had been falsely declined turned to competitors for the same financial services (M. [START_REF] Ciobanu | Why understanding your fraud false-positive rate is key to growing your business[END_REF]. The switch of competitors increased to 36 % for the customers between 18 and 24 years and a further 31 % for those in the 25-34 years old bracket (M. [START_REF] Ciobanu | Why understanding your fraud false-positive rate is key to growing your business[END_REF]. Such results show the profitability needed for modern fraud detection structures.

Financial, banking and fintech industries encounter various scams annually. The scams can be categorized into these classes: digital fraud, physical attacks, internal collusion and violation of the Four Eyes Rule. The last two items involve employee-based schemes or traditional malpractices.

Digital fraud, however, involves a range of online fraud activities. Machine learning and automation are needed to combat digital fraud as they have evolved into crucial business tools as fraudsters develop increasingly intricate practices. Such advanced technologies help financial companies avoid any potential fraud and protect their esteemed clients' interests. Through the adoption and utilization of such innovative technologies., financial institutions will be better placed to protect their customers and entities from financial losses.

To add to the challenges encountered by the traditional rule-based system, fraudsters need specific patterns and keep changing their hacking techniques. This renders the system cumbersome and quickly obsolete. There arises the need to change this traditional approach in any financial institution. As per the Nilson report, fraudulent activities targeting cards amounted to losses of $32.34 billion globally in 2021, a 14 % increase from the previous year (C. [START_REF] Mullen | Card industry's fraud-fighting efforts pay off: Nilson Report[END_REF]. With the increase in technological processes in the banking sector and due to the diverse payment channels, such as debit and credit cards and smartphones, the number of digital transactions has increased since 2020.

Due to such occurrences, there is a dire need to create more robust and rigid fraud detection solutions in the financing world. The onset of AI has opened a myriad of approaches to adopting and curbing these online malpractices.

Technological giants like Google, Facebook, Apple, Amazon and Netflix have also leveraged proprietary AI tools to improve their back-end and front-end financial processes. Currently, they have prioritized using AI in their financial strategies by frequently collecting and using new data to serve through AI models, which has set the bar for the economic world in fraud detection.

Fraud Detection in the Finance Industry

An article in Javelin Strategy and Research on fraud detection claims that fraud detection in financial institutions uses a brick-and-mortar model, which takes much longer to implement [START_REF] Pascual | Identity Fraud: Securing the Connected Life[END_REF]. Such long durations could be more welcoming for the financial institutions and their customers since several dire fraudulent malpractices could be affected within these durations. Fraud also hugely affects financial institutions that are involved in online payment services, mainly under the contemporary technological advancement in the business industry. For instance, almost 20 % of customers change financial institutions after encountering fraud (S. [START_REF] Sando | Consumer Preference Drives Shift in Authentication[END_REF]. The defection of such members to rival institutions causes financial losses and bad reputations to the victim institution, mainly if the trend becomes recurrent. It, therefore, becomes an essential practice for financial institutions to mount robust fraud detection structures within their systems. There are two major fraud detection approaches, the rule-based approach and the leveraging of OpenAI.

Machine Learning-Based Fraud Detection

There are hidden and disguising events in user behaviour that lack the clarity of outright evidence to be identified as fraudulent transactions. Machine learning allows for the development of algorithms that can handle massive datasets with several variables and helps identify these hidden behaviours between operator behaviour and the likelihood of fraudulent actions. Machine learning structures are more advanced than traditional rule-based structures due to their fast data processing capabilities and automation tools in data handling. For example, intelligent algorithms are excellent in behaviour analytics as they reduce the number of verification steps needed.

Financial institutions are more involved in monitoring the likely occurrence of fraudulent activities as they must identify and communicate any flagged online activity. A research by Villalobos explains a scenario that involved the programming of a machine prototype on a dataset containing transactions that had been criminally executed. The prototype used in the rule-based model helped identify the hidden relations manifested in the transactions and criminal activities. Such machine learning systems reduce the amount of work in smaller financial institutions that undertake fraud monitoring operations. The suggested solution in the article revealed that 99.6 % of money laundering transactions and cut down the reported transactions from 30 % to 1 % [START_REF] Villalobos | A Statistical and Machine Learning Model to Detect Money Laundering: An application[END_REF].

Machine learning (ML) is built on algorithms, which increase efficiency as the data size increases. The greater the data, the more the machine learning prototype grows more efficient and can differentiate the differences and similarities between different behaviours. The more the machine learning model unearths the differences between fraudulent and legitimate operations, the more its systems grow more efficient in sorting data sets into the needed classes. Machine learning models become more scalable as the customer database increases in size.

Although machine learning algorithms have numerous benefits in fraud detection among financial institutions, they carry some drawbacks that limit their application in detecting fraud. For example, one of the main disadvantages is that machine learning needs colossal amounts of data for the models to be accurate. The data threshold is manageable, but there should be ample data points that identify the legitimate causal associations in smaller financial institutions. In addition, machine learning algorithms run on actions, activity and behaviour. The model may overlook clear connections, for instance, a card used in multiple accounts, which could render the fraud detection operation inaccurate.

Article Reviews

Ayowemi [START_REF] Awoyemi | Credit card fraud detection using machine learning techniques: A comparative analysis[END_REF]) researched why credit card-related fraud detection becomes an impediment and articulated that it occurs for two main reasons. Firstly, fraudulent and regular behaviour changes constantly. Secondly, there is a massive imbalance in the datasets generated from credit card fraud. In the same vein, the approach used to sample the dataset, the selection of variables and the methods used in fraud detection also affect the fraud detection performance in transactions related to credit cards. The research investigated the performance of naïve Bayes, k-nearest Neighbour and logistic Regression on credit card datasets assumed to be largely skewed. The research also uses a hybrid technique that involves the undersampling and oversampling of skewed data. The techniques were applied to the generated data and later transferred to Python. The results demonstrated higher levels of accuracy for k-nearest Neighbour, naïve Bayes and classifiers for the Logistic Regression The research compared some of the machine learning methods leveraged while identifying fraud at Medicare. The researchers carried out a comparative study using hybrid machine learning methods that relied on four performance systems of measurements and minimization of class imbalance by deploying the 80-20 undersampling technique in tandem with oversampling. The former sampling technique had a better performance than the latter approach. The research concluded that oversampling leads to poor performance in machine learners [START_REF] Herland | Big data fraud detection using multiple medicare data sources[END_REF].

To add to this pool of research, balanced accuracy (BACC) seemed unreliable as a method of measuring the performance of models in various models and rendered it unable to reflect more realistic alterations observed in other metrics. Therefore, the undersampling technique enhanced learner performance and the supervised approaches turned out better than the hybrid and unsupervised hybrid learners. The provider section contributed to impediments in fraud detection, with somewhat specialized provider categories depicting higher performances than other general categories.

Use of Generative AI in augmenting and enhancing fraud detection strategies

Generative AI's backbone involves the use of transformer deep neural networks. One such example of generative AI is OpenAI's ChatGPT. Generative AI is constructed to provide data sequence as output and has to be trained using sequential data, like payment histories and sentences. It varies from other methods that produce single categorizations, such as fraud/ not fraud, depending on the given input and training data, that can be provided to the model in any sequence. Generative AI's yield can progress indefinitely, while other classification methods only yield single outputs.

Generative AI becomes the superlative tool needed to generate data grounded on actual data synthetically. Its development will depict essential applications in detecting fraud, whereby, as earlier noted, the number of feasible fraud samples remains little and challenging for machine learning to effectively learn from., A model can apply generative AI and use the existing patterns to develop novel, synthetic samples that pose as actual fraud samples, enhancing the fraud signals for essential fraud detection machine learning tools.

