Ananya Dua

A Novel, Genetically Modified Seed Design for Increased Biofuel Production and Poverty Alleviation in Indonesia

. This early exposure also leads to detrimental side effects, with tobacco-related mortality accounting for 10% of total deaths in Indonesia (Dasar, 2018).

In Indonesia, tobacco companies are financially powerful and successful because they are the largest source of government revenue after oil, timber, and gas (Reynolds, 2013). Their integration into the government makes it easier for them to promote and encourage their agenda. Revenues from cigarette excise taxes are considered critical to the functioning of the Indonesian government [START_REF] Nichter | Reading Culture from Tobacco Advertisements in Indonesia[END_REF].Unfortunately, in Indonesia, smoking cessation programs have yet to receive the required attention from the government as a preventative strategy for reducing the popularity of the smoking culture. Apart from cigarette taxes, government support for cessation programs still falls short due to their reliance on the tobacco industry to support the Indonesian economy and the livelihoods of farmers, who deem tobacco green gold and the primary source of their income [START_REF] Bader | Effects of Tobacco Taxation and Pricing on Smoking Behavior in High-Risk Populations: A Knowledge Synthesis[END_REF].

Recently, due to the COVID-19 pandemic and conflicts with the government, tobacco farmers in Indonesia are facing increasing challenges. About 57.23 percent of tobacco farmers are now living in poverty, significantly higher than the national average of 9.66 percent in 2018 (Sahawedo, 2020). Roughly 40 percent of tobacco farmers are now reliant on governmental social protection programs (Sahawedo, 2020). Extreme weather and complicated excise taxes aggravated by the COVID-19 pandemic have made local farmers helpless and powerless. Tobacco farmers are at the bottom rung of the industry and have felt the pinch. Farmers are accusing the intermediate buyers of increasing prices and forcing them into accepting demands and are grappling with middlemen taking commission fees out of their payment. Farmers are suffering losses rather than making profits from the harvest, due to the large sum of money they have to shell out upfront for the harvesting of tobacco (Sahawedo, 2020). A separate study of 1350 smallholder tobacco farmers in Indonesia found that tobacco farming is not profitable for the farmer, that most farmers are poor, and that many suffer from green tobacco disease, which is a type of nicotine poisoning that occurs while handling tobacco plants.Workers are at especially high risk for developing this illness when their clothing becomes saturated from tobacco [START_REF] Astuti | Why Is Tobacco Control Progress in Indonesia Stalled? -A Qualitative Analysis of Interviews with Tobacco Control Experts -BMC Public Health[END_REF].

Aiming to eliminate smoking in Indonesia is a noble initiative. It would help prevent the numerous deaths each year and help the country move towards a healthier and more sustainable lifestyle. But, this motive will negatively affect the farmers and the economy. With the diminishing demand for tobacco, tobacco farmers would inevitably lose their main source of income. The government would also suffer a loss in revenue, as tobacco is one of their largest profiting industries.

Thus, I suggest an alternative use for tobacco that will allow farmers to continue supporting their lifestyles while also helping curb tobacco consumption in Indonesia -genetically engineered tobacco seeds grown for production of biofuel. The high sugar and oil contents in the genetically engineered seeds make them the optimal choice for producing biofuel; this would effectively ensure that companies would seek out these tobacco farmers to buy their plants, which would, in turn, provide them with a steady source of income to provide for their families, without having to directly sell tobacco and contribute to the nationwide crisis. Tobacco farmers who have been experiencing loss of profitability, resulting in moderate to severe repercussions to their livelihoods, would be the most open to this proposition. Other tobacco farmers who may not have experienced any personal loss due to the declining smoking industry will likely still want a safety net in the event of their sales dramatically decreasing within the next few years. On top of that, the product addresses the finite and rapidly depleting reserves of fossil fuels. In 2019 alone, 95 billion gallons of fuel were consumed by the aviation industry (E. Mazareanu, 2021). Fossil fuels are non-renewable resources; at the rate at which their reserves are being burnt, there would inevitably be a shortage shortly. Tobacco biofuel, on the other hand, is a renewable resource since tobacco plants can be continuously grown and harvested, thus ensuring that an aviation fuel shortage can be avoided.

The current climate crisis is another important issue that the product tackles. In 2019, 915 million tons of carbon dioxide were emitted from air travel, which represents around 2% of total emissions worldwide (Air Transport Action Group, 2020). Although it doesn't seem like an exorbitant percentage, the airline industry is one of the fastest-growing industries in the last 20 years, averaging a 5% annual increase in flights (Jocelyn Timperley, 2021). Air travel is primarily dependent on fossil fuels, so to get to the root of the problem, we have to find sustainable alternatives that are much less harmful to the environment, such as tobacco biofuel; it could reduce around 75% of carbon dioxide emitted from air travel (Aisling Irwin, 2016). Hence, these genetically modified seeds-that eventually produce tobacco biofuel-would contribute to efforts aiming to contain the ongoing climate crisis.

The sharp decrease in demand for air travel over the past few years due to the COVID-19 pandemic, coupled with rising jet fuel prices have sent most airline companies into a state of financial decline. The US jet fuel prices hit a 13-month high, doubling to almost $750 per metric tonne over the year, according to data by S&P Global Platts. The rising oil prices would have normally passed onto the consumers through increased ticket prices, however, due to the uncertain times of COVID-19, airlines are already running below the normal capacity. In a time when companies are providing discounts to encourage air travel, increasing ticket fares would only cripple the already struggling industry. To aggravate matters, most airline companies stopped hedging their future fuel requirements during the crisis in the oil market last year, leaving them more exposed than ever. To quantify the situation, RyanAir has lost around $300 million during the pandemic period while Delta Airlines has had only one profitable quarter this year. The airline industry is suffering largely from fuel prices and would need a substantial alternative fuel resource to survive.

The recent string of climate change policies and the rising awareness amongst people about the devastating effects of greenhouse gasses have caused a chain reaction, driving people to prefer cleaner ways of energy. To comply with the public demand for increased reliance on renewable or cleaner ways of energy, many fuel companies are expected to cut down on their fossil fuel extraction programs. This renewed climate deterioration awareness also means that more people would prefer cleaner, environmentally friendly modes of transport rather than those with pernicious impacts. This has already been since in the automobile industry as more people switch towards electric cars or hydrogen fuel cell cars over traditional petrol cars. A similar change is expected in the airline industry, with the United Nations Framework Convention on Climate Change (UNFCCC) recent Kyoto Protocol, most countries have to reduce their emissions and mitigate CO2 release. This program was renewed at the 'Durban Platform for Enhanced Action' and has seen many countries like Norway, Sweden, and Finland set out policies to significantly reduce their emissions by 2030 via a system of quota demanding SAF(sustainable aviation fuel) to be around 30%. These policies would mount greater pressure on the aviation industry as it lacks a sustainable, clean source of fuel for lengthy and high-weight travels.

Taking into account all factors: pandemic, changing attitude towards the use of fossil fuels, increased awareness about drastic environmental effects, sharply growing air travel demand, and fastly depleting reserves of fossil fuels, the aviation industry needs a sustainable, functioning, substantial, and socially acceptable fuel source and the product addresses that. Grown from a farm through sustainable farming methods (rejecting pesticides and insecticides), it has public validation. Increased sugar and oil concentrations mean it has greater energy per kJ, allowing it to sustain prolonged, heavy flights. With minimal expenditure on genetically engineering the product and stable cost of services, large fluctuations in the price of the product would not exist, resolving the high oil price chaos. Its hybrid properties of withstanding droughts and higher yield mean it can be grown almost anywhere, during any time of the year, and in magnanimous quantities, therefore there is no risk of depletion.

Besides the numerous concerns of the airline industry that the service addresses, it also heals many of the agricultural industry's ails. The service produces tobacco with minimal nicotine levels as the room is made for sugar and oil content. This allows the crop to be grown without fears of the crippling green disease, which takes 1.6 million lives each year. Lower nicotine levels also mean that the crop is less susceptible to being attacked by animals.

In a period of increasing land pressure, formerly agricultural lands make room for housing projects and commercial avenues. Fewer cultivable land means decreased yield for the farmer and smaller profits. However, the hybrid vigor of the product allows us to produce double the yield in half the land area, while requiring fewer resources. Fewer fertilizers, less water, and no pesticides are all of these features that significantly cut down the farmer's expenses, boosting profits, and making tobacco a financially viable crop again. Additionally, the product's resistance to diseases like Blue Mold and Black Root Rot means that epidemics would no longer be a threat to the farmer and they would not have to experience devastating financial losses to them.

Ultimately, many global issues need to be dealt with, and the seed design has targeted these three causes to serve humanity: providing a source of income for tobacco farmers, jet fuel shortage, and climate change.

