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Start with the bank’s risk—not the vendor’s product
Before comparing systems, define what the bank needs to detect and what it will do with the result. A tool designed to identify suspicious activity is not automatically suited to payment fraud, account takeover, identity risks, or authentication decisions. Coverage in one use case does not prove coverage in another.
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Map the scope to the bank’s risk assessment and internal controls. The FFIEC BSA/AML Examination Manual advises banks to consider their products, services, customers, geographic locations, and relevant transaction data. The categories and weighting are bank-specific; monitoring should reflect the areas the bank has identified as higher risk. The FFIEC cautions that “Any single indicator does not necessarily determine the existence of lower or higher risk.”
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Write down the use case
Specify the fraud typologies, products, channels, customer groups, geographies, transaction volumes, and decision points in scope. Note which data the system will receive, how timely that data must be, and which teams will act on the output. Identify meaningful changes—such as new products, customer types, services, or geographic expansion—that could alter the risk profile. The FFIEC manual calls for assessments to account for such changes but does not prescribe continuous or periodic updates.
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Define what the system is allowed to do
State whether an output will prioritize an investigation, create a case, trigger step-up authentication, hold a transaction, decline it, or support another action. For each action, identify the decision owner, override and escalation routes, and likely consequences of a false alert or missed event. A system used to rank cases has a different operational and customer impact from one that can block a payment automatically.
What U.S. banks should know about model-risk guidance
On April 17, 2026, the Federal Reserve, OCC, and FDIC issued revised interagency model-risk guidance. It superseded SR 11-7 and the 2021 interagency statement on model risk management for systems supporting BSA/AML compliance. The guidance addresses model development and use, validation and monitoring, governance and controls, and vendor and other third-party products. It is supervisory guidance, not a prescriptive standard, and calls for practices tailored to an organization’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. That is the Federal Reserve’s 2026 applicability framing—not a rule that smaller banks are always outside model-risk expectations. The guidance may also be relevant to smaller organizations with significant exposure because of model prevalence, complexity, or activities beyond traditional community banking. Its 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.”
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Classification depends on how a system works and is used, not simply whether its vendor markets it as “AI.” The guidance defines a model as a complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates. It excludes simple deterministic rules and software without those theoretical underpinnings. Ask the bank’s model-risk function to assess the actual method and use. The guidance excludes generative and agentic AI from its scope; that exclusion should not be taken to remove the need for bank governance and controls over those tools.
For a UK bank, the applicable framework may differ. The PRA’s SS1/23 page identifies five model-risk principle themes: model identification and risk classification; governance; model development, implementation and use; independent validation; and model-risk mitigants. The page lists its current version as published and effective April 23, 2026. Do not assume that statement applies to a U.S. bank or to every UK institution. NIST’s AI Risk Management Framework 1.0, released January 26, 2023, is a voluntary reference and is being revised; NIST also released a Generative AI Profile in July 2024. These resources do not replace applicable supervisory requirements.
Request evidence a reviewer can examine
Ask each vendor for documentation that the bank—or a qualified independent reviewer—can use to understand the system and evaluate its fit. Proprietary components may limit disclosure, but vendor ownership does not transfer the bank’s responsibility to understand, validate, and monitor a system it relies on.
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- Purpose and design: the intended use, system design, assumptions, limitations, and which components are statistical models, deterministic rules, or other software.
- Data and labels: development and validation data provenance, coverage, exclusions, and representativeness for the bank’s channels and populations; how labels are defined and checked; and how delayed or changing fraud outcomes are handled.
- Performance evidence: results for a population and operating context relevant to the bank, with the methodology, observation period, outcome definitions, operating threshold, and limitations made clear.
- Monitoring and change control: how the vendor monitors drift and performance, notifies the bank of material changes, documents versions, and records bank-specific customizations.
- Validation and audit support: what evidence and access the vendor provides for independent validation, audit, outcome analysis, investigation workflows, and explanations of system results.