An archetypal fraud signal comprises non-fraudulent and fraudulent data. Usually, the nonfraudulent data appears first in the sequence of events and carries the actual behavioural activity of the card's owner. Generative AI can generate such payment sequences and simulate a fraud attack on the card, which would then be used to train data to help fraud detection machine learning tools and enhance their performance.

Using Generative AI to detect Fraud

One of OpenAI's criticisms is that current models can rely on incorrect outputs. This is a significant flaw that most people in financial institutions are concerned about as they never use public tools like customer chatbots to present made-up, more false information. However, the perceived flaw can be used to generate synthetic fraudulent data since synthetic disparity in synthesized output can develop exceptional fraud patterns, enhancing the end fraud defence model's detection performance.

As known to many, repetitive examples of a similar fraud signal do not always enhance detection since most of the machine learning models need a few occurrences of each entity to learn from. The variation in the developed outputs generated from the generative model increases the sturdiness of the end fraud model, helping it spot any fraud patterns in the data and identify similar attacks that would have quickly passed unnoticed if traditional processes were used. This would pose some concerns for fraud managers and cardholders as they may wonder how a fraud model trained on generated data can enhance fraud detection and any merits attached to the exercise. Unknown to them is that before a model is used on live payments, it passes through severe evaluation operations to maintain its projected performance. It is abandoned if it does not attain the expected top-notch performance and replacements are trained until the best models are found. This process is standard and is the norm for all produced machine learning models since models trained using authentic data can also produce substandard results during the evaluation stage.

Tools used in OpenAI to effectively detect Fraud in Finance

Financial institutions are overwhelmingly shifting to AI to help in efficient fraud detection.

Multiple industries including banking, fintech and e-commerce have already adopted fraud detection solutions. Using machine learning algorithms, such industries can now process huge amounts of data and detect suspicious patterns to safeguard the business from fraud.

i.

The Use of Logistic Regression in Fraud Detection Machine Learning Algorithms

Logistic regression is the supervised learning method supported by definite decisions. All obtained results are categorized as non-fraud or fraud once a transaction occurs. This technique uses a cause-and-effect relationship to generate organized data sets. The regression analysis method is more complex when detecting fraud due to the data set sizes and numerous variables. This algorithm forecasts whether new transactions will be categorized as fraudulent. The models are primarily accurate for clients from larger financial institutions. However, the general models also remain viable and applicable for use.

ii.

Using Decision Trees in Fraud Detection Machine Learning Algorithms

This AI version creates a graphic illustration of a decision-making process. They are useful tools in fraud detection, as they did in identifying the most crucial variables that led to fraud and Designing a decision tree discards any unrelated features and does not need wide-ranging data normalization. After a tree is inspected, it becomes clear why some decisions were made by relying on the group of rules initiated by a specific client. The machine learning algorithm output may surface as a model aping the decision tree, giving a possible trace of fraud based on earlier events.

iii.

Using Random Forest in Fraud Detection Machine Learning Algorithms

Random Forest Machine Learning combines decision trees to produce more accurate results.

Every tree assesses transactions for various decisions (V. [START_REF] Ayyadevara | Pro machine learning algorithms[END_REF]. Training is conducted on random datasets. Depending on the executed training offered on the decision trees, each tree classifies transactions by deeming them either fraudulent or non-fraudulent. The model is then harnessed to accurately predict the result, allowing fraud detectors to even out errors that may surface in a tree. It improves the overall performance model accuracy and sustains the ability to interpret the results and give explicable scores to the users.

Random forest runtimes are fast and can handle unbalanced or missing data. However, they have some weaknesses. For instance, when deployed in regression, they cannot predict past the variety in the training of the data and may provide overfit data sets, often termed as noisy.

iv.

Using Neural Networks in Fraud Detection Machine Learning Algorithms

They emulate the complex nature of the human brain. vi.

Natural Language Processing

World leading institutions, such as PayPal, American Express and Bank of New York Mellon, are some of the financial institutions using the power of Natural Language Processing in fraud detection efforts (OpenAI 2023). NLP extracts signals from IVR interactions, voice and chats to enable these financial companies to effectively spot and prevent suspicious fraud due to the technology's capacity to enhance routine detection.

Merits of Using AI-Powered Fraud Detection Systems

AI-powered fraud detection approaches create a more efficient strategy than the existing traditional methods as they offer intricate fraud pattern detection and real-time analysis and are adaptable to emerging fraud schemes; by reducing the associated time, budgets and false positives, OpenAI will increase the efficiency and accuracy of detecting fraud, causing to decreased financial losses emerging from cybercrimes.

From a client's viewpoint, institutions that accurately and efficiently detect fraudulent activities will prevent customers from falling victim to financial fraud. Therefore, institutions that adopt OpenAI will benefit from preventing fraud and increasing customer retention and loyalty.

Partnerships between OpenAI and Fintech companies

Since its inception, a synergetic partnership between fintech companies and Open AI has existed. The partnership is quickly changing how financial operations are being executed. These Fintech companies are innovative entities now integrating OpenAI to expand the boundaries they can achieve in their financial operations. OpenAI has been used by fintech institutions in the following ways:

i.

Spearheading Intelligent Investing

Key to this new advent is the growth of robo-advisors, whereby OpenAI's data-crunching abilities equip investors with algorithm-driven and personalized tips (P. [START_REF] Mahajanam | How AI is revolutionizing fraud detection in financial transactions and processes[END_REF]. The collaboration between OpenAI's analytical prowess and fintech's accessibility upscales intelligent investing, making intricate strategies accessible to vast audiences.

ii.

Using Blockchain to Revolutionize Transactions

Incorporating OpenAI into blockchain technology has revolutionized how transactions are carried out. OpenAI has advanced skills in understanding complex instructions and has helped streamline and secure agreements using smart contracts. The marriage has set the bar for a future controlled and managed by decentralized and transparent financial operations and increased its efficiency.

iii.

Enhancing Customer Experience

OpenAI has natural language processing techniques that improve customer interactions in fintech companies. Such natural language-reliant tools include chatbots and virtual assistants, which offer a personalized and seamless user experience when powered by OpenAI algorithms. These interfaces have redefined customer engagement by assisting in financial planning and answering queries and have made financial services approachable and user-friendly.

iv.

Fortifying Security Structures

OpenAI and fintech partnership extend to fraud prevention and risk management. Open AI has powerful algorithms that analyze existing patterns in real-time and identify anomalies and other likely frauds with utmost accuracy. This proactive measure protects financial institutions and revamps consumer trust by ensuring that the security and integrity of AI-powered financial services are upheld.

v.

Manoeuvring Regulatory Practices

As the partnership transcends time, it becomes more essential to navigate regulatory landscapes. Fintech institutions, in collaboration with OpenAI, employ various strategies to comply with the set regulations. The two must balance innovation and concurrently adhere to changing legal frameworks to ensure they remain responsible and promote sustainable growth amongst themselves.

Financial Companies using OpenAI: The Case of Stripe and Mastercard

Stripe

One financial company that has harnessed Open AI's powers in fraud detection is the Irish/American financial services company Stripe. It is among the pioneering OpenAI's GPT-4 users.

Stripe facilitates the payment of large and small businesses over the Internet. As the organization develops its ecosystem to support all elements of the payment procedures, developers become their fundamental users. The more accomplished the developers grow while enrolling in Stripe, the more Stripe expands through the digital payments realm and grows the Internet's GDP.