I plan to produce seeds that are a hybrid of two genetic modification methods. One of these methods works to increase oil production and resilience by suppressing the FAD2 gene, which is responsible for reducing the overall oil content. The other depends on overexpression of DGAT-1 and LEC-2 genes that increase the production and regulation of TAG (Triacylglycerol) and the rate of photosynthesis. For this process, I will be employing varieties used in the production of Begerac's ITB-6184 strain due to its optimum sugar to nicotine ratio.Though these are two gene manipulation methods already developed by scientists, the process of hybridizing genetically-engineered seeds (as opposed to naturally optimum seeds) has not yet been done. Recent research has established that the combination of genetic engineering and hybridization leads to copious benefits, including greater plant yield, resistance to disease and drought, and a healthier overall supply, and mine will be one of the first products to implement this knowledge into the development of the seed.

In Nicotania Tobacum, the FAD-2 gene codes for the key enzyme that is responsible for the biosynthesis of polyunsaturated fats in non-photosynthetic tissues like roots and developing seeds. FAD-2 gene in the endoplasmic reticulum utilizes phospholipids as substrates with NADH, and NADH-cytochrome as electron donors. FAD-2 gene is divided into further four types: FAD2-1, FAD2-2, FAD2-3, and FAD2-4; all variations are dependent on the site of their function and the pattern of gene expression during transcription. My project focuses on the FAD2-2 variety. Expressed at a low level from vegetative to maturation state during seed development, FAD2-2 is the major gene responsible for the synthesis of linoleic acid. Coding for fatty acid desaturase enzyme, FAD2-2 is responsible for desaturating oleic acid to linoleic acid at the mesocarp and the maturing seeds, covering the greatest function area of all the four varieties. The FAD2-2 gene expression decreases as the temperatures go from 15oC to 35 oC, while its expression increases with the level of light intensity and in case of wounding or a pathogen attack.

After using genomic flanking the T-DNA was cloned by plasmid rescue to isolate cDNA and genomic clones of FAD2, a cDNA containing the entire genomic sequence of FAD2 was expressed in FAD2 mutant plants. The Gel Blot analyses of FAD2 mRNA levels showed that the gene is expressed throughout the plant and transcription levels are in excess than needed for oleate desaturation. Therefore, inhibiting the FAD-2 gene to a certain level would not harm the plant and it will maintain its useful functions through oleate desaturation like plant development and tolerance against salt and cold.

In the second base strain, the two other Arabidopsis genes, DGAT and LEC-2, will be used. DGAT gene consists of two varieties, DGAT1 and DGAT2. Both DGAT genes are responsible for coding acyl-CoA: diacylglycerol acyltransferase (DGAT) enzymes. These enzymes are involved in the biosynthesis of Triacylglycerols (TAGs). Triacylglycerols are major storage molecules of metabolic energy in living organisms. Triacylglycerols (triglycerides) (TGs), a major type of neutral lipid, are a heterogeneous group of molecules with a glycerol backbone and three FAs attached by ester bonds. TAGs serve many vital functions in living organisms like storage molecules for energy utilization and synthesis of membrane lipids. Heightened LEC-2 levels which are attained through the gene's expression catalyze seed development and enhance the levels of oil storage. Increasing the DGAT enzyme concentration would not only speed up the rate of this reaction but also increase the overall TAGs production, leading to greater oil content.

The two strains are crossed together for a given number of generations, isolating the required characteristics, similar to what is done during selective breeding of livestock. The desired characteristics in the resulting hybrid offspring are higher oil and sugar content, greater plant height, and lower nicotine levels. The hybrid vigor possessed by these offspring leads to greater plant height, plant number, overall yield, and area, and the ability to withstand tough environmental conditions and survive droughts. These seeds also have increased disease protection, especially against Blue Mold and Black Root Rot.

Currently, the global aviation fuel market is a rapidly-growing industry that is anticipated to generate $238.5 billion by 2026. With a shortage of worldwide aviation fuel, many companies are looking for alternative methods to produce this fuel, one of the most famous methods including tobacco. The optimally designed seeds are perfect for the purpose due to their increased sugar and oil levels, a significant advantage that improves the quality and selling price of aviation fuel.

In conclusion, this unique, never-before-done combination of hybridization and genetic engineering methods on the seeds ensures that this product will be successful in protecting the livelihoods of tobacco farmers while mitigating the aviation fuel shortage in the country and bolstering the economy.


Mathieu Gouhier
email: m.gouhier@opgc.fr

Franck Donnadieu
email: f.donnadieu@opgc.fr

Systematic retrieval of ejecta velocities and gas fluxes at Etna volcano using L-Band Doppler radar

Keywords: Ejecta velocities, Gas fluxes, Etna volcano, Doppler radar, Particle sizes

Strombolian-type volcanic activity is characterized by a series of gas bubbles bursting at the top of a magma column and leading to the ejection of lava clots and gas emission at the surface. The quantitative analysis of physical parameters (e.g., velocity, size, and mass fluxes) controlling the emission dynamics of these volcanic products is very important for the understanding of eruption source mechanisms, but remains difficult to obtain in a systematic fashion. Ground-based Doppler radar is found to be very a effective tool for measuring ejecta velocities at a high acquisition rate, and close to the emission source. We present here a series of measurements carried out at Mt. Etna's Southeast crater, using an L-band volcanological Doppler radar, during the July 4 2001 Strombolian eruptions. Doppler radar data are supplemented by the analysis of video snapshots recorded simultaneously. We provide here a set of physical parameters systematically retrieved from 247 Strombolian explosions spanning 15 min and occurring during the paroxysm of the eruption from 21:30 to 21:45 UT. The timeaveraged values give a maximum particle velocity of p V max = 94.7  24 m/s, a bulk lava jet velocity of rad PW V  = 37.6  1.9 m/s, and an initial gas velocity at the source vent of g V 0 = 118.4

 36 m/s. The time-averaged particle diameter is found to be about rad PW D  = 4.2  2.1 cm. The volume and mass gas fluxes are estimated from time-averaged source gas velocities over the sequence duration at g v Q =3-1110 3 m 3 /s and g m Q =0.5-210 3 kg/s respectively.

Introduction

Volcanic surface processes are, potentially, important sources of information for understanding the deeper mechanisms behind an eruption's dynamics. Gas and pyroclast velocities are particularly important parameters as they are direct indications of the physical conditions during Strombolian explosions. Velocity measurements taken during fieldwork near an erupting volcanic vent have generally been restricted to passive remote sensing methods such as photography [START_REF] Chouet | Photoballistics of Volcanic Jet Activity at Stromboli, Italy[END_REF][START_REF] Blackburn | Mechanisms and dynamics of Strombolian activity[END_REF][START_REF] Ripepe | Image processing of explosive activity at Stromboli[END_REF], video [START_REF] Sparks | Explosive volcanic eruptions -V. Observations of plume dynamics during the 1979 Soufrière eruption, St Vincent[END_REF][START_REF] Ripepe | Time constraints for modeling source dynamics of volcanic explosions at Stromboli[END_REF] or acoustic techniques [START_REF] Vergniolle | Strombolian explosions: 2. Eruption dynamics determined from acoustic measurements[END_REF]. Ground-based active remote sensors like Doppler acoustic sounders [START_REF] Weill | Acoustic sounder measurements of the vertical velocity of volcanic jets at Stromboli volcano[END_REF] or, more recently, Doppler radars have been successfully operated at Stromboli [START_REF] Hort | Volcanic eruption velocities measured with a micro radar[END_REF] and Mt. Etna volcanoes [START_REF] Dubosclard | First testing of a Volcano Doppler Radar (Voldorad) at MtEtna[END_REF][START_REF] Dubosclard | Doppler radar sounding of volcanic eruption dynamics at Mount Etna[END_REF][START_REF] Donnadieu | Remotely Monitoring Volcanic Activity with Ground-Based Doppler Radar[END_REF][START_REF] Gouhier | Numerical modeling of Doppler radar signals of Strombolian eruptions[END_REF], 2008[START_REF] Gouhier | The geometry of Strombolian explosions: insight from Doppler radar measurements[END_REF]. The advantage of Doppler radars is that they enable the interior of the jet to be studied. Moreover, they provide direct velocity measurements with a good spatial resolution and high sampling rate, which should make possible the systematic retrieval of kinetic and loading parameters which characterize volcanic products. However, Doppler radar measurements can be complex to interpret as they only measure radial (along-beam) velocities and integrate the volumetric information. Therefore we have tested here a systematic retrieval method during typical Strombolian activity at Etna's SE crater in July 2001, which provides a relatively simple framework for the analysis of Doppler radar data. This study has allowed us to derive statistical parameters for a large number of Strombolian explosions: the maximum particle velocity ( p V max ), the source gas velocity ( g V 0 ), the power-weighted particle velocity and diameter ( rad PW V  , rad PW D  ), and the volume and mass gas fluxes

( g v Q , g m Q ).

