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Hersh's avatar

Re "using intelligence in addition to data can help uncover risk signals earlier in the scheme" -- in theory (admittedly always a suspect proposition), AI could develop and apply increasingly accurate models of precursors of malfeasance and non-compliance, such that government could conceivably head fraud off at the pass, e.g., require entities to meet heightened standards before receiving payments etc.

This would go beyond detecting subtler risk signals, and instead involve identifying patterns that arise prior to any actual fraudulent activity. One can envision AI crunching vast amounts of data to to detect developments that indicate actors may be laying the groundwork for fraud.

We already do this in the national security context, where a sudden rise in an individual's debt (e.g., credit card balances) or financial obligations (e.g., alimony payments per a recent divorce) prove nothing, but based on previous espionage case, increase the risk of recruitment by foreign intelligence services. Presumably, there are similar, but as-yet-unknown and extremely subtle, non-obvious precursors in the government payments space -- activities that do not indicate actual fraud, but (especially in combination with other precursors) demonstrably increase the probability of future malfeasance.

To nakedly toot my own horn, I discussed this (using the term "prescient proactive compliance") in a recent post here: https://www.linkedin.com/feed/update/urn:li:activity:7432448138701082624/

Steve-O's avatar

Great analysis of a needed strategic shift to combat fraud and change the annual backward-looking improper payments exercise.

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