The shift to OpenAI began when Stripe summoned a team of 100 staff from its different departments to cease their duties and brainstorm how GPT-4 would optimize old features or develop new ones for the organization. Stripe tasked the 100 employees with dreaming up functionality and features to use in the payment platform using OpenAI's language learning model's newest generation, GPT-4 [START_REF] Boukherouaa | Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance[END_REF]). Stripe's specialists from the onboarding, support and risk sections considered where their institution would leverage artificial intelligence that comprehends free-form images and text and develops human-like responses to either change or improve workflows or features.

According to Eugene Mann, Stripe's Applied Machine Learning Team product lead, the company's mere access to GPT-4 helped them realize they had various problems that could be amicably solved using GPT. Mann stated that their primary mission was to discover workflows or products across the organization that would be enhanced using large language models and understand specific areas where the models would work well or struggle in delivering results. Stripe is a familiar user of AI as it used OpenAI's previous sequel technology, GPT-3, to aid its support team in better serving users through services like summarizing a user's query and routing issue tickets.

In the initial development process, Stripe's team assembled 50 potential applications to test GPT-4. After vigorous testing and vetting, 15 of the prototypes emerged as strong candidates that would be incorporated into the platform to serve functions that included fraud detection, support customization and answering any questions pertaining to support. Stripe uses OpenAI in the following operations;

i.

Seeking Clarity over the Users' Operations

To enhance user experience and give the expected support, Stripe has to precisely understand how each of its customers uses the platform and tailor its support accordingly. Although it may seem like an obvious step, it needs long human hours to master and effect.

Mann states that most businesses, for instance, nightclubs, keep their websites mysterious and sparse, making it challenging as one searches to discover what is happening on such platforms. However, the advent of GPT-4 has enabled Stripe to scan such websites and provide a summary that vastly outdoes those performed using human skills. Upon hand-checking the results, Stripe realized that humans were wrong, but the model deployed was the right pick. However, GPT-4 has now erased any traces of uncertainty as it produces accurate results.

ii.

Answering Queries Regarding Documentation

Yet another way Stripe supports developers is through detailed technical documentation and a strong developer support team that answers technical queries or troubleshooting-related challenges.

GPT-4 can understand, digest and provide virtual assistance. The technology understands all questions from the user and reads comprehensive documentation for them.

iii.

Detecting Fraud in Community Platforms

The need arises to control harmful or malicious actors. Stripe houses a strong community on forums such as Discord, which not only crowdsources help for niche technical queries but also enhances developers' visibility for upcoming works. However, since it operates online, malicious fraudsters gain access to such forums, mainly intending to access crucial information from community members or obtain credibility with Stripe's community team after being expelled from the platform.

GPT-4 becomes helpful in this scenario by evaluating the posts' syntax on Discord and flagging accounts where Stripe's fraud team should investigate and ascertain that it is not a fraudster in disguise. GPT-4 is also helpful in scanning inbound communications and discovering coordinated activities from suspected actors.

iv.

Future Engagements

The Stripe Team is now considering other upcoming features from OpenAI. GPT can be harnessed as a business coach that can interpret revenue models or advise financial institutions on effective strategies. As GPT grows more intelligent over time, its potential areas of applications keep growing.

MasterCard

Mastercard is another beneficiary of the new AI-powered tool in fighting financial fraud. As payment and banking security advances, scammers have opted for impersonation tactics to bypass security measures. They aim to convince people or institutions to send them funds, thinking they are legitimate people or entities. Mostly referred to as APP (authorized push payment) fraud, it accounts for 40 % of the United Kingdom's bank fraud losses and it is predicted that it could cost $4.6 billion in the U.K. and U.S alone by 2026 [26]. Ajay Bhalla, Mastercard's president of Cyber and Intelligence admits that banks find these scams challenging to detect [START_REF] Mastercard | Mastercard leverages its AI capabilities to fight real-time payment scams[END_REF]. He states that customers bypass all set security checks and send the funds themselves, saving criminals the need to breach any security measures. Online fraud erodes customers' confidence in digital financing in a digitally advancing world. Bhalla reiterates that Mastercard's mission is to build and maintain customer trust. Using the new AI technology, Bhalla believes, will help banks identify and forecast any payments linked to fraudsters and stop them early enough.

TSB's director of Fraud Prevention, Paul Davis, compares identifying fraudulent payments among the millions of transactions carried out within a day to looking for a needle in a haystack.

According to Paul, the new TSB's partnership with Mastercard will give the financial intelligence required to discover fraudulent accounts and deter any payments linked to such accounts.

Results generated from banks adopting Consumer Fraud Risk's score reveal massive success in preventing fraud, especially when deployed with various insights concerning customers and their behaviours. This has helped the banks develop fraud strategies that precisely identify various forms of fraud, mainly romance, impersonation and purchase scams. Purchase scams are the leading firms in the U.K., accounting for 57 % of all scams and a significant nuisance for the banks 1. In 2022, the U.K experienced 207 372 cases of authorized push payment scams and incurred losses of up to £485 million [26].

OpenAI as an Advantage for Fraudsters

The As financial institutions adapt AI to curb fraud, online crooks are updated on the same as they develop off-the-shelf tools. This has seen the emergence of names like WormGPT and FraudGPT, reliant on generative AI models used by tech corporations with fraud intents.

In one fake YouTube video, generative AI helped clone Elon Musk's voice and face hawking a crypto investment prospect that involved a $100,000,000 Tesla-sponsored bargain, which promised to give back double the bitcoin, dogecoin, ether or tether amount the investors would pledge. In the video, Elon was heard appreciating the interested investors and hailed the platform as an online broadcast enabling all cryptocurrency owners to increase their incomes. In this low-resolution video, Musk attributed the lack of clarity to hosting the crypto event from SpaceX. Since the video was fake, innocent and unsuspecting investors would have fallen victim to this scam. Scammers had used a similar 2022 YouTube Video (CNET 2022) he had given while on a SpaceX spacecraft program and impersonated his voice and image. Although YouTube pulled the fake video down, any investor that had sent crypto to any of the issued addresses lost their funds to innovative fraudsters that harnessed the power of generative AI. Musk remains a significant target for impersonations, as numerous audio samples are online to power AI-enabled voice clones. However, these fraudsters can impersonate almost anyone with online audio samples.

Voice impersonations are also gaining much use in scamming people through calls. The elderly American population is mainly targeted in this case. Everyone needs to be cautious about incoming calls, even when they are from what seems to be conversant numbers. Victims who have once been scammed find it challenging to trust any incoming calls due to spoofing phone numbers in robocalls, according to Kathy Stokes, director of fraud prevention programs at AARP, a lobbying and services provider with more than 38 million members aged over 50 years [START_REF] Mastercard | Mastercard leverages its AI capabilities to fight real-time payment scams[END_REF]. Stokes claims they always suspect emails and text messages sent to them, which has posed severe concerns for their primary communication channels.

Another worrying development is the new threat to the already established security structures.

For instance, Vanguard Group is a huge financial institution that has given voice recognition utmost priority to its customers. This mutual fund giant serves over 50 million investors and allows its clients to access specific services over the phone by speaking instead of answering security questions.

Vanguard Group entrusts the client's voice to be as unique as their fingerprints, according to its 2021 video promotion on YouTube (CNET 2022). The company was rallying its members to sign up for its voice recognition feature in the video promotion. However, the latest exploits in voice-cloning suggest that financial institutions should rethink such practices. Ranjan, Sardine's CEO, claims that she has seen individuals deploying voice cloning to authenticate with unsuspecting banks and. access accounts effortlessly [START_REF] Mastercard | Mastercard leverages its AI capabilities to fight real-time payment scams[END_REF].