Experimental conditions

Volcanological Doppler radar (VOLDORAD) is a pulsed ground-based Doppler radar (L-band, 1,274 MHz) developed by the Observatoire de Physique du Globe de Clermont-Ferrand (France), and is designed to monitor all types of explosive volcanic activity. It operates at a wavelength of 23.5 cm and permits the monitoring of volcanic eruptions over a large range of distances (0.3 -11 km), with a high data acquisition rate (>10 Hz). The Doppler radar measurements used here were made during eruptive episodes of the SE crater on 4 July 2001, over about 4.5 h of typical Strombolian activity. The consistent and well-known activity provides a relatively simple framework within which to interpret the Doppler radar measurements and consequently to test a systematic retrieval method to investigate the dynamics of Strombolian explosions in more detail. The activity began around 18:00 UT and involved, at first, small explosions repeated every ~10 s on average. The intensity then increased progressively, culminating in powerful Strombolian explosions every 2-3 s between 21:00 UT and 22:00 UT. The eruption intensity then decreased rapidly from 22:00 UT and ended around 23:00 UT. VOLDORAD was set up at an altitude of about 3,000 m, at a horizontal distance of 930 m to the SSW of the centre of the SE crater (Fig. 1). Volcanic particles crossing the antenna beam were detected in different sampling volumes, called range gates, 120 m deep, and 150 m wide in azimuth and elevation. Echo-power data acquired from successive range gates are displayed in real time as Doppler spectra representing the power spectral density as a function of the radial velocity (Fig. 1). From the processing of Doppler spectra in each range gate, raw time series can be computed for two sets of positive (indexed +) and negative (indexed ̶ ) parameters, corresponding mainly to ascending and descending ballistics, respectively. The maximum radial velocity (  max V ) is defined by the highest velocity of the spectrum for which the power becomes equal to the background noise level. The power-weighted particle velocity ( rad PW V  ) is the average velocity weighted by the power spectral density (Fig. 1). For more details on the spectral moments calculation, see [START_REF] Dubosclard | Doppler radar sounding of volcanic eruption dynamics at Mount Etna[END_REF].

Radar and video observations

Direct video observations represent a simple approach for obtaining first order estimates of pyroclast velocities. The comparison of video measurements with radar records (measured by VOLDORAD) should provide additional constraints on ejecta velocities. We have worked on 27 explosions that occurred at Etna's SE crater during the paroxysm of the 4 July 2001 eruption between 21:30 and 21:45 UT. The majority of these explosions were characterized by vertically oriented jets with pyroclasts propelled within a relatively narrow ejection cone. Ejecta velocities inferred from video measurements were calculated from the cumulative displacement of incandescent bombs tracked on the video footage, at a sampling rate of 25 Hz. We then selected the maximum value from each explosion in order to compare with the maximum radial velocities (  max V ) given by the Doppler radar (Fig. 2a). In this calculation, we assume that vertical blocks selected travelled parallel to the image plane, which may induce an error that underestimates the real block velocities. Also, the mean size of particles selected on video snapshots is probably larger than that of particles recorded by VOLDORAD from which  max V is calculated. Errors on the pixel scale and on the method used to carry out these measurements are also taken into account, leading overall to an uncertainty of 20% on video-derived velocities. The uncertainty of radial velocities recorded by the radar is smaller (12%), and mostly due to the aliasing of the Doppler frequency. The results obtained show that  max V is generally much lower than video-derived maximum velocities, with average values of 89.9  10.5 m/s and 150.2  29.8 m/s respectively. The reason is that velocities recorded by the radar are measured along the antenna beam axis, and  max V hence corresponds to the maximum radial component of particle velocities. Two end-member cases exist: (1) particles are ejected parallel to the beam axis, so the radial velocity component equals the actual particle velocity; (2) particles are ejected perpendicular to the beam axis, when the radial velocity component equals zero. All intermediate cases can occur between these end-members, with the radial component of velocity always lower than the actual particle velocities.

Retrieval of kinetic data

Maximum particle velocity: p V max

In order to obtain reliable estimates of maximum particle velocity ( p V max ) from VOLDORAD measurements, a geometrical correction must be applied to  max V , taking into account both the radar sounding and the ejecta dispersion geometry. As the sounding geometry is fairly well constrained, most uncertainty arises from the ejecta dispersion (i.e., particle velocities and trajectories). This is a difficult issue, as the maximum radial velocity (  max V ) recorded by VOLDORAD reflects both the velocity and the trajectory of the particles. Therefore the ratio, hereby named "geometric coefficient" ( geom C

), between the maximum particle velocity ( vid V )

derived from video measurements and  max V must be related to the particle's trajectory in three dimensions. We obtain geom C = 1.67  0.38 (Eqs. 1a and 1b), which represents an average ejection angle of  = 72° with an error of -14° and +7°, calculated from:

           ) 2 ( cos 1 max    V V C vid geom (1a)     2 1 2 2 max 2 max 2 max 1                                  vid vid geom V V V V V C (1b)
C C V V V         (2a)           2 1 2 2 max 2 max 2 max geom geom p C V V C V        (2b)
In an attempt to characterize the ejecta velocity distribution and its control on radar velocity measurements, a two-dimensional (2-D) kinetic analysis was carried out from video measurements of a typical lava jet (Fig. 2b). To minimize the bias of measuring only the component of velocity in the 2-D image plane, we selected the fastest of a large number of blocks in various directions of each image within ±70° from the upright axis and their velocity was determined from block tracking on scaled images. The uncertainty on video-derived velocities is about 20%, i.e., larger than the underestimation resulting from the inclination of the image plane (statistically 8% for a vertical jet), and thus conservative. Despite these possible biases, Fig. 2b shows that: (1) particle velocities vary significantly over a wide range of ejection angles; (2) particles ejected vertically have the highest velocities, up to 110 m/s in this example, with velocities decreasing sideways quite symmetrically away from the vertical axis, with the envelope of the (highest) velocity values approximating a Gaussian distribution; (3) importantly, the equivalent radial (along-beam) velocities calculated for each block (Eq. 1a), hence comparable to the radial velocities recorded by the radar, show a maximum (43 m/s) for particles ejected at about 70°. This latter value is in agreement with the mean ejection angle ( = 72°) deduced from the average video/radar velocity ratio of the 27 Strombolian explosions studied (Fig. 2a). A vertical velocity maximum is in line with gas velocities exiting a vertical conduit. The distribution of particle velocities might be influenced by the geometry and orientation of the conduit, and by the near-surface dynamics of the slug responsible for the acceleration and shape of the liquid magma overlying the gas slug. [START_REF] James | Modelling the rapid near-surface expansion of gas slugs in low viscosity magmas[END_REF] have shown in particular that the near-surface dynamics may increase slug-nose velocities, resulting in significant pre-burst magma-surface velocities and particle acceleration. The shape of the ejecta velocity distribution may also vary as a function of time during gas release; the emptying of elongated gas slugs, for instance, is expected to narrow the velocity distribution shape, as gas expands from progressively deeper in the conduit, resulting in vertically oriented lava jets sustained by the gas flux. Different particle velocity distributions have also been quantified from Doppler radar records of hemispherical lava bubble outbursts and lava jets, possibly reflecting differences in shape and pressurization of the gas slugs [START_REF] Gouhier | The geometry of Strombolian explosions: insight from Doppler radar measurements[END_REF]. Initial gas velocity: g V 0 We show in this section that the initial gas velocity at the source vent can be inferred from Doppler radar measurements using a coupled gas-particle kinetic model. Following [START_REF] Dubosclard | Doppler radar sounding of volcanic eruption dynamics at Mount Etna[END_REF], the position of particles dragged along by the gas phase can be calculated at each time-step on the basis of the equations of motion, assuming that the following two forces are acting on a moving particle: drag force (with a direction opposite to the velocity vector), and gravitational force. The velocity of the particles can then be calculated by numerical integration using a fourth order Runge-Kutta method. The initial velocity of a particle with a diameter, D, is as follows [START_REF] Chouet | Photoballistics of Volcanic Jet Activity at Stromboli, Italy[END_REF]:

  D k V D V g p   0 0 (3)
where g V 0 is the gas velocity at the vent and k=150 stands for

g m Cd g   3 / 4
, with g being the gravity, m and g the ejecta and gas density, and Cd the drag coefficient [START_REF] Chouet | Photoballistics of Volcanic Jet Activity at Stromboli, Italy[END_REF][START_REF] Ripepe | Image processing of explosive activity at Stromboli[END_REF]). The gas velocity decreases exponentially with distance from the source vent, z, as follows:

    z V z V g g    exp 0 (4)
The gas velocity decay rate is a major unknown, challenging to measure accurately in the field.

