Supporting the digital transformation of the financial industry with data science

ILB DataLab

The ILB DataLab is an applied research unit made up of data scientists who are well versed in the problems of the financial industry. The team is recognized for its ability to carry out projects involving companies, public institutions and experts, particularly academic experts. The techniques cover data processing and analysis, as well as the development of AI algorithms and machine learning.

Our identity

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Comprehensive AI offering

We provide all types of consulting and R&D projects in data science from conception through to industrialisation on issues defined in association with our partners. We adapt the format and duration of the collaboration to best meet everybody’s needs.

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Scientific rigour

We are able draw on the most relevant expertise (machine learning, quantitative finance, econometrics, actuarial sciences, etc.) for each project thanks to our preferential access to the 1,000 or more researchers in the Louis Bachelier Network. All our projects are carried out under the scientific direction of Jean-Michel Lasry.

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Security and confidentiality

We adhere to very strict confidentiality and data access standards on our secure information systems.

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Flexibility and agility

The team can adjust to the partners’ various constraints, whether these be legal or related to information systems, processes or data quality.

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Proven track record

The ILB DataLab has carried out more than thirty projects with some twenty different partners. In nearly 90% of cases, these projects are in the test phase or already industrialised.

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Controlled costs

Project funding is eligible for tax benefits (sponsorship 60% or Research Tax Credit 60%) allowing the partner to maximise the impact of its human capital.

Some figures

6

Years experience

15

Data Scientists

+ 30

Projects

+ 10

Internal research topics

39

Academic partners

60 %

Tax allowance

Case Studies

Improved ROI from sales efforts

Improved ROI from sales efforts
Prediction of loan non-payment

Prediction of loan non-payment
Prediction of bank customer churn

Prediction of bank customer churn

Improved ROI from sales efforts

Improved ROI from sales efforts
The issue

How to recommend strategic and operational changes with the aim of increasing the value of credit reports

The approach
  • Identification of levers for improving ROI, seasonal factors, opportunities and file quality
  • Development of an analytical profitability model for sales prospecting
  • Prioritization of prospecting by type of customer and demand
The results
  • One-third reduction in costs with no decrease in revenue
  • Management of the sales team made more flexible

Prediction of loan non-payment

Prediction of loan non-payment
The issue

How to dynamically assess the latent risk of home loans to individuals by creating a credit score based on customer behaviour

The approach
  • Creation of explanatory variables with high added value from data available internally and in open data
  • Development of a machine learning model based on decision trees
  • Risk assessment by a credit score defined as the probability of non-payment within six months
The results
  • Effectiveness of the credit score: multiplied by 2 with respect to the partner’s existing indicators and by 3.2 with respect to the “Basle” regulatory credit rating
  • Concentration of resources on cases requiring preventive management of defects and adaptation of the provisioning system
  • Mapping of underwriting risks by agency and by region

Prediction of bank customer churn

Prediction of bank customer churn
The issue

How to build predictive indicators of customers using several banks or churn

The approach
  • Creation of explanatory variables with high added value, based on available data (bank flows, volume of savings, use of means of payment)
  • Development of a self-learning gradient descent model
The results
  • Construction of a monthly score for each client indicating the risk of churn within six months
  • Learning automatically and regularly updated with new data and performance monitoring of the machine learning model implemented
  • Very good feedback from customers contacted, doubling the number of products purchased and identification of life events based on the analysis carried out

Our Team

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Jean-Michel Beacco
CEO ILB
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Jean-Michel Lasry
Scientific Director
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Louis Boulanger
Head of ILB DataLab
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Enzo Dalby
Data Scientist
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Marine Neyret
Data Scientist I Market finance
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Anthony Galtier
Data Scientist
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Thibaud Barreau
Data Scientist

Contact

For further information, please contact us: contact_datalab@institutlouisbachelier.org

Join us

The DataLab is currently recruiting trainees

Internship opportunities available