Large and small businesses that use informal procedures to pay bills or transfer funds are also critical targets for online AI fraudsters. Since time immemorial, fraudsters have been emailing fake invoices to demand payment bills that appear to have been initiated by suppliers. The practice can reach higher levels of deceit as fraudsters can use the AI tools to call the company's employees using cloned versions of the executive's voice and order transactions or ask employees for sensitive information to conduct vishing or voice vishing attacks. According to Rick Song, CEO at Persona, impersonating an executive for highly valued fraud remains one of the biggest fears of voice recognition security measures [START_REF] Mastercard | Mastercard leverages its AI capabilities to fight real-time payment scams[END_REF].

Criminals continuously use generative AI to engage fraud-prevention specialists assigned to thwart these threats in the digital finance system. Fraud prevention specialists must verify that the customers are who they claim to be to safeguard the institution and the customers from losses from online fraud. One of the ways that fraud-prevention businesses like MiTek, Socure and Onfido verify their users is by use of liveness checks. This method requires the user to take a video or selfie photo and the fraud prevention specialists use the elements to match the live face with the face found in the ID, which the user is also prompted to submit. When they understand how the system operates, online fraudsters purchase images of driver's licenses on the dark web and use the now cheaper and available video morphing programs to superimpose the real faces onto theirs. The program also allows them to talk and move their heads behind real people's digital faces, maximizing the chances to bypass the liveness checks.

There has been an upsurge in the generation of fake faces. The fake faces are high-quality and used to mount automated attacks to impersonate liveness checks. The upsurge varies according to the industries, but the previous years have recorded significant cases. Crypto and Fintech companies have experienced the highest number of impersonated liveness check attacks. Fraud experts reported to Forbes that they suspected that well-established verification providers such as MiTek and Socure have experienced their fraud prevention metrics degrade due to the attacks. Johnny Ayers, Socure's CEO, points out that some clients need to be more active in adopting the firm's new models, which could adversely affect their performance [START_REF] Mastercard | Mastercard leverages its AI capabilities to fight real-time payment scams[END_REF]. Ayers also pointed out a top bank behind four versions, citing the dangers posed to the financial institution.

MiTek failed to comment on its performance metrics. However, Chris Briggs, its senior vice president, claims that if a particular model were developed some months ago, it would be argued that the older model performed at lower levels than the newer models. The vice president stated that the firm's models undergo vigorous training and retraining trained and retrained using lab-based and reallife data streams.

Wells Fargo, Bank of America and JPMorgan failed to remark on the impediments they faced with generative AI-powered online scam. One spokesperson from Chime, America's most prominent digital bank and a victim of significant fraud problems, also claims that the institution has not recognized any upsurge in generative AI-powered fraud attempts [START_REF] Vanguard | How voice verification works[END_REF].

Online fraudsters behind the growing financial scams vary from solo individuals to well-organized groups made of hundreds of tech gurus. The organized online groups work in multi-layered structures and have adept members, with data scientists onboard. They own their command-and-control centers.

Some members are only tasked to identify leads by phishing phone calls and emails. When their phishing attacks get an unsuspecting customer, they hand them over to the next colleague in line, who masquerades as a bank branch manager and attempts to persuade the victim to transfer the money from the account. One of the critical steps in the scamming process involves persuading the victim to install a program like Citrix or Microsoft TeamViewer that allows them to control their computer.

With such levels of control on the victim's computer, the online fraudsters further stretch to carrying out more purchases and withdrawing funds to other addresses.

OpenAI has attempted to develop precautions that hamper people from using ChatGPT to commit fraud. For example, the model immediately declines any attempt to initiate ChatGPT to ask an individual for their account number. OpenAI recognizes fraudsters' possible misuse of the platform and has a safety and misuse policy on its website that reads. There is no silver bullet for responsible deployment, so we try to learn about and address our models' limitations and potential avenues for misuse at every development and deployment stage." [START_REF] Haverstock | Fintech's Fraud Problem: Why Some Merchants Are Shunning Digital Bank Cards[END_REF].

Meta, on the other hand, released a language model, Llama 2, which is even easier to use for advanced criminals due to its open-source nature, which displays all its deployed codes. This expands the possible number of ways online fraudsters can tailor it to their advantage and spell doom on Fraud prevention organizations are rapidly trying to innovate and remain updated by increasingly assessing new data types to identify possible fraudsters. Defining people as being who they are online is a challenging feat to achieve and calls for the use of AI. AI will be needed to combat the generative AI-powered trickery from the fraudsters.

Summary

OpenAI is a revolutionary tool in finance. However, its usefulness will depend on the parties being more conversant and updated about leveraging its powerful tools. Its deep machine learning tools are used in the financial sector in various operations, including using blockchain to revolutionize transactions and chatbots to enhance customer experience. Machine learning has also been a cheat code in fraud detection, as financial fraud detectors harness the power of neural networks, decision trees, algorithms, natural language processing and machine learning to develop robust security structures that thwart fraud attempts. International companies like Stripe and Mastercard can attest to the benefits of using OpenAI in detecting fraud and they hope to upscale their AI operations into their business. However, if OpenAI lands in the wrong hands, it can be used to swindle money from these financial institutions effortlessly, as depicted by the emergence of the terms WormGPT and

FraudGPT. Numerous cases have been reported involving use of generative AI to clone voices and images or even generate convincing texts that con unsuspecting individuals or organizations. Since fraudsters will always be on the move to curb their economic needs, fraud detection specialists must proactively use OpenAI to curb these attacks and protect their financial institutions from incurring huge losses through online fraud.

  were 97.92 % and 97.69%. According to the comparative results, k-nearest Neighbour outperformed the naïve Bayes and Logistic Regression algorithms.Research carried out by Bauder et al. focused on the alleged fraud experienced at Medicare.

  developing a framework used in identifying fraudulent transactions. Decision Tree algorithms come into play while classifying atypical activities in any transaction an authorized user initiates. The algorithms house trained constraints that are essential tools in fraud classification on the dataset. The algorithms are used in the regression or classification extrapolative modelling challenges. They are fundamental rule sets designed to use fraud allegations involving clients.

  Financial institutions use it to parse antique databases of preceding transactions, inclusive of those predetermined as fraudulent transactions. Each transaction a neural network processes upsurges its accuracy levels in detecting future frauds and incorporates it into its vast repository of historical information, enabling the model to learn new and existing patterns of habitual fraudsters continually. Neural networks are designed to function similarly to the human brain. They utilize various computation layers. They also use cognitive computing that aids in developing machines that can use self-learning algorithms that involve data mining, processing of natural language and recognition of patterns (D. Graupe 2016). Neural networks pass through multiple layers during the data training process. They however, give more accurate results than other models since they use cognitive computing and learn from the patterns of authorized behaviour. They are therefore able to distinguish between non-fraud and fraudulent transactions. They blend into the change in the behaviour of what is assumed to be standard transactions and identify types of fraudulent transactions. Neural networks are fast and function in real-time. v. Deep Learning Mastercard is one of the leading users of AI in preventing card-related fraud. The adoption of AI technology has helped Mastercard reduce occurrences of false declines. Through the leveraging of deep learning models that progressively learn from the organization's 75 billion transactions processed annually across its 45 million locations worldwide, the AI system uses a constantly flowing stream of data and self-searching algorithms to make its decisions (OpenAI 2023). The results are hugely impressive, significantly reducing fraudulent practices and false declines for Mastercard.