In Eq. 4, we use a decay value of =0.013, inferred from FLIR camera measurements carried out at Stromboli on 39 explosions by [START_REF] Patrick | Strombolian explosive styles and source conditions: insights from thermal (FLIR) video[END_REF]. Synthetic particle velocities were calculated at range gate G3 for a wide range of diameters (100 µm -20 cm) and initial gas velocities (100 -250 m/s) (Fig. 3a). Because of the decoupling between gas and particles due to inertia, the particles with the maximum velocity as they enter range gate G3 are found to be centimetre-sized lapilli (1-3 cm). From this, the average ratio R=1.250.13 can be defined between the initial gas velocity and the maximum particle velocity (Fig. 3b), allowing us to derive first order estimates of initial gas velocities ( g V 0 ) at the source vent with related uncertainties (

g V 0  ) of 30% (Eq. 5a, b). ) ( ) ( max max 0 R R V V V p p g       (5a)       2 1 2 2 max 2 max 2 0 R V V R V p p g      (5b)
Fig. 3 Particle velocity calculated at the bottom of range gate G3 (75m up the vent) using the ballistic model, for a wide range of particle sizes and various source gas velocities. It shows that maximum velocities are calculated for 1 to 3-cm diameter particles with a velocity ratio ranging from 1.1 to 1.4.

Power-Weighted particle velocity:

rad PW V  The power-weighted particle velocity is directly computed from measured Doppler spectra as the product of each class of radial velocity by its associated echo power, integrated over the positive range of measured velocities [START_REF] Dubosclard | Doppler radar sounding of volcanic eruption dynamics at Mount Etna[END_REF]). This raw parameter integrates the along-beam velocity components of ascending particles in the probed volume weighted by the power they backscatter as a function of their size. To characterize the kinetics of ascent for the lava jet as a whole, we use the power-weighted velocity of ascending particles only ( rad PW V  ). Then, lava jets being mainly vertically oriented, we performed a vertical correction using Eq. 1a, with =90° (vertical ejection) and -/2=23° (beam angle at the range gate centre).

Power-Weighted particle diameter:

rad PW D  Using the gas-particle kinetic model presented previously (Eqs. 3, 4;[START_REF] Dubosclard | Doppler radar sounding of volcanic eruption dynamics at Mount Etna[END_REF]) as a forward model, we derive the power-weighted diameter of ejected pyroclasts, i.e. the radar mean size of particles. At the input of the inversion procedure, we need: (1) the initial gas velocity at the vent ( g V 0 ), as determined previously (Eqs. 5a and 5b), which varies from one explosion to another, and controls the velocity of ejected pyroclasts; (2) the mean ejection angle of particles, which can be reasonably taken as being vertical (=90°) [START_REF] Chouet | Photoballistics of Volcanic Jet Activity at Stromboli, Italy[END_REF][START_REF] Gouhier | The geometry of Strombolian explosions: insight from Doppler radar measurements[END_REF]; (3) the distance from the source vent to the centre of range gate G3, which is found to be about z=150 m. The power-weighted diameter ( rad PW D  ) is found by iteration, and corresponds to when the synthetic power-weighted velocity matches the radarinferred power-weighted velocity. The error on rad PW D  (50%) is mainly controlled by the initial gas velocity ( g V 0 ), the gas decrease coefficient ().

Analysis of the Strombolian explosion series

The thorough study of discrete events is obviously fundamental to the understanding of shallow physical processes such as bubble bursting, magma drainage or gas release mechanisms. However, the analysis of volcanic processes integrated over longer periods of time is also essential in order to understand eruptive mechanisms occurring over larger time and space scales, and continuous radar measurements prove to be a powerful tool in this respect. In this section we systematically retrieve the previously described eruptive parameters ( p V max , g V 0 , rad PW V  , and rad PW D  ) from a sequence of 247 Strombolian explosions, occurring during the paroxysm of the 4 July 2001 eruption at Etna's SE crater, from 21:30 to 21:45 UT (Fig. 4). The maximum particle velocities ( p V max ) are calculated using Eqs. 2a and 2b, and average 94.7  24 m/s over the whole sequence, with maximum values of up to 200  50 m/s and minimum values of less than 50  12.5 m/s. The average initial gas velocity at the vent ( g V 0 ) is found to be about 118.4  36 m/s. The bulk jet velocity ( rad PW V  ), which integrates the power-weighted velocities of all ascending particles of various sizes in the sampling volume is about 37.6  1.9 m/s. Iterative runs carried out from the kinetic model for each event give a mean rad PW D  of 0.042  0.021 m.

Gas flux retrieval:

g v Q , g m Q
The flux and total volume of gas released during this sequence can also be deduced from the time series of source gas velocities. However, the gas velocities given in Fig. 4 correspond to the peak values at each explosion onset and cannot be used alone to calculate the average gas flux. Instead, we average the source gas velocities computed from all radar Doppler spectra (2,478 velocity data points acquired at 2.5 Hz) over the entire 15-minute sequence. The gas flux (Eqs. 6a and 6b) can be expressed as being the product of the vent sectional area (assumed circular with radius of 5  2.5 m; P. Allard pers. comm.) multiplied by the average source gas velocity (70.2  21 m/s).

2 0 0 ) ( ) ( r r V V Q g g g v        (6a)           2 1 2 2 0 2 0 2 2 2 r r V V r Q g g g v        (6b)
The mean gas flux during this sequence of paroxysmal Strombolian activity is found to be g v Q = 3-1110 3 m 3 /s. Considering that H2O commonly represents more than 90% of the gas released during Strombolian eruptions [START_REF] Allard | Spectroscopic evidence for a lava fountain driven by previously accumulated magmatic gas[END_REF], we assume that H20 is the only magmatic gas species. The water vapour density is a major unknown as it depends on the pressure and temperature of the gas when released. At 3300 m elevation with a temperature ranging from 25° to 400°C we obtain a density ( vap O H 2  ) of 0.2-0.4 kg/m 3 . Using Eqs. 7a and 7b, the gas mass flux is found to be g

m Q = 0.5-210 3 kg/s. ) ( ) ( 2 2 g v g v vap O H vap O H g m Q Q Q         (7a)           2 1 2 2 2 2 2 2 g v vap O H vap O H g v g m Q Q Q        (7b) Fig. 4 Eruptive parameters ( p V max , g V 0 , rad PW V  ,

Discussion and conclusions

The thorough analysis of Strombolian explosions measured by ground-based Doppler radar at Etna's SE Crater on 4 July 2001, has enabled us to develop a method to estimate in a systematic way a range of source parameters related to the kinetics and loading of volcanic clasts and gas. The opportunity to obtain reliable estimates with a high acquisition rate and in a systematic way is of obvious interest for improving our understanding of eruption dynamics. Our values fall within the upper range of values given in the literature for typical Strombolian activity. Apart from major eruptions, common values for ballistic ejecta velocities at Stromboli volcano were found to range between 10 and 40 m/s [START_REF] Chouet | Photoballistics of Volcanic Jet Activity at Stromboli, Italy[END_REF][START_REF] Ripepe | Image processing of explosive activity at Stromboli[END_REF][START_REF] Blackburn | Mechanisms and dynamics of Strombolian activity[END_REF]), and gas velocity was estimated at about 70 to 110 m/s [START_REF] Chouet | Photoballistics of Volcanic Jet Activity at Stromboli, Italy[END_REF][START_REF] Blackburn | Mechanisms and dynamics of Strombolian activity[END_REF][START_REF] Steinberg | Experimental velocity and density determination of volcanic gases during eruption[END_REF]. [START_REF] Weill | Acoustic sounder measurements of the vertical velocity of volcanic jets at Stromboli volcano[END_REF] found vertical jet velocities close to the conduit exit of between 20 and 80 m/s for more than 100 explosions at Stromboli using an acoustic Doppler sounder. More recently, [START_REF] Hort | Radar Doppler velocimetry of volcanic eruptions: theoretical considerations and quantitative documentation of changes in eruptive behavior at Stromboli volcano, Italy[END_REF] found initial ballistic ejecta velocities ranging from 44 to 70 m/s by means of Doppler radar measurements. Also [START_REF] Patrick | Strombolian explosive styles and source conditions: insights from thermal (FLIR) video[END_REF] gave a mean gas velocity, using a Forward Looking Infrared Radiometer (FLIR), of about 30 m/s at the crater rim with maximum velocities reaching 100 m/s. Values obtained at Etna are usually higher, with a mean ballistic ejecta velocity of about 50 m/s [START_REF] Mcgetchin | Cinder cone growth modeled after Northeast Crater, Mount Etna, Sicily[END_REF]) and a mean gas velocity of about 100 m/s [START_REF] Steinberg | Experimental velocity and density determination of volcanic gases during eruption[END_REF]. During the paroxysmal eruption of 5 April 2003 at Stromboli, ballistic ejecta reached 170 m/s, with a gas velocity of about 310 m/s (Rosi et al. 2006). Estimates of particle settling velocities inferred from Doppler radar measurements at Stromboli show that the mean particle sizes range from 0.004-0.008m [START_REF] Hort | Radar Doppler velocimetry of volcanic eruptions: theoretical considerations and quantitative documentation of changes in eruptive behavior at Stromboli volcano, Italy[END_REF], i.e. an order of size smaller than our estimates for Etna (average diameter, 0.042 m). In addition, we show that first-order gas flux estimates could be retrieved from VOLDORAD measurements. Our estimates are in good agreement with acoustic measurements carried out on the same eruption. [START_REF] Vergniolle | From Strombolian explosions to fire fountains at Etna Volcano (Italy): what do we learn from acoustic measurements?[END_REF] give mean gas volumes of 4.210 3 and 1110 3 m 3 per explosion from acoustic and lava fountain heights, respectively. We obtained independently a mean value of 15.710 3 m 3 per explosion. The small difference may be related to the fact that our measurements were carried out during the paroxysm only, whereas the mean value derived from acoustic data are calculated over the whole episode (i.e., with lower activity phases). Also, weakly pressurized bubble outburst may remain undetected by acoustic sensors, thus leading to an underestimation of the total volume of gas released while releasing hot gas responsible for the ascent of fine ash particles detected by the radar. The quantification of gas volumes and fluxes from radar velocity measurements and field observations compares favourably with other methods and turns out to be a valuable method to improve the characterization of eruption dynamics.

