Yes, that's a common approach. You can take a dataset of consumer features at the point in time when loans were opened along with information about the loan outcomes and try to predict the loan outcomes. You can't just take a kitchen sink approach with the features though because there are regulations that demand a level of explainability. To get a sense of the basics, I think the book Intelligent Credit Scoring by Naeem Siddiqi [0] is very good.
i would also like to add that modelling in credit risk is not just about yes / no answers around loan outcomes.
There are lots of other goals that are regularly modelled such as default rates, profit optimization, loss minimization, delinquency and payoff rates at specific parameters ... endless options
There are also lot of different ways in which these models are implemented ( decision trees, statistical analysis, ML... )
Some examples of (real life) projects include:
- if our institution offers this card to clients with 750 credit scores vs 790 credit scores, how does my profit move vs my losses and what the factors to limit losses while maximizing profits
- how do I minimize my costs for servicing this card while keeping profits at the max ?
- what rewards options lead to the highest number of preselected / qualifying clients taking up a product at the lowest cost
- what contact strategies are best for specific types of clients if they are late on payments - call or email or text or legal letter? which strategies are the cheapest? which strategies give what this institution considers to be the best response ? which lead to fastest full payment? fastest partial payment? which lead to getting back to a regular payment plan?
- how can we identify clients who have a lending product with us who might be on the market for another lending product in the next 12 months? in the 6 months?, those who might need a limit increase pro-actively? those who whose might need a limit decrease pro-actively?
And, one of the largest area pf credit risk evaluation is real time decisioning on transactions: 'is throwaway201606 really buying $6000 of apple products, in person, at this mall in Toronto, Canada right now when I (the system) know I he bought a daily Wendy's Spicy chicken sandwich 10 minutes ago in Dallas" and should we allow this payment
Some example of how models are used here include ( note that modelling helps establish which transactions to look at more carefully and which to ban outright among other things )
- predict what type of terminals are being targeted: scammers -> we have left bank ATM machines alone and started looked at gas pumps:
- predict where transactions of interest might come from: scammers -> we do scams on site A at Christmas, scams on site B in the summer or we do site A scams with brand Y card and do site B scams with brand Z card
- predict behaviour patterns of transactions of interest: we always test the cards we will use by purchasing a $5 'brand x' gift card online 10 minutes before
Yes, completely agree, I omitted the part about not just modeling binary outcomes for the sake of brevity. I was even considering linking the article Capital One: Exploiting an Information-Based Strategy [0], showing that one of Capital One's innovations was successfully modeling more complicated outcomes like profitability.
[0] https://towardsdatascience.com/book-review-intelligent-credi...