  Mastercard adopted the use of AI technology in the last decade. Today, AI has evolved into a foundational technology deployed all over Mastercard's operations and has become a game-changer in identifying fraud patterns. The new AI-powered cybersecurity solutions have saved over $35 billion in fraud losses in the last three years [26]. It uses AI to help banks envisage upcoming frauds in real-time before any funds are transferred from a victim's account. If all U.K banks successfully adopt the new technology, the Trustee Savings Bank predicts a decrease in scam losses of up to $100 million [26]. Ranging from simple enticing scams to fictitious online frauds, impersonation scams of various forms have hurt businesses and individuals over recent times and reduced the confidence of those yet to be scammed. However, the situation is changing as financial institutions like Mastercard have reinforced the fight against online fraudsters using a new Consumer Fraud Risk Solution. By using the organization's new AI capabilities and its exemplary network monitoring of account-to-account payments, the new technology helps financial institutions look for any impending fraud. Mastercard has partnered with nine UK banks, including Bank of Scotland, TSB, Lloyds Banks, Monzo, Halifax and now uses large-scale payment data in picking out actual payment frauds before initiating a funds transfer from any account.Organized fraudsters have scammed unsuspecting individuals through a series of assumed mule accounts by disguising them as trustworthy parties. To battle the trend, Mastercard collaborated with United Kingdom Banks to track the flow of funds through these fraud accounts and lock them out. Using insights gained from the tracking activity and supporting them with unique analysis factors like payment values, payee history, account names, payer details and payee's links to accounts linked with scams, Mastercard's AI tool gives banks the needed intelligence to intervene in real-time and thwart any suspicious payment in time. Trustee Savings Bank is among the first beneficiaries of the new revolution. The bank has adopted Mastercard's Consumer Fraud Risk Tool. In its first four months, the bank attests that the new tool has revamped its fraud detection capabilities. Based on its reports, in the U.K., the amounts that would have been saved from scams in a year is £100m[26], should all banks adopt the solution. Other banks have on-boarded the process and Mastercard looks to scale the solution to other international markets.

  photos to curate a high-quality, deep fake video. However, AI can now complete such tasks using a

  unsuspecting victims. These fraudsters can develop malicious AI tools on top of the model. Amid the insecurity concerns, Meta CEO Mark Zuckerberg said Llama is open-sourced to enhance its security and safety. According to Zuckerberg, many people can scrutinize open-sourced software to highlight and fix issues.


Sina Ahmadi

Elastic Data Warehousing: Adapting To Fluctuating Workloads With Cloud-Native Technologies

Keywords: Data Warehousing, Artificial Intelligence, Data Integration, Automation, Cloud Native

This research focuses on the development of elastic data warehousing while adapting to changing workloads with the help of cloud-based technologies. The traditional methods of data warehousing need innovative and creative strategies in order to improve their efficiency. Thus, this research focuses on analyzing innovative methods which can improve the future of data warehousing, such as machine learning, encryption, artificial intelligence, etc. Moreover, the study also focuses on specific industries that require customized solutions to data warehousing. These include the manufacturing, finance, and healthcare industries. The study uses qualitative data gained through a comprehensive review of literature. The findings reveal a great level of significance of modern data warehousing techniques that assist in improving the overall efficiency of traditional methods.

Introduction

Elastic data warehousing can be defined as a process that enables a database to adapt to the growth in data rate, data volume and concurrency without affecting the overall performance. Such a data warehouse plays a vital role in providing dedicated resources for undergoing different business operations within the organization and managing all the tasks [START_REF] Georgiou | Enabling workload scalability, strong consistency and elasticity with transactional database replication[END_REF]. In this digital world, data warehousing has been advanced with the help of advanced and modern technologies like clout-native technologies that are helpful in managing workloads effectively and efficiently. These technologies play a vital role in providing flexibility and scalability in the organization. The purpose of this research study is to explore the role of cloud-native technologies in enhancing elastic data warehousing. [START_REF] Sleek | What are the Advantages of Building a Cloud Data Warehouse?[END_REF] In this digital world, organizations have to go through impulsive workloads that necessitates the use of data warehousing strategies for the purpose of workload management. Elasticity is the key of cloud-based technologies that allows the organizations to make their internal and external structure strong and meet the changing demands of the organizations. This research aims to go through cloud-based technologies such as containerization that play an important role in shaping the business environments in terms of managing data. With the help of such tools and technologies, organizations can ensure enhanced performance, efficiency in business operations and cost effectiveness to meet the requirements of data collection, management and maintenance.

Literature Survey

Challenges in Traditional Data Warehousing for Fluctuating Workloads

Data warehousing encounters many challenges like scalability and speed limits, unreliable results, high costs, inflexibility, issues with data management, etc. Research conducted by [START_REF] Liu | Towards Elastic Data Warehousing by Decoupling Data Management and Computation[END_REF] in this regard, focused on the challenges and issues with data warehousing, mainly in terms of big data. According to the research, big data is characterized by the aspects of value, veracity, volume, variety, and velocity. Traditional methods of data warehousing are usually based on multidimensional types of data. However, the use of new data types is different from traditional methods of data warehousing. This creates difficulty in managing the workload. Thus, every company needs big data analytics to overcome this issue. Another research conducted by [START_REF] Hariharan | Trends in Data Warehousing Techniques[END_REF] focused on the challenges encountered in big data warehousing. The research showed that due to the dynamic and complex nature of supply chains, today's firms need proper data solutions that can help integrate their big data sets and manage big data analytics. This helps them ensure that proper protective efforts are made rather than reactive ones. The researchers also developed a proof-of-concept BDW (Big Data Warehouse) that can help companies manage data requirements in the area of data warehousing. Overall, the use of data-driven, user-focused, and goal-driven strategies is necessary to manage the issues encountered in data warehousing.

Elasticity in Data Warehousing: Conceptual Framework

The elasticity aspect in the area of data warehousing is very important since it helps in provisioning server resources when required. Then, it reallocates all the resources in order to manage the workload. Overall, this elasticity helps ensure the high scalability and performance of data warehousing systems while simplifying the aspects of capacity tweaking and planning.

The same aspect was explored by [START_REF] Ning | Dual access control for cloud-based data storage and sharing[END_REF], who focused on managing and maintaining elastic data warehousing. According to the researchers, traditional data warehousing systems could be more flexible to manage resource control. This restraints both the users and the cloud providers to manage the desired quality of service and optimize the total cost. Another study was conducted by [START_REF] Arora | Mechanism for securing cloud based data warehouse schema[END_REF], which focused on the elasticity of data warehousing.

According to the research, an elastic data warehouse has the ability to quickly adapt to growth in concurrency, data volume, query intensity, and data rate. Moreover, it can also provide the required resources for diverse workloads and utilize additional resources whenever needed. Moreover, their elasticity also helps them easily adapt to fluctuations in content and data structure without needing any new data storage structures, additional data migration, and schema redesign.

