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Choose a system by matching its evidence and operating design to your bank’s specific fraud risks—not by accepting a vendor’s general “AI accuracy” claim. Define the use case and intended actions, examine the model and data, test performance in a relevant context, assess operational and security fit, and assign clear owners for validation and ongoing oversight.
Start with the bank’s risks and the job the system must do
Before contacting vendors, document the risks and operations the system is meant to address. The bank’s BSA/AML risk assessment is a useful foundation: the FFIEC advises considering relevant products, services, customers, geographic locations, and transaction data. The categories and weighting are bank-specific, and monitoring should reflect the risks identified.
Be precise about the use case. Transaction fraud, suspicious-activity monitoring, account takeover, and identity or authentication risks are not interchangeable. A vendor’s claimed coverage of one does not establish suitability for another. Specify the fraud types, products, payment channels, customer segments, geographies, transaction volumes, data sources, and decision points in scope.
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Understand which rules and guidance apply
This article uses U.S. banking guidance as its primary frame; requirements and supervisory expectations vary by jurisdiction and institution. On April 17, 2026, the Federal Reserve, OCC, and FDIC issued revised interagency model-risk guidance, superseding SR 11-7 and the 2021 interagency statement for bank systems supporting BSA/AML compliance. The agencies describe it as risk-based guidance, not a prescriptive standard, and say practices should be tailored to the bank’s model-risk profile and the size and complexity of its operations.
The Federal Reserve says the guidance is expected to be most relevant to banking organizations with over $30 billion in total assets. The guidance may also be relevant to smaller organizations with significant exposure due to model prevalence, complexity, or activities outside traditional community banking. The asset figure is an applicability framing, not a blanket assurance that smaller banks have no model-risk responsibilities.
The guidance defines a model by its method and use: a complex quantitative method, system, or approach applying statistical, economic, or financial theories to process input data into quantitative estimates. It excludes simple deterministic rules and software without those theoretical underpinnings. A vendor calling a product “AI” does not settle its classification; have the bank’s model-risk function assess the actual methods and intended use. The guidance’s exclusion of generative and agentic AI from its scope does not remove the need for bank governance and controls over those tools.
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The issuing agencies state: “Each banking organization ultimately is responsible for adopting model risk management practices that are appropriate and effective for managing the specific risks the banking organization faces or is likely to face.” A vendor’s ownership of the system or proprietary methods does not transfer that responsibility.
For a UK bank, the PRA’s SS1/23 identifies five model-risk themes: identification and risk classification, governance, model development, implementation and use, independent validation, and model-risk mitigants. Its current version is listed as published and effective April 23, 2026; applicability depends on the institution and jurisdiction. NIST’s AI Risk Management Framework 1.0, released January 26, 2023, and its July 2024 Generative AI Profile are voluntary references, not substitutes for applicable supervisory requirements. NIST says the framework is being revised.
Request evidence a bank can actually review
Ask each vendor for documentation that allows the bank, or a qualified independent reviewer, to understand the product and assess it for the proposed use. The agencies’ guidance emphasizes understanding vendor-model conceptual soundness, design, development data, and performance, as well as validating vendor products internally or through outside parties.
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- Purpose and design: Request the intended use, model design, assumptions, limitations, and a description of which components are statistical models, deterministic rules, or other software.
- Data and labels: Ask about data provenance, coverage, representativeness for the bank’s channels and populations, known exclusions, label quality, and how changing or delayed fraud outcomes are handled.
- Performance evidence: Require the tested population, operating context, methodology, time period, outcome definitions, threshold, and limitations. Ask how the results relate to the bank’s intended decisions and workload.
- Change and monitoring: Ask how the vendor monitors drift and performance, documents versions and bank-specific customizations, and notifies the bank of material changes.
- Validation and audit: Establish what evidence and access the vendor will provide for independent validation, audit, explanations, investigation workflows, and outcome analysis, including where proprietary elements limit access.
- Security and continuity: Document data flows, retention, access controls, subprocessors, hosting, incident handling, service continuity, and exit or migration arrangements.
Compare candidates on the same bank-specific scorecard
Use common definitions and evidence requirements across vendors. Set the importance of each criterion according to the bank’s risk assessment and the intended use; the FFIEC says risk categories and weighting are bank-specific. The scorecard below is a procurement aid, not a regulator-prescribed formula.
| Criterion | Evidence to compare | Questions for the bank |
|---|---|---|
| Use-case fit | Coverage of specified fraud types, products, channels, geographies, and decisions | Does the evidence address the bank’s actual scope, or a different use case? |
| Outcomes and workload | Relevant fraud outcomes and resulting investigation workload, with definitions and observation period | Were thresholds and outcome measures defined consistently before comparing claims? |
| Data fit | Input availability, quality, timeliness, lineage, and representativeness | Can the bank supply suitable data, and are material gaps or exclusions understood? |
| Operational fit | Latency, resilience, throughput, integration effort, case-management fit, alert handling, and human review | Can the system work at the bank’s volumes and decision points, with workable escalation? |
| Governance and explainability | Access to outcome factors, challenge and override processes, change records, validation support, and audit evidence | Can responsible bank teams understand and oversee the system’s use? |
| Security and third-party risk | Access controls, data handling, dependencies, incident response, and continuity | Do the controls and vendor arrangements fit the bank’s risk and resilience requirements? |
| Lifecycle cost and dependency | Vendor quotes and bank-specific estimates for implementation, monitoring, customization, maintenance, and exit | What continuing work is required, and how dependent would the bank be on vendor-controlled data or components? |
There is no universal vendor, price, score, or performance threshold established by the cited regulatory sources. Request quotes and estimate costs for the bank’s own deployment rather than treating a market-wide figure as comparable evidence.
Validate the system before relying on it
Plan validation around the intended use, not a vendor’s headline metric. A single aggregate accuracy figure cannot show whether results are suitable for the bank’s population, threshold, or decision consequences. Require enough information to interpret the tested population, label and outcome definitions, time period, and the effects of false alerts and missed events.
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Before production reliance, the bank’s validation process should examine conceptual soundness and performance for the proposed use, implementation and data issues, limitations, and plans for monitoring and outcome analysis. Obtain the access and evidence needed to do this work. If vendor restrictions prevent parts of the assessment, document what cannot be validated and decide whether compensating controls or narrower use are necessary.
Establish how the bank will reassess the system after material changes to the model, data, portfolio, products, services, or deployment context. Ongoing monitoring should determine whether the vendor system remains accurate, reliable, and fit for purpose; bank-specific customizations should be documented and justified.
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Define accountability across business, fraud operations, compliance, model risk, technology, cybersecurity, procurement, and audit. Record who approves deployment, reviews vendor change notices and outcomes, investigates alerts, authorizes overrides, handles incidents, and can suspend or limit use.
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Maintain an inventory of the system and relevant third parties, and assess authentication and access risks whether the bank or a vendor provides the solution. Interagency guidance identifies fraud research, customer service, cybersecurity, and customer reports as useful sources of risk information. Where single-factor authentication is inadequate for the assessed risk, it describes multifactor authentication alongside layered security controls as a mitigation.
Revisit the bank’s risk assessment and controls when it adds products or customer types, enters geographies, changes channels, or encounters new fraud patterns. The FFIEC says independent testing should review the assessment and how it is used in the compliance program; it does not prescribe continuous or periodic risk-assessment updates.
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