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‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

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Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#41

Post by kqr »

mjburgess wrote: post #37

Accuracy is almost never the right statistic. Here, profitability is. As soon as you realise the target statistic applies a human-goal-value-weighting to the prediction outcomes, it should be easier to see why ML systems are often unsuited to the task they're set. Here, those 5% of cases are likely business-ending or business-making: precisely because they arent naively routine.

I think you can be even bolder: accuracy never matters. It's always about the consequences, not probabilities. Sometimes the two are the same, but most of the time they are not.

Also worth noting that the arithmetic expectation is only a good way to measure the profitability if we are talking about small amounts compared to your total wealth. For any other case, you should use the geometric expectation of total wealth to evaluate options. (This is equivalent to maximising log wealth, the Kelly criterion.)

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#43

Post by kqr »

Brian_K_White wrote: post #40

Title is oddly framed. What is interesting or useful about merely not-quite predicting what a human will do? Do the AI's 5% discrepency picks perform better or worse than the human's picks? The title is worded to suggest the AI can, or is very close, do the human's job, which could totally be so. But it could also be that the AI loses 5% vs the human, and the bank only makes 5% on loans in the first place, and so los…

Given the research by Meehl, if there's a decent numeric rule and a human decision that are closely predicting each other, my money would be on the numeric rule being slightly more accurate.

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#44

Post by kqr »

epolanski wrote: post #18

I don't think you need AI. Like in Italy the total of your loans cannot exceed 1/3rd of your monthly salary, as simple as that.

Does this mean most Italians rent their homes for life and a small fraction purchase their homes in cash?

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#46

Post by yobbo »

kqr wrote: post #41
mjburgess wrote: post #37

Accuracy is almost never the right statistic. Here, profitability is. As soon as you realise the target statistic applies a human-goal-value-weighting to the prediction outcomes, it should be easier to see why ML systems are often unsuited to the task they're set. Here, those 5% of cases are likely business-ending or business-making: precisely because they arent naively routine.

I think you can be even bolder: accuracy never matters. It's always about the consequences, not probabilities. Sometimes the two are the same, but most of the time they are not. Also worth noting that the arithmetic expectation is only a good way to measure the profitability if we are talking about small amounts compared to your total wealth. For any other case, you should use the geometric expectation of total wealt…

Outcome * probability = expectancy, which is the quantity usually maximised or minimized in AI.

The problem in this case was defined as reproducing the human scores. Mainly, it's a demonstration of the information content in the data and the scores. To me, it demonstrates how simplistic the human scores are.

With information about historic outcomes, we could have compared the effectiveness of the "credit score method" with some other AI algorithm that was optimizing total value.

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#47

Post by Brian_K_White »

platers wrote: post #23
Ekaros wrote: post #17

Loan decision is relatively straightforward algorithmic process. I see no reason why AI is needed or wouldn't give similar answers.

Shouldn't the sentence be flipped in that case? I see no reason why expensive human is needed or wouldn't give similar answers.

I think they mean algorithmic as in, simpler and deterministic and auditable plain if/then/math algorithm, vs black box magic AI.

Even if an AI can produce seemingly the same results as a human, it should be out of the question anyway to let an inscrutable black box make decisions over people's lives. Because at least with a human you (their boss, or a judge, etc) can ask them "Why did you decide that?" and they can tell you. A racist or mysoginist or religious human can be identified and fired or corrected etc. How do you judge if an AI is giving inhumane decisions?

The decisions themselves can't really be judged, only the process that generated them, and you can't see that process in an AI.

If someone doesn't get a loan, and someone else does, you can't tell that wasn't right just from the final result.

Even if the results "look" wrong, like only 30% of black people get the loan while they made up 40% of applicants, even that could possibly be exactly correct, but you can't know if you can't see the process.

But a human can be asked, and simple algorithm code can be read.

Probably these days the human has so little discretion anyway that the corporate policy is the algorithm and the human is pointless anyway except as a sham human-looking interface to appease customers. It helps sales to have a human, but the human in fact wields none of the human power that the customer wants a human for.

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#48

Post by anaganisk »

shadowgovt wrote: post #9
jokethrowaway wrote: post #5

I really wish we would stop calling this AI. I understand startups need funding from VCs whose idea of technology is "BLOCKCHAIN AI" but, being interested in AI, the amount of spam I have to dig through to find actual AI work is insane.

What does actual AI work look like these days?

If key==0: print(“its zero”) else: print(“here is your 10 sec un-skippable ad”)

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#49

Post by kqr »

yobbo wrote: post #46
kqr wrote: post #41

Earlier quoted context omitted.

I think you can be even bolder: accuracy never matters. It's always about the consequences, not probabilities. Sometimes the two are the same, but most of the time they are not. Also worth noting that the arithmetic expectation is only a good way to measure the profitability if we are talking about small amounts compared to your total wealth. For any other case, you should use the geometric expectation of total wealt…

Outcome * probability = expectancy, which is the quantity usually maximised or minimized in AI. The problem in this case was defined as reproducing the human scores. Mainly, it's a demonstration of the information content in the data and the scores. To me, it demonstrates how simplistic the human scores are. With information about historic outcomes, we could have compared the effectiveness of the "credit score method…

One little expansion: what you described first is the arithmetic expectation, which is a good approximation when the numbers involved are small compared to total wealth.

When you start taking larger bets, you want the geometric expectation of total wealth, i.e. (current wealth + outcome)^probability.

(This is the Kelly criterion for judging significant opportunities.)

Re: ‘Simple’ AI can anticipate bank managers’ loan decisions to over 95% accuracy

#50

Post by bob1029 »

throwaway81523 wrote: post #3

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.

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