Key Cloud-Based Technologies for Elastic Data Warehousing

Different cloud-based technologies can be used in elastic data warehousing. Cloud data warehouses can help by using powerful computing methods that support streaming data. In this way, they help make queries in real-time. As a result, users can use and access data much faster than with traditional methods of data warehousing. This allows the users to gain more accurate and correct insights faster and make better business decisions. Research by [START_REF] Agiwal | Napa: Powering scalable data warehousing with robust query performance at Google[END_REF] was also conducted in this regard. According to the research, cloud-based technologies in data warehousing can provide services in an open network [START_REF] Ning | Dual access control for cloud-based data storage and sharing[END_REF]. Today, many cloud-based technologies have been developed in the area of data warehousing. These include Amazon Redshift, Azure Synapse Analytics, Google BigQuery, Snowflake, etc. All such data warehouses ensure high levels of scalability, security, and cost-effectiveness. The use of such cloud-based systems can help in improving the overall efficiency of data warehousing. Arora and Gosain (2021) also focused on this aspect. According to the researchers, an enhanced encryption model can be used in this regard to secure data warehouses [START_REF] Arora | Mechanism for securing cloud based data warehouse schema[END_REF].

Scalability and Resource Management in Elastic Data Warehousing

The aspect of scalability mainly relates to the ability of a data warehouse to manage large volumes of unstructured, structured, or partially structured data. On the other hand, resource management is a kind of managerial activity that uses different data management tools in order to manage the data resources of a firm in order to meet its organizational objectives. One such research was held by [START_REF] Agiwal | Napa: Powering scalable data warehousing with robust query performance at Google[END_REF] who focused on developing a scalable data warehouse to improve the performance of Google. The researchers mainly developed Napa, which is a kind of analytical data management system that enhances the scalability of warehouses at Google [START_REF] Agiwal | Napa: Powering scalable data warehousing with robust query performance at Google[END_REF]. It also facilitates robust query performance and offers a great level of flexibility. Resource management is also a very important activity in data warehousing. This is because the adequate management of resources ensures that the system runs smoothly without any operational difficulties. It also helps in enhancing the visibility of the data assets of a firm, which makes it easier for individuals to confidently and quickly find accurate data for analysis. This data visibility in turn helps a firm in becoming more productive and organized, which assists the workers in finding the data they require to perform their jobs in the best way. According to Bogojevic (2020), the aspect of resource management in the arena of data warehousing can help in managing costs, size, and time constraints [START_REF] Bogojevic | Project management in data warehouse implementations: a literature review[END_REF]. Overall, the use of resource management techniques can assist in meeting the requirements of data warehousing.

Best Practices for Designing and Managing Elastic Data Warehousing Systems

In order to manage flexible data warehouses, it is important to integrate different efficient practices. For instance, a firm should always align its objectives within each department in order to ensure smooth data warehousing. Moreover, they should also develop some disaster recovery protocols in case of data breaches. The first and foremost technique is to choose the most accurate warehouse architecture. The next step is to focus on the aspect of data cleaning. According to [START_REF] Huang | Blockchain-based data management for digital twin of product[END_REF], the use of blockchain-based data warehousing systems can also prove to be highly beneficial in this regard [START_REF] Huang | Blockchain-based data management for digital twin of product[END_REF]. Such systems can help a firm in improving its decision-making procedure while cutting extra costs. Therefore, such systems should be implemented by firms operating complex data warehouses. Another important way is to manage the process flow in data warehousing. This procedure is mainly based on four steps. The first step focuses on extracting and loading the required data. The second step is to clean and transform the data. This is very important as it ensures the smooth transformation of data into the data warehouse. The next step focuses on creating a backup for the data and archiving it. The last step is to manage the queries and direct them to the specified data sources. Garani et al. ( 2019) also focused on this smooth procedure of managing the process flow. The researchers emphasized the importance of ensuring the smooth transformation of data into the warehouses [START_REF] Garani | A data warehouse approach for business intelligence[END_REF].

Future Trends and Innovations in Elastic Data Warehousing

The area of data warehousing will experience many innovations and creative trends. For instance, artificial intelligence is expected to play a very crucial role in improving data warehousing. The incorporation of natural language processing, machine learning, and many other techniques will make the current data warehouses more effective and intelligent at interpreting large volumes of data. Pandian (2019) also focused on proposing the use of AI in a smart warehousing environment. The researcher stated that it is important to modify the current operations in data warehousing with the help of innovative techniques like AI [START_REF] Pandian | Artificial intelligence application in smart warehousing environment for automated logistics[END_REF]. It can help in enhancing the potential of functioning of data warehouses in the areas of coordination, management, and logistics. Another important trend that is likely to emerge in data warehousing is zero-copy data sharing. This trend will help in reducing the costs and risks associated with traditional methods. One platform trying to offer this feature is Snowflake. This method helps firms in sharing read-only database items with other firms without transferring the real data. The major advantage in this regard is the separation of compute and storage. According to [START_REF] Bell | The snowflake data cloud[END_REF], the Snowflake data cloud can help in bringing huge innovation in the arena of data warehousing [START_REF] Bell | The snowflake data cloud[END_REF].

Problem Definition 2.1. Challenges in Traditional Data Warehousing

Modern organizations need a high level of scalability and agility but they both lack in traditional data warehousing especially when it comes to the security industry [START_REF] Nambiar | An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management[END_REF]. It is obvious that it is difficult to adjust according to the evolving changes and implementing them is also crucial just because of the rigid architecture. Thus, high speed and scalability are necessary to perform errorfree operations within an organization. Another challenge regarding traditional data warehousing is the inefficiencies with non-technical users. There is a need for data teams that could manage and fulfill all the requests related to the data. Thus, it results in inefficiency and delays in tasks.

Lack of Agility in Adapting to Evolving Business Needs

The agility of a business network is the most important thing to consider when it comes to meeting the market needs and demands. In this modern world, industries are becoming datacentric where there is a need to pay a great attention to data management and maintenance for managing business workloads effectively. For this purpose, cloud-based technologies must be implemented to grow a business and make it competitive because traditional data warehouses lack agility [START_REF] Gikandi | PROMOTING COMPETITIVE ENTREPRENEURSHIP: USING CLOUD COMPUTING FOR OPTIMAL BUSINESS INTELLIGENCE IN SMALL AND MEDIUM ENTERPRISES[END_REF].

Inflexibility of Traditional Data Warehouses

It is stated that modern organizations must be flexible enough to adjust according to the changing market trends and demands. There is inflexibility in traditional data warehouses because of their rigid structures which makes them expensive as well as time-consuming [START_REF] Roy | Efficient OLAP query processing across cuboids in distributed data warehousing environment[END_REF]. As a result, they fail to meet the requirements of real-time data collection, management and maintenance. To manage such data warehouses, Oracle, Teradata or SQL Server is required but their licensing costs and maintenance costs are high. Thus, the implementation of cloud-based technologies is necessary to reduce these expenses and enhance flexibility in businesses.

Increased Data Quality Concerns

An organization uses different sources to collect the data, which is available in different structures. It is difficult for traditional data warehouses to maintain the quality of the data because errors and discrepancies occur when the data is collected from different sources. This issue leads to inaccurate data results which in turn affect the decision-making processes of an organization. Another issue that occurs is data duplication, which is caused when the data is not updated timely.

Methodology/Approach

Research Design

This research utilizes a systematic literature review-based research design. This is because it helps in analyzing the changing attitudes of the target study. Besides, it also helps in gaining familiarity with the current research area. It helps in understanding the theories supporting the topic, allowing us to place the current research question into context. The approach of a systematic literature review is based on an organized and methodical search for relevant information while ensuring the analysis of a diverse range of opinions. Overall, it helps in the exploration of major gaps, trends, and key concepts in the current literature, which assists in developing the groundwork for a detailed acknowledgement of flexible data warehousing in the case of changing workloads.

