[pps43]

That game is common in credit scoring classes for fresh analysts. The class is split into small teams. Each team is given ten anonymized, but real, credit applications and corresponding credit bureau pulls. Five from customers who subsequently defaulted, and other five from customers who paid the loan back. Each team tries to guess which are which. The team that makes the most correct guesses wins.

Then the results are compared with FICO score, and usually FICO is clearly better. Even with people with banking experience on the teams it's very rare to see humans beat the model, partly because humans tend to base their decisions on irrelevant details and their own biases.

[taurine]

Let's change the game. You can allocate an investment to either of two banks. When building credit scoring models, one bank has access to just FICO scores, the other bank also has access to FICO scores in addition to behavioral and signature data. Which bank do you allocate your cash to?

Now change the game so FICO is unavailable: For instance, when micro-lending to third-world country entrepreneurs. Do you still feel these digital signatures are irrelevant to making better credit risk decisions?

[pps43]

All banks already use behavioral scores in credit card line management. Mortgages are a different story because there's little they can do once after the underwriting. That said, independent variables that go into behavior risk scores are not like the ones from the article.

In any case that game is pure gedankenexperiment, at least in the US. In reality banks have to comply with ECOA and host of other rules and regulations that limit the types of data they can use in credit decisions.

There may be more freedom outside the US, but even there social media probably carries much stronger signal.

[mmt]

> Do you still feel these digital signatures are irrelevant to making better credit risk decisions?

Yes, absent a credible theory as to why a particular characteristic could reasonably be linked to loan performance.

Otherwise, it's too likely the model could fall prey to confusing correlation with causation.

[taurine]

Let's say you add these digital signature variables to your credit risk scoring model anyway. The model then falls prey to confusing correlation with causation. What happens to the performance of the model?

[mmt]

I have no idea, as merely adding them may have no effect at all.

However, depending on them exclusively (or in substantial/majority part), which I believe is the main premise, the eventual performance will depend entirely on if the the actual causal relationship which created the correlation holds true. If it doesn't, the model would no longer be predictive.

https://en.wikipedia.org/wiki/Confounding