- Third-party controls: data flows, retention, access controls, subprocessors, hosting, incident handling, service continuity, and exit or migration arrangements.
These requests follow the agencies’ expectation that banks understand vendor-product conceptual soundness, design, development data, and performance, and validate vendor products internally or through outside parties. Their guidance also describes ongoing monitoring and outcome analysis to assess whether vendor models remain accurate, fit for purpose, and reliable.
Compare candidates on the same bank-specific scorecard
Use common definitions and request comparable evidence from every candidate. Set the relative importance of each criterion based on the bank’s risk assessment and the consequences of errors. This is a procurement aid, not a regulator-prescribed formula: the FFIEC says risk categories and weighting are bank-specific, while the interagency guidance calls for practices commensurate with the system’s use and risk.
| Criterion | What to assess | Evidence to request |
|---|---|---|
| Use-case fit | Coverage of the bank’s specified fraud types, products, channels, geographies, and decision points. | Documented use cases, known exclusions, and results tied to the bank’s intended operating context. |
| Outcome performance | Fraud outcomes and the resulting investigation workload under the intended operating threshold. | Test population, outcome definitions, observation period, methodology, threshold, and limitations. |
| Data fit | Availability, quality, timeliness, lineage, and representativeness of inputs for the bank’s portfolio. | Data requirements, provenance, coverage, exclusions, and handling of delayed or incomplete outcomes. |
| Operational fit | Latency, resilience, throughput, integration effort, case-management fit, alert handling, and support for human review and escalation. | Architecture and workflow documentation, service-continuity arrangements, and evidence from a controlled evaluation. |
| Governance and explainability | Whether the bank can inspect the basis for outcomes, challenge or override them, document changes, and support independent validation and audit. | Relevant model documentation, change records, access arrangements, and validation support. |
| Security and third-party risk | Access controls, data handling, third-party dependencies, incident response, and continuity. | Data-flow and retention details, subprocessor information, access-control arrangements, and incident and continuity procedures. |
| Lifecycle cost and dependency | Implementation, monitoring, customization, maintenance, exit effort, and reliance on vendor-controlled data or components. | A vendor quote and bank-specific estimates, including the costs and practical requirements of migration or exit. |
Regulatory sources do not establish a universal vendor, price, score, preferred model, or performance threshold. Publicly available material cited here also does not establish comparable figures for fraud-loss reduction, accuracy, false-positive rates, or implementation time. Ask candidates for evidence with context rather than treating an unqualified number as a benchmark.
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Validate for the intended decision before relying on it
Plan validation around the system’s actual purpose, data, deployment, and consequences. The bank’s process should assess conceptual soundness and performance for the intended use, implementation and data issues, known limitations, and a plan for monitoring and outcome analysis. Obtain enough vendor evidence and access to do that work. If the vendor restricts access, record what the bank could and could not validate, and determine whether compensating controls or limits on use are needed.
Do not select on a single aggregate accuracy figure. Ask what population was tested, how outcomes and labels were defined, which threshold was used, and over what period. Examine the operational consequences of both false alerts and missed events. The sources do not prescribe one standard set of performance metrics; the bank should define measures that correspond to its use case and decide how to interpret them before comparing claims.
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Assign ownership for decisions and ongoing control
Before deployment, name accountable roles across the business, fraud operations, compliance, model risk, technology, cybersecurity, procurement, and audit. The bank should make clear who approves use, receives vendor change notices, reviews results, investigates alerts, authorizes overrides, manages incidents, and can suspend or limit use.
Maintain an inventory of relevant systems and third parties, and include access and authentication risks in the assessment whether the bank or a third party provides the solution. Interagency guidance describes fraud research, customer service, cybersecurity, and customer reports as useful sources of risk information. It also describes multifactor authentication alongside layered security controls as a mitigation when single-factor authentication is inadequate for the assessed risk. Revisit the bank’s controls when its products, customer types, geographies, channels, or fraud patterns change. The FFIEC manual also says independent testing should review the risk assessment and how it is used in the compliance program.
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