Data Collection Methods

For the purpose of collecting data regarding the alignment of flexible data warehousing with changing workloads with the help of cloud-based techniques, a systematic approach has been developed. The secondary data collection method was used in this research to gain relevant information from existing literature. This section is based on a detailed depiction of the data collection procedure, showing the method used to select and assess the relevant journal articles.

Development of a Systematic Search Strategy

Before collecting data, a search strategy was developed to select relevant articles from different databases. The use of diverse databases was ensured in order to ensure the use of diverse opinions and perspectives regarding the research topic. Moreover, the literature search covered conference proceedings, journal articles, and peer-reviewed articles.

Inclusion Criteria

An inclusion criterion was also developed while collecting data. Only those articles were selected for the research that included the specific keywords of this research, i.e., elastic data warehousing, cloud-native technologies, and fluctuating workloads. Moreover, the research only included articles from 2019 to 2023. This helped in ensuring that only up-to-date data was gathered for the research study.

Keyword and Term-Based Search

The search procedure for the research was based on the use of specific keywords from the title of the research. These include: elastic data warehousing, cloud-native technologies, and fluctuating workloads. The use of specific term-based search helped in gaining the most relevant data for the research, which adds to its credibility and reliability.

Source Selection and Examination

The sources used for this research were analyzed in detail in order to gain useful insights into the aspects of flexible data warehousing. The analysis procedure was based on a critical examination of the selected articles while analyzing their credibility, reliability, and relevance to the research.

Data Analysis Technique

The information gathered from different articles was assessed with the help of thematic analysis. This technique provides an organized approach to synthesizing and organizing various sources, which makes it easy to analyze the patterns, gaps, and trends in the existing literature. Through this method, the key themes of the research have been identified, which provides a detailed analysis of particular aspects of the research questions.

Thematic Analysis

Thematic analysis is a very common but crucial way of analyzing research data. It is based on the analysis of the major themes or patterns in a research area. It helps in dividing the collected data into different themes, which makes it easier to analyze and assess them in detail. The use of this technique has been very helpful in the current research since it organized the whole dataset into a particular form.

Identifying Key Themes

The most important aspect of this research was to develop different themes based on the research topic. For this purpose, different sources were analyzed to identify the most important topics in this area. The major themes that were developed in this research are based on the challenges in traditional data warehousing, elasticity in warehousing, scalability and resource management, best practices for managing data warehouses, and future trends in this area.

Facilitating a Deeper Understanding of the Findings

The analysis procedure did not only involve the categorization of the major themes but was also based on the development of useful insights from the literature. The use of thematic analysis helped in the exploration of major aspects within each of them, which helped in gaining useful acknowledgement of the research topic.

Comparative Discussions

After developing the major themes for this research, a comparative discussion was also conducted in order to compare the major aspects in these themes. In this way, the differences and similarities among all the themes were identified, which helped in presenting the findings in a more organized manner.

Results and Discussion

Elastic Data Warehousing Frameworks

Amazon Redshift

There are a lot of elastic data warehousing frameworks among which Amazon Redshift is the one [START_REF] Armenatzoglou | Amazon Redshift re-invented[END_REF]. It can be defined as a framework that is responsible for maintaining scalable data within the Amazon Web Services ecosystem. It is efficient in handling and managing large sets of data as it uses a columnar storage approach where the data is stored in columns. Massively Parallel Processing (MPP) architecture has been employed in Amazon Redshift that plays a vital role in maintaining a large computing network and maintaining all the tasks among the connected users with the purpose of efficient and rapid query execution. This framework allows the organizations to adjust the sizes of clusters on the basis of organizational workload. This feature makes it cost-effective and efficient to get flexibility in data management.

Snowflake

Snowflake is one of the best data warehousing frameworks that is helpful for organizations to manage, handle and analyze their data to enhance their overall operations [START_REF] Gupta | A comprehensive survey to design efficient data warehouse for betterment of decision support systems for management and business corporates[END_REF]. Its storage methods and computing resources make it distinctive among other data warehousing frameworks. These features support scalable data which in turn offers flexibility at higher levels. The multi-cluster feature of this framework provides independent access to all the nodes connected with the network which results in enhancing the overall performance of the network as well as the data. Moreover, automatic scaling is also offered by Snowflake that helps in meeting the requirements of workload management along with maintaining cost-effectiveness and efficiency of the operations. Security and privacy is also the main concern of this platform which secures the sensitive data.

Google BigQuery

BigQuery is a modern data warehousing platform that is highly scalable and supports real-time analytics and operations in cloud computing [START_REF] Ali | Big Data analysis using BigQuery on cloud computing platform[END_REF]. This platform comes with an easy-to-use interface that makes it useful and no technical knowledge is required to use it. Standard SQL queries can be used for storing the data no matter how big the data is. This framework is based on the serverless model which makes it the best for the organizations in this modern world to fulfill all the needs regarding data warehousing. Its capabilities are enhanced with the help of cloud computing services which play a vital role in the processing and analysis of big data. The data in different structures and collected from multiple sources can be managed with the help of federated queries.

Scalability Dynamics in Cloud-Native Solutions

Overview of Scalability in Cloud-Native Environments

Scalability can be defined as one of the major benefits offered by cloud-native development [START_REF] Henning | A configurable method for benchmarking scalability of cloudnative applications[END_REF]. Organizations can scale their businesses with the help of containerization and microservices. These services help in allocating resources when necessary which results in optimizing costs as well as enhancing the overall performance during traffic spikes. The IT resources can be increased or decreased with a scalable cloud environment when the reason is to meet the changing needs and requirements of the market. Today, organizations are seeking scalable cloudnative environments that support scalable computing and storage operations. This, in turn, contributes to the overall success of the organization. Features like container orchestration are also helpful in scaling up cloud computing services to meet workload demands and enhance customer satisfaction

Mechanisms for Dynamic Scaling

Dynamic scaling plays a vital role in enhanced cloud-native environments because it involves those mechanisms that are helpful in automating system resources for the purpose of managing workloads [START_REF] Singh | Research on auto-scaling of web applications in cloud: survey, trends and future directions[END_REF]. In this concern, auto-scaling is applied, which includes predefined guidelines and policies for the removal and addition of cloud computing services in real time. This process makes sure that the resources are allocated efficiently when the activity is increased and demand is decreased. Dynamic scaling is usually supported by container orchestration platforms as they play an important role in deploying and managing the applications that support scaling of integrated applications and meet the customer demands at the same time.

Benefits of Scalability in Cloud-based Network

It is known that scalability offers a number of benefits to organizations when it comes to its involvement in cloud-native networks [START_REF] Henning | A configurable method for benchmarking scalability of cloudnative applications[END_REF]. Enhanced performance is one of the major advantages as it lets the organizations handle the workloads effectively and efficiently without any delay. Another benefit is cost effectiveness as cloud-native solutions provide the organizations with the opportunity to allocate their resources without investing a lot of money especially when it is a low activity period. The cost of infrastructure is reduced when a scalable cloud-based environment is implemented. Thus, the organizations can spend their money on innovations and advanced technologies to enhance the business operations and performance.

Assessing Adaptation Efficiency through Performance Metrics and Benchmarks

Performance Metrics Overview

The performance metrics play a vital role when it comes to measure the efficiency of a cloudbased network within an organization. These metrics ensure that this system is reliable enough to manage workloads without compromising the quality of the data [START_REF] Aslanpour | Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research[END_REF]. These performance metrics may include utilization of resources, throughput, response time etc. The response time can be defined as the period from the initialization of the query till the response of the system to that query. While, throughput includes the calculation of the number of queries made in a specified time period. When it comes to resource utilization, it explains the employment of resources when dealing with workloads.

