Rules-based automation follows conditions people specify; machine learning (ML) learns statistical patterns from data. In finance, rules often suit stable decisions that can be stated clearly, while ML can help find patterns that are difficult to describe in advance. Neither is universally better: the right choice depends on the task, the data, the consequences of errors and the organization’s ability to govern the system.
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.” Her remarks also describe rules-based systems as applying “specific rules to a defined set of variables.”
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How rules-based automation and machine learning differ
A rules-based system applies explicit, human-authored logic to defined inputs. For example, a payment workflow might flag a transaction when a specified condition is met. The system follows the rule; it does not infer a new relationship from examples.
An ML system uses data to estimate patterns or relationships, then applies what it learned to new cases. The relationships need not be individually written as rules, but the result depends on the data, model design and how the model is used. Some financial systems combine approaches: rules can set boundaries or handle straightforward cases while an ML model scores more complex ones.
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These are different ways to produce outputs, not a ranking of quality. The cited institutional publications describe applications and governance concerns, but do not establish controlled, head-to-head results for accuracy, cost or speed.
Where the approaches appear in finance
Examples below show where these methods may be applied; inclusion does not establish that one method performs better or is suitable in every case.
Rules-based applications
Explicit conditions can be used in bounded workflows such as eligibility checks, compliance procedures or transaction handling when the relevant conditions can be specified. The Federal Reserve’s U.S. banking supervisory guidance distinguishes deterministic rule-based processes from models within the scope of its model-risk definition, subject to the qualifications discussed below.
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Machine-learning applications
Financial-sector publications discuss ML and other AI methods in credit assessment, insurance, customer interaction, trading, claims handling, risk modeling, fraud detection, financial-crime prevention, regulatory compliance, surveillance, data-quality assessment and payments. The Financial Stability Board’s 2017 report covers a range of these uses; a September 2024 FSB/OECD summary describes topics discussed at a May 2024 roundtable, including risk modeling, trading, claims, fraud and financial-crime prevention. These are reported areas of use and discussion, not measured evidence of adoption rates or superior outcomes.
How to choose between rules and ML
Assess the task and its consequences before choosing a technique. The following comparison is a practical synthesis of the cited definitions and governance discussions, not a performance benchmark.
| Decision factor | Rules-based automation | Machine learning |
|---|---|---|
| How it reaches an output | Executes specified conditions against defined inputs. | Applies relationships estimated from data. |
| Natural fit | Stable decisions whose conditions can be written down. | Pattern-heavy tasks where useful signals may be hard to specify manually. |
| Main dependency | Accurate, complete rules and inputs. | Relevant, representative data, suitable model design and ongoing monitoring. |
| Inspecting changes | The conditions may be directly inspectable; the rule set still needs review and version control. | Explainability varies by method, and outcomes may change as data or conditions change. |
| Oversight focus | Validate the logic and its downstream effects. | Validate inputs and outputs, monitor performance and drift, assess fairness, and retain accountable oversight. |
Can the decision be stated explicitly?
If people can define stable conditions and the system’s job is to apply them consistently, rules may be the simpler fit. If relevant signals interact in ways that are difficult to express as a manageable set of conditions, ML may be worth evaluating—provided useful data and adequate controls are available.
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What happens when the system is wrong?
Consider both false positives and false negatives. A fraud alert that blocks a legitimate payment and a missed fraudulent payment have different costs; a mistaken credit decision can have material consequences for an applicant and a lender. The acceptable trade-off depends on the use case. Test outcomes against that context rather than assuming a model or rule is safe because its logic is understandable.
How often do the inputs or conditions change?
A rule set can become outdated when business conditions or relevant patterns shift, so it needs review. ML also needs monitoring: changes in data or circumstances can affect performance. Neither approach is maintenance-free.
What explanation and evidence are needed?
Rules may make it easier to inspect which condition led to an output, but inspectability does not guarantee that the condition is sound or that the result is fair. ML explanations vary by method; teams should establish how they will validate results and explain consequential decisions before deployment.
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Risks and controls for financial ML
ML can support information processing, pattern recognition and prediction, but financial-sector analyses also identify concerns around model risk, data protection, privacy, discrimination, opacity, complexity, ethics and governance. At a wider system level, the Financial Stability Board has highlighted potential interconnectedness, concentration, third-party dependencies and cybersecurity concerns. These are risk areas to assess, not claims that every deployment will cause harm.
- Data quality and representation: Check whether inputs are accurate, relevant and representative of the cases the system will encounter. Assess privacy and data-protection obligations.
- Model performance: Validate the model for its intended use, then monitor outcomes and changes over time. A successful initial evaluation does not establish continuing performance.
- Fairness and conduct: Examine how errors and outcomes affect different groups and customers. Set escalation and review processes for consequential cases.
- Explainability and auditability: Keep records that allow appropriate reviewers to understand the system’s role, inputs, limitations and changes.
- Third parties and system-wide exposure: Assess dependencies on external providers and consider how similar systems or shared providers could create concentration or interconnectedness risks.
- Accountability: Assign people responsibility for oversight and decisions. Automation does not remove institutional responsibility for how outputs are used.
Rules-based systems also require governance. A clearly written rule can encode a bad assumption, omit an important case or remain in place after conditions change. Review the logic, inputs and effects rather than treating rules as inherently safe.
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What U.S. Federal Reserve guidance says—and does not say
The Federal Reserve’s supervisory guidance on model risk is specific to U.S. banking supervision, not a universal legal definition of AI or a statement of rules-based systems’ safety. For purposes of that guidance, deterministic rule-based processes and software whose design or use is not based on statistical, economic or financial theories are excluded from its model definition. The guidance says its model-risk approach should reflect factors such as an organization’s size and complexity, the model’s risk profile, exposure, purpose and materiality. It describes model risk as the possibility of adverse financial consequences from decisions based on model outputs.
The guidance also states that it does not establish enforceable standards or prescriptive requirements. Its exclusion of certain deterministic systems from that model definition does not mean those systems need no controls. Requirements and supervisory expectations may differ across jurisdictions and institutions.
A practical decision rule
Start with the simplest approach that meets the task’s needs and can be validated and governed in proportion to its impact. Use explicit rules when conditions are stable and can be specified clearly. Consider ML when data-driven pattern detection offers a justified benefit that rules cannot readily provide—and only when the organization can manage the associated data, model, fairness, privacy, monitoring and oversight risks. This is a practical decision principle, not a regulatory mandate.
The Bank for International Settlements’ December 2024 analysis says existing frameworks address many AI-related risks while identifying areas that may need regulatory attention, including governance, expertise, model-risk and data governance, new business models and third-party providers. That analysis reinforces the need to evaluate the institution’s setting rather than assume one approach or one jurisdiction’s rules fit every financial use.
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