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Comment by throwaway81523 | Hacker News Reader

How does "simple AI" compare with an old fashioned small table lookup and a few "if" statements?

In my experience, many financial products marketed as having "intelligence" are indeed just layers of SQL queries (rules).

The benefit of using SQL rules engine in a financial setting is that you can prove causality and intent throughout. Why a customer was declined for a loan can always be traced deterministically to some rule SQL that legal previously approved per regulations in that jurisdiction.

Decision trees (the base learner in random forests and gradient boosted decision trees) are effectively nested “if” statements, and predictions from tree ensembles (RF and GBDT) are effectively averages of predictions from those nested “if” statements.

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

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

Difference is that random decision forests learn the rules for themselves, they don't have to be programmed in manually by experts. They're one of the most performant "pre-deep-learning" machine learning models and a sensible baseline for many ML tasks before you go out and buy a $1500 GPU

The ability to program approval rules and understand why decisions have been made (and then tweak the ruleset based on economic/statistical analysis) is a feature for these sort of organizations, not a limitation.

There will be elements of AI which are useful, but ultimately banks will want to know why a certain decision was made, and want to incorporate their own economic calculations and forecasts into the model.

Decision forests don't provide that explainability though, how can you interpret averages of hundreds of decision trees?

The "AI" or "machine learning" part is what decides on those statements. Nothing special there, typically banks are using simple methods which produce results that also humans can understand.

Toward Data Science has a neat example [1], scroll to the end to see the sample "scorecard" which can be implemented as lookup tables and ifs. Example shows how the customer gets certain points depending on the age group, home ownership and income group. Sum up these points and you get the total score, which then translates to probability of getting your money back from that customer.

[1] https://towardsdatascience.com/intro-to-credit-scorecard-9af...

A simple AI is merely an algorithm to generate the if statements. It wouldn’t be surprising if the final model could be reduced to 20 or so “ifs”, literally.

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