Fig. 1

 1 Fig. 1 Sketch of the radar sounding geometry used for the acquisition campaign on Etna's SE Crater, on 4 July 2001. The insert map shows the summit craters and the location of VOLDORAD. Doppler spectrum recorded by VOLDORAD in the range gate G3 (1,047m) at Etna's SE crater, at the onset of the Strombolian explosion at 21:42:00 UT on 4 July 2001, showing measured maximum (  max V ) and power-weighted particle velocity ( rad PW V  ).

Fig. 2 a

 2 Fig. 2 a Comparison between maximum video-derived velocities of ballistic pyroclasts and maximum radial velocities (  max V ) measured by Doppler radar during the paroxysm of the 4 July 2001 eruption at Etna's SEC. Uncertainties on video and radar measurements are estimated at 20% and 12% respectively. b Distribution of particle velocities derived from video measurements of 15 blocks as a function of their ejection angle  during a Strombolian lava jet at 21:42:00 on 4 July 2001. Asterisks, equivalent radial velocities (along-beam) as they would be recorded by the radar are indicated above each bar.

  Gouhier M. and Donnadieu F., 2011. Systematic retrieval of ejecta velocities and gas fluxes at Etna volcano using L-Band Doppler radar.Bull. Volcanol. 73: 1139-1145. https://doi.org/10.1007/s00445-011-0500-1 where  is the particle ejection angle,  and  are the antenna beam elevation angle (23°) and aperture angle (9°), respectively.

								C 	geom	is the error calculated from the uncertainties of video
	(20%) and radar (12%) velocities. Then,	C	can be used to retrieve systematically the
										geom
	maximum particle velocity parameter ( p V max ; Eq. 2a), with an uncertainty (	p V max 	; Eq. 2b) of
	25%.
	max p	(	max	max	)	(	geom	geom	)

Citation:

Acknowledgments This work was supported by a French MESR fellowship. We gratefully acknowledge Georges Dubosclard, Roland Cordesses and Claude Hervier for the precious radar measurements. Field work on Etna was possible thanks to the logistical help of INGV Catania and was funded by the CNRS-INSU PNRN programs 1999-2001. We also thank Patrick Allard, who also provided additional video data and insightful comments. Fran van Wyk de Vries kindly improved the English. We thank Matt Patrick and Mike James for their constructive comments and Maurizio Ripepe for handling the manuscript.


Edouard Augustin Ribes
email: edouard-augustin.ribes@mines-paristech.fr

Using classification techniques to accelerate client discovery: a case study for wealth management services

Keywords: Wealth Management, Brokerage, Machine learning, Classification;, Technological Change

Background context. The retail side of the finance industry is currently undergoing a deep transformation associated to the rise of automation technologies. Wealth management services, which are traditionally associated to the retail distribution of financial investments products, are no stranger to this phenomena.

Specific knowledge gap the work aims to fill . The retail distribution of financial instruments is currently normalized for regulatory purposes but yet remains costly. Documented examples of the use of automation technologies to improve its performance (outside of the classical example of robo-advisors) remain sparse.

Methods used in the study. This work shows how machine learning techniques under the form of classification algorithms can be of use to automate some activities (i.e. client expectations analysis) associated to one of the core steps behind the distribution of financial products, namely client discovery.

Key findings.

Once calibrated to a proprietary data-set owned by one of the leading french productivity tools providers specialized on the wealth management segment, standard classification algorithms (such as random forests or support vector machines) are able to accurately predict the majority of households financial expectations (ROC either above 80% or 90%) when fed with standard wealth information available in most of the database of financial products distributors.

Implications. This study thus shows that classifications tools could be easily embedded in digital journey of distributors to improve the access to financial expertise and accelerate the sales of financial products.

Introduction:

Wealth management revolves around the distribution of appropriate financial products to meet individual households objectives [START_REF] Tilmes | Private banking und private wealth managementdefinitionen und abgrenzungen aus wissenschaftlicher sicht[END_REF][START_REF] Maude | Global private banking and wealth management: the new realities[END_REF][START_REF] Collardi | Private banking: Building a culture of excellence[END_REF]. Those objectives generally revolve around the transmission of wealth across generations, retirement preparation and wealth protection (notably in entrepreneurial contexts). Current wealth management practices currently falls in one of two categories [START_REF] Brunel | Goal-based wealth management in practice[END_REF]. On one hand, a financial advisor can "push" products to customers and attempt to directly sell them a financial instrument. On the other, an advisor can invest time in "pulling" information out of a household representative (referred to as "the client" in the rest of this study) to generate a legal, fiscal and investment(s) advice which generally leads to the sale(s) of financial instrument(s). Despite being more effective when it comes to meeting households' need, this second method is, as of today, time consuming and heavily reliant on human interactions [START_REF] Maude | Global private banking and wealth management: the new realities[END_REF]. If information on household's wealth can be collected rapidly2 [START_REF] Philippon | The fintech opportunity[END_REF][START_REF] Arner | Fintech, regtech, and the reconceptualization of financial regulation[END_REF], the capture of households' objectives still rely on individual discussions between a client and his/her advisor. Besides the generation of a legal, fiscal and investment counsel generally requires several hours of manual preparation by a financial advisor. Given the associated time commitment (i.e. ≈ 1 day) and the usual daily rate of a financial advisor in northern geography (i.e. ≈ 1000$ per day), a proper financial advice comes at least at a cost of 1000$ per household. Advice is, in practice, however not billed to the client as the traditional business model of wealth management consists in being compensated through a commission representing a percentage of the overall asset under management by the advisor. Given that commissions are in the 2% range annually [START_REF] Bazot | Financial consumption and the cost of finance: Measuring financial efficiency in europe (1950-2007)[END_REF][START_REF] Philippon | Harnessing the promise of fintech[END_REF], advisors have to at least collect investments of about 100k$ per household to recoup their own operating costs. This, of course, limits drastically the portion of the population which can benefit from financial counselling. As of today, beneficiaries of such services are indeed the top 5% to 1% (income wise) of households residing in mature geographies 3 . This situation calls for action. On one hand, current demographic trends (notably population ageing) requires households to prepare for retirement on their own as public pensions systems are being reformed and public allowances reduced [START_REF] Organization | Active ageing: A policy framework[END_REF]. This is a well known topic, which is continuously monitored at state levels by means of instruments such as the Survey of Health, Ageing and Retirement in Europe [S.H.A.R.E] (Börsch- [START_REF] Börsch-Supan | A new comprehensive and international view on ageing: introducing the 'survey of health, ageing and retirement in europe[END_REF]. On the other, technological change induces a transformation of the labor structure of mature markets with a rising share of the population being self employed/ contracted (about 15% on average in europe and growing) [START_REF] Hatfield | Self-employment in europe[END_REF]. Those two trends requires more and more individuals to rely on financial advice to acquire the right financial instruments and generally calls for a commoditization of the counsel which is currently only provided today (because too expensive) to portions of the wealthiest part of the population (see [START_REF] Cocca | Potential and limitations of virtual advice in wealth management[END_REF] for a discussion on the potential opportunities and pitfalls of such a transformation).

