‘Simple’ AI Can Anticipate Bank Managers’ Loan Decisions to Over 95% Accuracy

A new research project has found that the discretionary decisions made by human bank managers can be replicated by machine learning systems to an accuracy of more than 95%.

Using the same data available to bank managers in a privileged dataset, the best-performing algorithm in the test was a Random Forest implementation – a fairly simple approach that’s twenty years old, but which still outperformed a neural network when attempting to mimic the behavior of human bank managers formulating final decisions about loans.

The researchers, who had access to a proprietary dataset of 37,449 loan ratings across 4,414 unique customers at ‘a large commercial bank’, suggest at various points in the preprint paper that the automated data analysis that managers are given to make their decision has now become so accurate that bank managers rarely deviate from it, potentially signifying that bank managers’ part in the loan approval process chiefly consists of retaining someone to fire in the event of a loan default.

The paper states:

‘From a practical perspective it is worth noting that our results may indicate that the bank could process loans faster and cheaper in the absence of human loan managers with very comparable results. While managers naturally perform a variety of tasks, it is hard to argue that they are essential for this particular task and a relatively simple algorithm can perform just as well.

‘It is also important to note that with additional data and computational power these algorithms can be further improved as well.’

The paper is titled Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?, and comes from the  Department of Economics and Department of Statistics at UoC Irvine and the Bank of Communications BBM in Brazil.

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