4 ms·
The lack of transparency is for a good reason: if a model's parameters become known, they can be gamed and lose their predictive power. Not only do banks etc k
by stevesimmons 8y ago
The lack of transparency is for a good reason: if a model's parameters become known, they can be gamed and lose their predictive power.
Not only do banks etc keep their models secret from customers, they keep them secret from other departments. The credit risk strategy team, for instance, won't want to risk customer service staff 'helping' customers alter their application details to get their scores over a cut-off.
(I used to run credit risk strategy, fraud, collections, operations etc for two credit card companies)
- A1kmm 8y agoGiving customers of banks transparency of the models as a third party could be a good application of causal reasoning. Each individual customer of a bank only has their own parameters, and a yes / no output from the bank. A third party designed to help customers get approved could aggregate data across multiple customers, generate hypotheses of what changes would artificially lower the bank's perceived risk for a customer (which would also require it understand what sort of changes customers can make easily), and test those hypotheses to refine a model. It could optimise for revenue, paying customers for information, and receiving income if it succeeds in getting them approved.