Rules-based automation follows conditions people specify; machine learning (ML) finds statistical patterns in data and uses them to produce an output. Neither approach is universally better. Rules often suit bounded decisions with stable, expressible conditions, while ML may help with complex patterns that are difficult to specify by hand. Financial organizations can also combine the two.
As U.S. Treasury Under Secretary for Domestic Finance Nellie Liang put it in June 2024, “In contrast to rules-based systems, machine learning identifies relationships between variables without explicit instruction or programming.” (U.S. Treasury remarks)
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How rules-based automation and machine learning differ
A rules-based system applies human-authored instructions to defined inputs. A rule might send a transaction for review when it meets specified conditions. The system does not infer those conditions from examples; people decide what to check and how the system should respond.
ML systems use data to estimate patterns or relationships, then apply what they have learned to new cases. The relationships need not be written as individual if-then rules. The result depends on the data, model design, and how the system is put into use.
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In practice, a financial workflow can combine both methods: an ML system can flag a case for review, while explicit rules govern routing or actions that must follow a defined policy.
Where each approach can fit in finance
Rules for defined, stable conditions
Rules can be a natural fit when an organization can state the decision conditions clearly and expects them to remain reasonably stable. They may be used to check whether information meets a specified requirement or to route a case according to an established procedure. The strength of this approach depends on the quality and completeness of the rules and inputs.
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A rule is inspectable as an instruction, but that does not make its assumptions correct. A poorly chosen rule can produce harmful results, and changed circumstances can make an once-useful condition inadequate. Rule sets still need review and version control.
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Machine learning for data-driven patterns
ML may be useful when relevant signals are present in data but are difficult to describe through a manageable set of explicit conditions. Financial applications discussed by the Financial Stability Board (FSB) include credit-quality assessment, insurance pricing, customer interaction, trade execution, compliance, surveillance, data-quality assessment, and fraud detection. These are examples of applications, not evidence that ML outperforms other methods in them. (FSB, 2017)
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A 2024 FSB and OECD roundtable summary reports discussion of potential efficiency gains in risk modeling, trading, claims handling, fraud detection, and financial-crime prevention. It also identifies risks involving models, data protection, governance, privacy, ethics, complexity, and opacity. The summary reflects a roundtable held in May 2024; it is not a controlled measure of how often these applications are used or what results they achieve. (FSB/OECD, 2024)
How to choose between rules and ML
There is no established head-to-head benchmark in the cited publications for accuracy, speed, or cost. Compare approaches against the task and the institution’s ability to validate and govern the consequences.
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| Decision factor | Rules-based automation | Machine learning |
|---|---|---|
| Can the decision be specified? | Fits when important conditions can be written as explicit instructions. | May fit when useful patterns are difficult to specify manually and can be learned from relevant data. |
| What does the approach depend on? | Complete, appropriate rules and reliable inputs. | Relevant and representative data, suitable model design, and monitoring. |
| How do conditions change? | Rules need review when policies, inputs, or circumstances change. | Performance may change as data or conditions change, so monitoring is important. |
| How inspectable is the decision? | The explicit conditions can be inspected, though their assumptions still need scrutiny. | Explainability varies by method; some systems are harder to interpret or audit. |
| What does an error mean? | Assess the downstream effects of applying the rule incorrectly or too broadly. | Assess the consequences of false positives and false negatives, along with model and data risks. |
| What oversight is needed? | Validate rule logic and its downstream effects; control changes to the rules. | Validate data and outputs, monitor performance and outcomes, assess fairness and privacy, and maintain accountable human oversight. |
The more consequential the decision, the more important it is to understand who reviews results, how errors are detected, and what happens when the system fails. The Federal Reserve’s supervisory guidance says model-risk practices should reflect an institution’s size and complexity, a model’s exposure and purpose, and the materiality of its use. (Federal Reserve model-risk guidance)
Why machine learning needs ongoing controls
ML does not become reliable simply because it has learned from data. Institutions need to consider whether the data is appropriate, whether outcomes differ unfairly across groups, whether sensitive information is protected, and whether a model continues to perform as conditions change. Third-party services can add dependencies that organizations must also manage.
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The FSB’s 2017 analysis highlights potential interconnectedness, third-party dependence, interpretability and auditability challenges, privacy, conduct, and cybersecurity risks. A December 2024 analysis by the Bank for International Settlements’ Financial Stability Institute says existing frameworks address many risks, while areas for regulatory attention include governance, expertise, model-risk management, data governance, non-traditional providers, new business models, and third parties. (BIS Financial Stability Institute, December 2024)
These concerns do not mean rules are automatically safe or that every jurisdiction imposes the same AI-specific requirements. Rules also need sound assumptions, testing, review, and accountable ownership. Governance should match the task’s risks and the consequences of mistakes.
What the Federal Reserve guidance does—and does not—say
The Federal Reserve guidance is U.S. banking supervisory guidance, not a universal legal definition of AI or a statement of global requirements. For its model-risk framework, it excludes deterministic rule-based processes and software whose design or use is not underpinned by statistical, economic, or financial theories. It also says the guidance does not set enforceable standards or prescriptive requirements. That boundary does not mean rule-based systems require no controls.
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Start with the simplest approach that can meet the task’s needs and be validated and governed in proportion to its impact. Use explicit rules when conditions are clear and stable. Consider ML when data-driven pattern detection has a justified potential benefit and the organization can manage the associated data, model, fairness, privacy, and monitoring risks. A combined system may make sense when learned signals are useful but actions or safeguards should remain explicitly defined.
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