To address this need, our firm, Manymore (a french financial technology provider4 ) has developed a three steps fully automated system. First, we put at the disposal of households a digital form which enables them to fill in the information pertaining to their wealth (e.g. revenues, expenses, list of assets and debts etc...). Second, a set of classification tools analyse their wealth profile and mimics the discussion the discussion they could have with an advisor to identify their objectives. Finally, wealth information and objectives are compiled by an expert sub-system to generate a written report detailing the best options available to a household in terms of legal, fiscal and investments actions. Links are then provided to specific platforms (e.g. subscription ones) to enable household to act on the recommendations provided by the system. The solution offered by Manymore therefore contributes to preservation of individuals' financial well-being in a context of change and also partakes to the overall transformation of the financial services industry highlighted in a variety of recent literature reviews [START_REF] Vives | The impact of fintech on banking[END_REF][START_REF] Navaretti | Fintech and banking. friends or foes? Friends or Foes[END_REF]EBE, 2017).

In terms of scope, this article will focus on description the classification methods currently used by Manymore to automate client objectives discovery. This will be done in three steps. Fist, a description of the proprietary data available to calibrate the previously mentioned classifiers will be performed. This will go hand in hand with a short methodological review of the classification techniques which were used and compared to shape the final solution. Second, results of the algorithms calibration to the available data will be reviewed. Finally, the limitations of study as well as potential opportunities for future research will be discussed.

Methodology:

Defining households financial objectives:

Households' objectives are normally co-defined with their financial advisors. In order to automate this discovery process, an objectives' referential was required. A panel of independent financial advisors was therefore gathered in 2019 to structure such a standard framework. This resulted in T = 8 main objectives labelled {O1; ...; O8} which could be further broken down in a set of J = 36 goals labelled {O1a; ...; O8c}. Details are displayed in table (4) located in the appendix. The framework was then embedded in Manymore's core solutions. Between 2019 and 2022 it was leveraged by a subset of 509 of its users on a total of N = 993 households. The framework was used to consistently capture households objectives during conversations with their advisor before being fed to Manymore's expert sub-system to generate an automated counsel. This resulted in a data-set where each household n ∈ {1; ....; N} had a rating Y {n, j} ∈ {0; 1} recording its interest according to goal j (Y {n, j} = 1 [resp. 0] indicating that goal j did [resp. not] resonate with household n).

Within the available dataset, households had on average 3.2 (+/-2.1) [resp. 7.9 (+/-6.7)] objectives [resp. goals] each. Besides, the data reported in table (4) shows that the classification system proposed by the panel of experts was relevant as all objectives and goals matched the interest of at least ≈ 10% of households. Results also show that from an objective standpoint, households primary interest was on the topic of wealth transmission [O3] and their expectations first revolved around the optimization of the inheritance rights [O3e] and their real estate wealth tax [O3f]. Conversely their last concern was the optimization of their professional assets [O6]. Finally, the financial objectives recorded for households displayed a certain level of correlation with each other (see table ( 5)). Objectives {O1;O2;O4;O5} exhibited a 40%+ level of correlation, which means that questions of capital accumulation appeared mainly in relation to retirement preparation. All other objectives exhibited a lower level of correlations with each other (i.e. <30%). Similar level of correlations were observed on goals.

The results displayed in table (4) show that households' objectives are mainly linked to a primary goal (e.g. {O1;O1c};{O2;O2a}...) and that sometimes, they express additional needs by specifying secondary goals. As a result, the research undertaken in the context of this article focused on finding means to automatically predict the interest of households for a given objectives (Ox) given the information they provide. Note that, in the future, further refinements could occur to make the system predictive with respect to individuals goals (Oxi).

Construction of the data-set used for prediction purposes:

The features used to predict households interest in a given objectives (Ox) were constructed through a two steps process. First, domain knowledge was extracted from the existing body of literature to pre-select potential features which may explain households' appetite for certain themes. This initial review was then combined with an assessment of the data points available in Manymore's research databases (which are anonymized) to ensure the feasibility of the analysis. This resulted in the collection of three types of data points, which profiles are detailed in this sub-section.

First, demographic information on households' structure was collected. This type of information is indeed known to be correlated to households financial behavior [START_REF] Baker | How financial literacy and demographic variables relate to behavioral biases[END_REF]. Amongst available data-points, the age of the client (a c i ) who requested financial advice for the household i (65 years on average [+/-13]), his/her marital status (m i ) (54% of the households were married), the age of his/her partner (if any) (a p i ) (53 years on average [+/-12]) and the number of children in the household (child i ) (1.7 on average [+/-1.6]) were then collected.

Second, aggregated financial information was collected as income and wealth are known to be determinants of households financial behavior and objectives [START_REF] Perry | Who is in control? the role of self-perception, knowledge, and income in explaining consumer financial behavior[END_REF]. Looking at available data-points; revenues (r i ) [113k$/year on average [+/-132k$/year]] and expenses (e i ) [39k$/year on average [+/-62k$/year]] data for each household i were thus collected from their K.Y.C file (Know your client). Revenue information was also decomposed depending in the individual generating the stream and the decomposition was added to the training data set. Revenues generated by the client (r c i ) were worth on average 81k$/year [+/-121k$/year], whilst revenues generated by clients' partner (r p i ) were worth on average 43k$/year [+/-41k$/year]. The difference between the client revenues and his/her partner (∆ i r = r c ir p i ) was also added to mix and displayed an average of 28k$/year [+/-93k$/year]. Two additional aggregated data-points were also collected in terms of the number of assets (whether physical [e.g. real estate] or financial [e.g. shares]) of the household i (A i ) [7.2 on average [+/-8.1]] as well as the total worth of those assets

(W i ) [1.31M$ on average [+/-2.7M$]].
Third, detailed financial information was collected. Households assets and revenue were indeed decomposed according to to the standard categories in place within Manymore's data collection systems. The underlying idea was that wealth and revenues profiles could share some extra light on individual households behavior [START_REF] Gomes | Household finance[END_REF]. Revenues for a household i were analysed according to five categories, namely professional revenues (r pro i ), movables income (r mov i ), real estate income (r re i ), pensions (r pensions i ) and other revenues (r others i ). Details on this front are presented in table (1) and show that, in the current data-set, most of household revenues came from professional revenues. Similarly assets were decomposed into eight categories aligned with the taxa- tion structure in place in the country on which the data-set was built (i.e. France). Wealth was therefore assessed according to: the level of assets in real estate (A real i ), the level of movable assets (A mov i ), the level of disponibilities (A dispo i

), the level of savings (A savings i ), the wealth in private pension plans (A pension i ), the wealth in life insurance plans A li f e i , the wealth associated to professional assets (A pro i ) and the remaining wealth (A other i

). Details are displayed in table (2) and the associated statistics show that households' wealth is mainly organised around real estate assets. In summary, the data-set used in this study was composed of about 30 input variables (divided according to three main themes). Observations for those variables were available for 900+ households located in France.

Classification algorithms & calibration techniques:

Feature selection techniques. Given the amount of variables that can be extracted or generated (through combination) from the current data-set, feature selection appeared mandatory to avoid potential over-fitting problems and achieve the best prediction/classification performance possible [START_REF] Jović | A review of feature selection methods with applications[END_REF]. Looking back at the literature [START_REF] Chandrashekar | A survey on feature selection methods[END_REF], this type of step is nowadays performed via two main methods: a correlation test [START_REF] Guyon | An introduction to variable and feature selection[END_REF] or a mutual information test [START_REF] Battiti | Using mutual information for selecting features in supervised neural net learning[END_REF]. In this case, both methods were tested and the mutual information yielded the best performance.

Calibration with an imbalanced dataset. Class imbalance is a common theme in machine learning [START_REF] Chawla | Special issue on learning from imbalanced data sets[END_REF] which is known to lead sub-optimal performances when calibrating classification algorithms. Looking back at the statistics displayed in table (4), the current data-set and thus study is no stranger to this phenomena. This is usually tackled through one of three core techniques [START_REF] Provost | Machine learning from imbalanced data sets 101[END_REF]. A first option consists in down-sampling the training data-set (i.e. re-sampling the majority class to make its frequency closer to the rarest class.). Another option consists in up-sampling the training data (i.e. replicating casing from the minority). The third option "ROSE" [START_REF] Menardi | Training and assessing classification rules with imbalanced data[END_REF] consists in the generation of artificial data points.In order to find the most suitable alternative, all three methods were tested and the one yielding the best results was kept. Note that additional alternatives exist (e.g. assigning weights to each class [START_REF] Chen | Using random forest to learn imbalanced data[END_REF] or combining up-and down-sampling [START_REF] Chawla | Smote: synthetic minority over-sampling technique[END_REF]) but were not tested in this study.

