An explanation of the statistics behind false positive rates in financial operations

Aging systems from different sources create a pile of manual reconciliation and quality control work for financial institutions. Creating a growing need for manual operations handling.

Even when using RPA and no-code software, labor is needed to deal with the large volume of exceptions. Most exceptions are false positives and can be tracked using an error (or confusion) matrix.

High false positive rates often occur when the predicted factor, like errors, occur very rarely in the overall sample population. The threshold can be changed to reduce false positives, but this could lead to too many false negatives, meaning true exceptions could get through undetected.

Artificial intelligence algorithms are particularly good at reducing false positives. There are many methods, but most seek to reduce datasets to increase the percent of what is being predicted in a given dataset.

AI can significantly improve its accuracy over human decision making by reducing false positives and improving the predictive capability to spot true positives.

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