Overview: A retail bank faced the challenge of optimizing offers to attract new clients and upsell existing clients. The marketing and risk departments were working with different approaches, and there was a need for a unified score that could provide a single measure of expected profitability per client. To address this challenge, our data science team was tasked with developing an AI solution that produces a robust objective score for the expected profitability per client, based on both marketing and risk factors.
Solution: Our team developed a CRM (Customer Relationship Management) model that combines marketing and risk factors to evaluate the expected profitability of each banking product based on our developed methodology product. The model uses Propensity-to-Buy (PtB) models to predict the likelihood of a customer purchasing a specific product, and Probability of Default (PD) models to estimate the risk of the customer defaulting on a loan. The output of the model is a single measure of expected profitability per client, which can be used to rank the Next-Best-Offer (NBO) for each customer.
To develop the CRM model, we first collected and integrated data from various sources, including transactional data, demographic data, and customer interaction data. We then applied various machine learning algorithms, such as logistic regression and random forests, to train and test the PtB and PD models. We also conducted feature engineering and selection to identify the most relevant variables for predicting profitability.
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