Selected algorithms. As highlighted in the recent review of [START_REF] Zhang | An up-to-date comparison of state-of-the-art classification algorithms[END_REF], machine learning techniques dedicated to classification problems are numerous. Yet, two main ones appear to dominate the field, namely random forests [START_REF] Breiman | Random forests[END_REF] [referred to as RF in this rest of this paper] and support vector machines [referred to as SVM in the rest of this paper] ( [START_REF] Cortes | Support-vector networks[END_REF]. Given their prominence, those two algorithms were pre-selected for the purpose of this study. Note that given the non linearity of the classification problem (which was assessed through a Principal Component Analysis on the training data set), the chosen implementation of the SVM techniques relied here on radial/exponential kernels. In addition to RF SVM algorithms, two additional contender techniques were selected amongst the ones known to yield the best performance on real world problems [START_REF] Zhang | An up-to-date comparison of state-of-the-art classification algorithms[END_REF], namely neural networks [START_REF] Abiodun | State-of-the-art in artificial neural network applications: A survey[END_REF] [referred to as NNET in the rest of this paper] and conditional inference trees [START_REF] Hothorn | ctree: Conditional inference trees. The comprehensive R archive network[END_REF] [referred to as CTREE in the rest of this paper].

Results:

Calibration pipeline:

To train and calibrate the selected models, the methodology displayed in figure (1) was used.

To feed the algorithms, the data was first pre-processed. This pre-processing activity consisted in two tasks. Input variables X i (where i refers to the i-th input) where subject to either a log normalization f (.

) (s.t. f (X i ) = log( X i +1 E(X i )+1
) if they the variable was supported in R + or left untouched if it represented a dummy variable ( X i ∈ {0; 1}). Log normalization was, for instance, applied to variables representing the revenue of households or the worth of the real estate assets. Dummy variables representing, for example, the household marital status were left as is.

Once pre-processed, a series of activities occurred to calibrate the selected algorithms for each objective t ({O1; .; Ot; .; O8}). First, the data was divided into a testing and training set, with a {50|50} split. Then the mutual information of each input variable against the output variable (representing the household interest in objective t) (Y (Ot)) was calculated (MI(X i ,Y (Ot))). Input variables were then sorted according to their mutual information score and only the ones in the top 25% percentile were kept and fed to the pipeline. Once filtered, the training data-set was then re-balanced to avoid over-fitting according to a down sampling technique. As seen in table (3), most objectives were either usually important for clients (i.e. O3 on the topic of trans-generational wealth transfers) or rather rare (i.e. O6 on the protection of professional assets).

Once filtered and balanced, the training data-set associated to each objective was fed to the proposed algorithms. Calibration was ensured with a 5 fold cross-validation and its optimization was performed according to the R.O.C criteria. The performance of each algorithm was then assessed (in terms of average ROC and the associated standard deviation observed during the cross validations). An additional step was added to the pipeline in terms of creating a greedy (linear) ensemble model based out of the 4 selected algorithms. Results did however show that an ensemble model did not bring additional predictive power. Note that for each objective several re-sampling techniques were tested and compared (see 2.3). The proposed pipeline was implemented in R and leveraged the Caret Library [START_REF] Kuhn | Building predictive models in r using the caret package[END_REF]. Results are detailed in the following sub-section.

Algorithms performance:

As seen in table (3), random forests did outperform the other methods. Besides the current data proved sufficient to yield good (i.e. ROC ∈ [80%; 90%] ) or even very good (i.e. ROC > 90%+) levels of performance according to standard benchmarks [START_REF] Carter | Roc-ing along: Evaluation and interpretation of receiver operating characteristic curves[END_REF]. Good levels of performance were registered when it comes to predicting the interest of clients for the core wealth management topics of capital accumulation [O1.] (ROC of 85%), tax optimization [O2.] (ROC of 81%), retirement preparation [O5.] (ROC of 85%), assets transfer preparation [O3.] ( ROC of 81.6%). Superior levels of performance (i.e. ROC above 90%) were achieved on the topics of succession [O3.] (ROC of 96%), additional revenue generation [O4.] (ROC of 94%) and on the interest of households for miscellaneous projects [O8.] (ROC of 96%). However, clients interests with respect to relatives protection [O7.] proved more complex to predict as algorithms only displayed here an average ROC of about 70%.

In terms of details, household's interest in capital accumulation [O1.] appears to result from a mixture of a 10+ parameters: household's total revenue (100% importance), client's partner age (92% importance), client's age (85% importance), proportion of liquidity in the household wealth (85% importance), expenses (84% importance), clients revenue (82% importance) etc... No clear cut interpretation pattern perspired, which makes senses as interest in capital accumulation is highly correlated to a host a diverse other topics such as tax optimization [O2.], retirement preparation [O4.] etc.. (see table ( 5)).

The interest of households towards tax optimization [O2.] appears to result from the combination of 8 variables which yield an importance above 75%. The total revenue of the household and its decomposition across the members of the household generated (normalized) importance scores of 100% importance (for the total revenue of the household) and 80% [resp. 57%.](for the revenue of the client [resp. his/her partner]). Households member age proved also important: the age of the client (resp. his/her partner) yielded an importance score of 82% (resp. 80%). Finally, some indications of the household's wealth decomposition were also of use to assess its appetite on the topic. First, the total amount of assets owned by the household had an importance of 76%. Second the proportion of assets of financial nature (76% importance) and the amount of money stored on life insurances (72% importance) proved useful to predict their interest on tax related topics. This appears in line with the idea that older individuals with higher level of revenues (two variables which highly correlates [START_REF] Lazear | Retirement from the labor force[END_REF])) attempt to benefit from tax incentives (i.e. rebates) offered by states to prepare for retirement and the transfer of their assets towards the next generation (through life insurance for instance in the case of France).

To predict households interest towards succession related topics [O3.] the current pipeline initially identified 7 features, 6 of which ended up being used to reach a 80% ROC. The most important features pertained to the level of assets of the household (importance = 100%), followed by the age of the client (65% importance), the amount of capital associated to real estate (57% importance), the age of the client's partner (55% importance), the total amount of wealth (34% importance) and the amount of capital invested in life insurance (20% importance). Here again, results appear to align with the intuition that older and wealthier individuals are more likely to be interested in succession related matters.

Additional revenue generation [O4.] appears to be interesting as a combinations of 6 core parameters: the revenue difference between household's members (100% importance), the total amount of revenue generated by the household (76% importance), its expenses (70% importance), the age of the client's partner (79%), the household's total assets (81% importance) as well as the amount of real estate wealth owned by the household (71% importance). Results on this front are slightly harder to interpret but seem to point towards the idea that households seek to optimize revenue generation through their professional activity or by leveraging real estate to maximize their cash-flow from rents.

When it comes to retirement preparation [O5.], 7 core features were selected by the feature filtering process and only 4 displayed an importance to predict individual interest, namely: the current professional revenue of the household (importance = 100%), the age of the client (resp. his/her partner) (importance = 69% (resp. 59%)) and the total revenue of the household (including existing pensions etc..) (importance = 46%). This aligns with the intuition that older active households with higher level of revenue are more interested in the topic of retirement preparation than other households (especially as, in France, the wealthier an individual, the lower his/her replacement rates according to public pension schemes) .

The question of professional assets protection [O6.] was shown to be most impacted by 5 of the 7 features pre-selected according to the mutual information criteria. First, the most important factor for the algorithm was the amount of wealth tied to professional assets (100% importance), followed by a host of secondary factors: the total client wealth (20% importance), the difference in revenue between the client and his/her partner (14% importance) as well as the amount of wealth allocated to real estate (14% importance) and liquidities (13% importance). This also aligns with the intuition that this kind of topic is more linked to individuals with an important amount of wealth tied to professional assets who wants to secure their revenue streams.

Finally, the appetite of households for other projects [O8.] appear to be driven by a mixture of 12 components. The total amount of assets owned by the household (100% importance) and their level of expenses within their budget (92% importance) stand at the top of the mix. This is then followed by a host of considerations on the revenue decomposition of the household: difference in revenue between the members of the household ( 65% importance), total revenue (42% importance), revenue from real estate (30% importance), revenue from professional activities (29% importance) etc... In essence, the scores associated to other projects appears to be correlated to standard parameters which would be used to assess the eligibility of a household for a loan.

