Case Studies
Assessing a Portfolio Climate Alignment Score
The issue
- Determine the main differences between a predefined climate transition score and existing climate alignment methods
- Test the transition score against alignment metrics
The approach
- Qualitative analysis of the different elements on which the transition score is built
- Three quantitative tests to estimate the compatibility between the transition score and existing alignment metrics: correlations, overshoot and integrating an alignment criterion in the transition score
The results
- An alignment metrics has to be used in addition to the transition score
Carbon Impact of an Auto Credit Portfolio
The issue
- Development and application of a methodology to measure the environmental impact of an automobile credit portfolio through a Life Cycle Analysis (LCA) approach
- Analysis of the results integrating socio-demographic and socio-economic data
The approach
- Creation of a model to obtain the annual mileage of customers based on socio-demographic data as well as vehicle data
- Clients profiling according to how likely they are to purchase a low-emitting vehicle
- Suggestion of measures the company can implement to reduce its footprint
The results
- Some countries have more polluting fleets than others
- Clients’ propensity to purchase less polluting vehicle (electric or hybrid) will depend on their income and other factors
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
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
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
