Predictive Analytics to Help Prevent Fraud
Turning historical patterns into timely, controlled intervention
The difference between detecting and preventing
Fraud analytics has often been deployed after a loss: investigators extract transactions, look for unusual activity, and reconstruct how a scheme operated. That work remains necessary, but organizations that possess timely transaction data can apply some of the same analytical discipline before a payment is released, a vendor is activated, or a claim is approved. The distinction is not the sophistication of the algorithm. It is whether the organization can intervene while an identified risk remains actionable.
Predictive analytics estimates relative risk from historical observations and current indicators. It does not determine intent, prove wrongdoing, or eliminate the need for human review. A high-risk score is an invitation to examine the underlying facts. A low-risk score is not proof that a transaction is legitimate. This distinction is essential when designing controls, allocating investigators, and explaining results to an Audit Committee.
Design the analytical question before selecting the technology
Begin with a clearly defined decision: Which payment, vendor change, claim, or journal entry can be reviewed before it becomes irreversible? Define the relevant population, data fields, review capacity, escalation rules, and consequences of an erroneous alert. An impressive model that produces more alerts than the organization can investigate may weaken rather than strengthen the control environment.
Historical cases are useful for understanding patterns, but known fraud is an incomplete and potentially biased sample of the actual exposure. Data quality, changes in business processes, seasonality, and adversarial adaptation can undermine performance. Risk indicators should therefore be evaluated for stability, explainability, and practical relevance rather than judged solely by statistical fit.
From data to preventive action
An effective implementation combines transparent rules with, where justified, more advanced models. Duplicate invoices, shared bank accounts, changes to vendor payment instructions shortly before disbursement, and unusual transaction timing are understandable starting points. Additional modeling can prioritize combinations of indicators that merit review. Every flagged event needs an owner, documented disposition, and an opportunity to correct the underlying data or control.
For example, a vendor-bank change made shortly before a large payment might trigger a hold and independent callback. The appropriate control is not to accuse the vendor or employee of fraud. It is to validate the change through a trusted channel before funds move.
The Medicare example—and its limits
The U.S. Government Accountability Office has described the Centers for Medicare & Medicaid Services’ use of predictive analytics and other information to identify high-risk Medicare fee-for-service claims and providers. The transferable lesson is that historical billing patterns and risk indicators can support earlier review. The public-sector program should not be represented as proof that any particular commercial model will achieve comparable savings or detection accuracy.
Boards should ask for evidence of the model’s operating effectiveness: how often alerts lead to substantiated exceptions, how much investigator time is consumed, how frequently legitimate activity is delayed, and whether preventive controls actually reduce avoidable losses.
| Design question | Practical measure | Why it matters |
|---|---|---|
| How many alerts? | Alerts per 1,000 transactions | Shows workload and threshold calibration |
| Are alerts useful? | Confirmed exceptions / alerts reviewed | Tests precision; not proof of fraud |
| Is action timely? | Time from alert to disposition | Measures opportunity to intervene |
| Are legitimate transactions affected? | False-positive holds and release time | Identifies operational harm |
| Is performance sustained? | Trend in outcomes by period and business unit | Identifies drift and control breakdown |
Implementation and governance
A disciplined pilot begins with one defined process and an auditable baseline. It should include documented data access, model or rule logic, threshold approvals, exception case management, and periodic review of false positives and false negatives. Management owns the operational control; Internal Audit can independently assess its design and performance without assuming responsibility for operating it.
The objective is not a dashboard of anomalies. It is a repeatable system in which risk signals lead to proportionate action, feedback improves detection, and governance bodies receive evidence of outcomes rather than inflated claims of prevented fraud.
Professional references
- U.S. Government Accountability Office, reports on CMS Fraud Prevention System and predictive analytics in Medicare.
- COSO and ACFE, Fraud Risk Management Guide, Second Edition (2023).
- The Institute of Internal Auditors, Global Internal Audit Standards (effective 2025).