Discussion:

Limitations:

The current study presents a number of limitations. First, the objectives and goals detailed in table (4) and used to calibrate the classification algorithms were specified by a small panel of french financial advisors. Although the scores currently captured through the system show the relevance of the proposed classification, the resulting structure may not fully represent the best practice for financial advisors and associated professionals. Besides, although seemingly simple, it may not be appropriate for all geographies as the expert panel used to design the structure naturally presented a localisation bias.

Second, the data used to calibrate the system relied on information from French households. Variations would therefore be expected when replicating the study on a different geography. The french market is indeed specific when it comes to certain financial products dedicated to retirement preparation and to the usage of life insurance invest-ments. As a result, re running the study and its process on a different market, such as Germany, the UK or the US, may result in a change of the features deemed important by the proposed system and different level of predictive accuracy & algorithmic specificity.

Third, the data-set fed to the classification algorithms was composed of high net worth households (see table (1)), whose revenue places them in the top 5 to 1% (income wise) of the french economy (i.e. revenue above 100k$ per year). Additionally, their wealth (which is in the 1 to 2M$ range) is mostly structured around real estate assets. Although this is common and representative of this fringe of the population, this typical asset structure is different from the one of households whose income is in-between the third and eight decile of the french population and from the one of ultra high net worth individuals (wealth above 5M$) [whose assets are mostly geared towards financial products]. The results of the study may therefore be different if it were to be replayed on a different subset of the population.

Finally, the study (and its results) could benefit from a larger sample. The initial data-set indeed encompassed a total of N ≈ 1000 households and the objective with the lowest interest (i.e. O6) was only applicable to about 100 households. This generated a relatively high standard standard error (up to 5%) on the ROC estimation during the cross validations. Ideally, having a subset of 10k households (i.e. increasing the size of the data-set by a factor 10) would likely result in smaller standard deviations (likely by a factor 2 to 3).

Future research opportunities:

In light of the current results, there are two main areas that could benefit from further research.

On one hand, it could be interesting to further the current research on predicting individuals financial objectives. The results presented in the previous section indeed show that data pertaining to wealth can be used to predict most of the aspects of households financial behavior with the exception of interests towards insurance topics represented by objective [O7]. It would therefore be interesting to see what kind of predictors can be used to assess households interest towards risks (and associated insurance products). Second, it could be interesting to replicate the study on a different fringe of the population to explore the differences in interests between diverse socio-economic communities (by notably differentiating on household's income). It is indeed likely that individuals with less wealth would not be as interested in tax optimization or succession topics, while ultra high net worth individuals would have more complex considerations towards their financial assets. The variables used for prediction purposes and their importance would therefore likely change. And a general ensemble model could be appropriate to design a predictive system which could apply to an entire population in a given country.

On the other hand, beyond a natural follow up to the study, it could also be interesting to move past the considerations of objectives to see whether or not it is possible to accurately predict detailed individuals goals (e.g.03b, 04a etc...). This could indeed represent an additional step towards the design of a self sustainable system which could provide households with a financial next-best-action (N.B.A). This could be very valuable as it could enable the democratization of wealth management advice and practices to a less wealthy clientele. The idea would indeed be that technology-enabled financial professionals (i.e. financial advisors) would provide support to their clients and that this interaction would train systems that could directly generate recommendations to individuals whose revenues and wealth is insufficient for them to benefit from professional services.

Conclusion:

This study shows that machine learning techniques can be used to accelerate client discovery in the wealth management industry. This could notably be of use in recommendation systems which analyse households financial profiles to offer them suitable financial products and legal solutions. The results proposed in this paper indeed show that standard algorithms yield a good to superior performance (ROC above 80%) with respect to the prediction of households interest towards the most wealth management topics (e.g. succession, retirement preparation, professional assets protection....). Those predictions appear easily replicable as predictors consists in simple aggregates detailing the wealth of a household. The study was however non conclusive when it comes to predicting individuals appetite towards insurance solutions dedicated to the protection of relatives.

Additional research could be here beneficial to identify additional predictors improving the predictive power of the proposed algorithms. Besides further investigations (both functional and technical) would be helpful to understand the drivers of individuals' appetite towards insurance solutions for their relatives.

Figure 1 .

 1 Figure 1. Classification techniques -calibration pipeline.

Table 1 .

 1 Households revenue decomposition (k$/year) in the data-set.

	Variable	r pro i	r pensions i	r mov i	r re i	r others i
	Mean value	99 885.8	39 886.9	39088.5	27 111.4	33 216.9
	Std	117 007.6	35 432.8	122 550.6 34 013.1	119 980.5

Table 2 .

 2 Households assets decomposition (M$/year) in the data-set.

	Variable	A real i	A mov i	A dispo i	A savings i	A pension i	A	li f e i	A pro i	A other i
	Mean	1.18	0.34	0.16	0.19	0.25	0.32	1.31	0.15
	Std	1.85	0.86	0.32	1.53	0.89	0.72	2.51	0.36

Table 3 .

 3 Machine learning algorithms calibration results.

	Clients' s Objectives	Best sampling	Best algorithms ROC (Std)
	O1. Accumulate capital	up-sampling	RF	84.7 (2.3)
	O2. Optimize your taxes	ROSE	RF	80.9 (2.1)
	O3. Organize assets' transfer	up-sampling	RF	95.8 (2)
	O4. Generate additional revenue	up-sampling	RF	94.1 (2.4)
	O5. Prepare for retirement	up-sampling	RF	85 (2.8)
	O6.Protect professional assets	up-sampling	RF	99 (1)
	O7. Protect relatives	up-sampling	RF	70.1 (3.4)
	O8. Other projects	up-sampling	RF	96.1 (1.9)

Table 4 .

 4 Financial objective -proposed standard framework.

	D ¯etailed Goals [% of households interested]
	Objectives [% of households interested]

Table 6 .

 6 Machine learning algorithms calibration results with down sampling.

	Clients' s Objectives	RF -ROC	SVMR -ROC Rpart -ROC	CTree
	O1. Accumulate capital	84.7 (2.3)	68.7 (3.1)	66.6 (1.8)	68.0 (3.3)
	O2. Optimize your taxes	73.8 (5.7)	71.8 (4.5)	66.3 (6.4)	71.0 (5.9)
	O3. Organize assets' transfer	95.8 (2.0)	81.8 (4.8)	73.5 (6.4)	79.5 (2.3)
	O4. Generate additional revenue	94.1 (2.4)	73.5 (2.8)	60.7 (4.0)	72.0 (2.5)
	O5. Prepare for retirement	85.0 (2.8)	76.1 (1.5)	71.5 (1.6)	73.0 (2.8)
	O6.Protect professional assets	99.0 (1.0)	89.9 (4.6)	78.7 (3.7)	90.1 (1.8)
	O7. Protect relatives	70.1 (3.4)	62.6 (3.7)	53.2 (3.7)	60.8 (3.9)
	O8. Other projects	96.1 (1.9)	76.4 (2.8)	64.9 (4.1)	68.8 (6.0)

Table 7 .

 7 Machine learning algorithms calibration results with upsampling.

	Clients' s Objectives	RF -ROC	SVMR -ROC Rpart -ROC	CTree
	O1. Accumulate capital	80.0 (4.7)	74.4 (5.6)	66.2 (3.2)	66.6 6.5
	O2. Optimize your taxes	80.9 (2.1)	76.1 (3.1)	66.2 (6.4)	68.1 (4.5)
	O3. Organize assets' transfer	86.4 (4.8)	81.1 (4.4)	72.7 (4.1)	78.1 (4.1)
	O4. Generate additional revenue	73.3 (8.0)	68.9 (7.1)	60.5 (7.7)	63.0 (5.7)
	O5. Prepare for retirement	83.5 (4.5)	73.6 (4.5)	74.6 (6.1)	73.1 (4.8)
	O6.Protect professional assets	82.8 (5.0)	80.4 (3.8)	70.3 (2.0)	75.2 (3.8)
	O7. Protect relatives	68.9 (4.1)	69.1 (2.7)	57.3 (1.8)	63.0 (5.8)
	O8. Other projects	76.9 (3.3)	71.7 (4.1)	59.9 (3.8)	62.7 (7.1)

Table 8 .

 8 Machine learning algorithms calibration results with ROSE sampling.

Collection can even be fully delegated to a client through a digital medium (e.g. an online "K.Y.C" portal (Know Your Client)).

This is consistent with the documented fact that, for now, wealth management services are mainly open to households with more than 500k$ of assets[START_REF] Cocca | Potential and limitations of virtual advice in wealth management[END_REF] in most mature countries

Manymore produces applications for financial product distributors (e.g. wealth managers). The firm currently has the second largest market share in France (see https://www.manymore.fr/)

December 7, 2022 6. Appendix: December 7, 2022