Benchmarks as Evaluation Tools

Benchmarks are essential tools that are helpful in evaluating the overall performance of a system according to some predefined industry standards to regulations. They measure the computing resources performance and have also been applied to cloud resources in the network. There are a number of benchmarks in cloud-based data warehousing, for example, OLTP-Bench is a common benchmark that plays a vital role in controlling workload skew, mixture and transaction rate during the execution of a task. Moreover, the TPC-H benchmark is used for enhancing the performance of the database as a whole. Benchmarks also come with some challenges because of the diversity in workloads, that's why it is important for the organizations to be selective when it comes to choosing benchmarks. They must select the ones that are in accordance with the organizations' specific use cases.

Security and Compliance in the Cloud

It is important to consider the factors of security and compliance when it comes to cloud computing because it is necessary to maintain confidentiality for the sensitive data of the business as well as the users connected with the network. Cloud-based technologies offer a lot of benefits but they also come with a number of challenges that are needed to be addressed for an efficient network.

Challenges in Cloud Security

One of the major challenges in cloud security is related to the protection and privacy of the data. The confidentiality of the data becomes complex and intricate because it travels from a lot of sources i.e. cloud services and services, that's why it is difficult to maintain efficiency of such data. The major challenges that occur in this concern include data breaches and unauthorized access which may result in tangible hazards. It is important for the cloud service providers to deal with such cyber threats and implement the shared responsibility model in the cloud-based environment where both customers and cloud services providers are responsible for securing data.

Strategies for Security in the Cloud

When the security concerns in the cloud are increased, it is important for the organizations to focus on implementing advanced and latest cloud-based technologies. Access to users must be limited so that the data could be protected from unauthorized access. Identity and Access Management systems must be implemented by the organizations to limit the controls over data handling and management so that only authorized individuals could have access to the sensitive information. Encryption techniques must be applied in this concern for the protection of existing and newly-added data. When the data is transferred from one user to another, the data is encrypted and can only be decrypted with the help of specified keys. Moreover, the organizations must monitor and audit their data regularly so that the discrepancies in data could be resolved timely.

Compliance in the Cloud

The cloud-based networks come with a number of compliances as well so it is important for the organizations to focus on these compliances and stay aligned with geographical as well as industrial rules and regulations. Some common compliances include the General Data Protection Regulation (GDPR), the Payment Card Industry Data Security Standard (PCI DSS) and the Health Insurance Portability and Accountability Act (HIPAA), where they all are related to the protection of data within an organization. In cloud computing, compliance risks may lead to legal penalties as well as financial loss if industry standards are not being followed. It is necessary for the policy-makers to focus on implementing robust security controls so that industry standards could be met.

Challenges and Opportunities in Elastic Data Warehousing 4.5.1. Challenges in Cloud Computing

• Security Concerns Cloud computing comes with security challenges when they are implemented in small or large organizations. It is important to protect this data whether it is at rest or is getting transferred from one node to another. For this purpose, the shared responsibility model is the best to implement where both the customers and cloud services providers are responsible for the maintenance and security of the data so that it could be protected from cyber threats.

• Cost Management

Cloud computing comes with a lot of benefits but it is expensive to manage data in it. It is important for the organizations to optimize the process of resource allocation by choosing the best pricing model and focus on cost-tracking techniques so that expenses could be managed effectively. It is stated that cost efficiency of a cloud model is helpful in enhancing the overall performance of a business.

• Interoperability and Vendor Lock-In Avoiding vendor lock-in and the interoperability between different cloud-based platforms are also included in the challenges associated with cloud computing. A cloud computing network must be able to move data and applications to other cloud platforms, ensuring flexibility and mitigating the reliance on just one provider. Thus, organizations must implement a multi-cloud approach in this concern.

Opportunities in Cloud Computing

• Innovation Acceleration Cloud computing not only comes with challenges but also opportunities like innovation acceleration [START_REF] Koehler | Real World Applications of Cloud Computing: Architecture, Reasons for Using, and Challenges[END_REF]. Organizations can introduce new prototypes and focus on developing innovative solutions by using advanced cloud computing services and tools. SaaS, PaaS and IaaS are helpful for the organizations to innovate solutions and make them competitive in the market.

• Global Accessibility and Collaboration

The organizations that use cloud computing services can get connected with other cloud networks globally which means they can share their data and resources worldwide [START_REF] Sunyaev | Cloud computing[END_REF]. Team members from different corners of the world can get connected and work seamlessly. Thus, cloud computing provides the opportunity to collaborate in real time, enhance communication, and share resources for the purpose of increasing the overall efficiency and productivity.

Conclusion

To conclude, the use of modern data warehousing techniques including cloud-based technologies can help in improving the efficiency of overall systems. The research has shown that the utilization of different methods like data virtualization and gradual migration can help in developing efficient data warehouses. While many issues are present in this regard, there are many opportunities for technology firms to dig deeper into the aspects of cost optimization, scalability, and innovation by using modern innovative techniques like machine learning and AI. It is also seen that it is important to enhance the operational efficiency of data warehouses by using modern techniques. It can also help in improving the working operations within the digital world by integrating new and novel approaches of data warehousing.

Future Scope

Future researchers should focus on exploring the integration of modern innovative techniques in data warehousing. They should try to develop such systems that overcome the challenges encountered in traditional data warehouses. More specifically, they should delve deeper into the implementation mechanisms of the aspects of machine learning, automation, and AI in data warehouses. Moreover, the implementation of these mechanisms in specific industries should also be ensured. The development of customized systems for each industry will help in ensuring that the unique requirements of each industry are being met by upgraded data warehousing methods.

Figure 1 :

 1 Figure1: Data Warehouse Using Cloud Computing[START_REF] Sleek | What are the Advantages of Building a Cloud Data Warehouse?[END_REF]

Figure 2 :

 2 Figure 2: Architecture of a Traditional Data Warehouse[START_REF] Storagenewsletter | Elastic Data Warehouse Through AWS Marketplace by Snowflake Computing[END_REF]

Figure 3 :

 3 Figure3: Elastic Data Warehouse[START_REF]Benefits of Cloud Data Warehouse for Your Business[END_REF]

Figure 4 :

 4 Figure 4: Cloud-based Data Warehouse[START_REF] Roy | A Complete Guide To Data Warehousing -What Is Data Warehousing, Its Architecture, Characteristics & More![END_REF]

Figure 5 :

 5 Figure 5:Resource Management in Data Warehousing[START_REF] Roy | A Complete Guide To Data Warehousing -What Is Data Warehousing, Its Architecture, Characteristics & More![END_REF]

Figure 6 :

 6 Figure6: Designing and Managing Data Warehousing[START_REF] Pragna | How to Design a Data Warehouse-Best Practices and Examples[END_REF]

Figure 7 :

 7 Figure 7: Machine Learning in Data Warehousing[START_REF]Machine Learning Juggernaut[END_REF]

Figure 8 :

 8 Figure 8: Challenges in Traditional Data Warehousing[START_REF] Richman | What Is A Traditional Data Warehouse? Examples & Challenges[END_REF]

Figure 9 :

 9 Figure9: Data Quality Concerns in Data Warehouses[START_REF] Pandey | Data Quality in Data warehouse: problems and solution[END_REF]

Figure 10 :

 10 Figure10: Illustration of Thematic Analysis[START_REF] Salomão | The Art of Interpretation: A Journey through Thematic Analysis[END_REF]