Classifying Unidentified Payments Restores Millions to a Client’s Bottom Line

Every month, a U.S. corporation had to hand over millions of dollars in customer payments to the state as “abandoned property.” Why? Their customers failed to include an address, account number or other personal data. Was it possible to distinguish payments for services rendered, and add millions of dollars to the bottom line?

The corporation had already invested in extensive research to correctly classify several hundred payments as “state-defaulted” or “company-serviced.” Starting with this “training set” of unidentified accounts, A+I senior statistician Dr. William Fairley applied a statistical algorithm for classifying payments according to the few known facts, such as the payment mode, amount and date. This “learning algorithm” was then “trained” to determine a classification rule.

The A+I algorithm correctly classified more than 80% of the payments in the training set, and achieved a success rate of over 60 percent when applied to other payments.

The algorithm estimated the classification for unidentified payments, and the probability that a payment was “state-defaulted.” Applying these percentages to the payment amount determined the amount due the state, and the balance due the company.

Dr. Fairley satisfied state auditors that the statistical method divided the unidentified payments with objectivity and reasonable accuracy, and helped the client recoup millions of dollars.