The Series
Every GenAI budget line eventually meets the same question from Finance: what is this actually worth? In fraud, that question is uniquely hard to answer, because the value of fraud prevention is an absence. A stopped theft leaves no invoice, no deposit, no ledger entry: nothing to count. This three-part series builds a framework for estimating that invisible value anyway, and defending the estimate in front of a committee. It separates projects that catch more fraud from projects that lower the cost of operating, prices the costs most business cases forget, replaces false precision with honest ranges, and ends with the number to lead with in the room. One worked example, a large financial institution with twelve million transactions a year, runs through all three parts.
Part 1: The Value Model
Fraud projects come in two classes, and confusing them is where business cases go wrong. Detection projects are valued as avoided loss, but the value only counts net of what your recovery team was already clawing back, and net of what the new alerts cost: investigator hours and, above all, the customer friction that lands in other departments’ budgets. Efficiency projects are valued as released hours, and hours only become dollars through a mechanism you can name: a smaller invoice, a renegotiated contract, a deferred hire. Part 1 builds both equations, plus the cross term that appears when you run both projects at once and must be claimed exactly once.
For the full value model, including the formulas and the worked example with real numbers, read Part 1: The Value Model → https://analyticsrusers.blog/2026/08/29/estimating-the-return-on-genai-projects-in-enterprise-fraud-management-part-1-of-3-the-value-model/
Part 2: From Point Estimates to Probabilities
Everything in Part 1 produced a single number, and single numbers invite a question you will lose: where did that come from? Part 2 replaces the point estimates with ranges. It handles the events too rare for ordinary math (the once-a-decade catastrophe whose cost is mostly the client relationships that walk out the door) with named scenarios and structured expert judgment. Then a Monte Carlo simulation turns the whole model into percentiles, with one warning that decides whether the simulation informs or flatters: catch rate and false alarm rate move on a single dial, and simulating them separately invents worlds no threshold could produce.
For the scenario method, the severity math, and the full simulation design, read Part 2: From Point Estimates to Probabilities → https://analyticsrusers.blog/2026/08/29/estimating-the-return-on-genai-projects-in-enterprise-fraud-management-part-2-of-3-from-point-estimates-to-probabilities/
Part 3: The Decision
Value is half the story; the decision lives on the cost side. Part 3 adds the program costs (building, governing, permanently reviewing the AI’s output, and paying for inference, where one architectural choice separates a $28,000 line item from a $4.8 million one). It then sets the whole case against the honest baseline: not doing nothing, but what your own engineers would build in three quarters. The series closes with how to present the result: lead with the cautious number, state the probability of a losing year out loud, and name the architecture the small cost line depends on.
For the cost equations, the build-versus-buy formula, and the final roll-up of the worked example, read Part 3: The Decision → https://analyticsrusers.blog/2026/08/29/estimating-the-return-on-genai-projects-in-enterprise-fraud-management-part-3-of-3-the-decision/