Rules-based automation follows conditions people specify; machine learning (ML) learns statistical patterns from data. In finance, rules can suit bounded decisions with stable, clearly stated conditions, while ML can help find patterns that are difficult to express manually. Neither is universally better: the right choice depends on the task, the data, the cost of errors, and whether the institution can validate and 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.” Read the remarks.
How rules-based automation and machine learning differ
Rules-based automation applies explicit conditions
A rules-based system executes conditions written by people against defined inputs. For example, a process might flag a transaction when specified criteria are met. Its behavior follows the logic encoded in those rules; the system does not learn a new relationship from examples unless a separate learning component is added.
Liang described such systems as solving problems “using specific rules applied to a defined set of variables.” Her June 2024 remarks contrast that approach with ML.
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Machine learning estimates patterns from data
ML uses data to estimate relationships or patterns and applies what it has learned to new cases. People still design, train, test, and deploy the system, but they do not have to write an explicit rule for every relationship the model uses. Different ML methods can be more or less explainable; “machine learning” does not describe one uniform model type.
Financial systems can combine both
A financial workflow can use explicit rules alongside an ML model—for example, applying fixed eligibility or control checks while using a model to identify patterns in data. The approaches are not mutually exclusive, and the combination still needs controls appropriate to its purpose and impact.
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Where financial organizations use these approaches
The examples below describe applications discussed by institutional sources, not evidence that one approach outperforms another or that every organization uses it.
| Financial activity | Examples discussed in the sources |
|---|---|
| Risk and credit | Credit-quality assessment, risk modeling, and capital optimization. The Financial Stability Board (FSB) discussed credit-quality assessment and capital optimization in its 2017 report; a 2024 FSB/OECD roundtable summary noted risk modeling. |
| Insurance | Insurance pricing and marketing, and claims handling. The FSB discussed pricing and marketing in 2017; the FSB/OECD summary noted claims handling in 2024. |
| Payments, fraud, and financial crime | Fraud detection and financial-crime prevention, among other payment-related applications. The FSB discussed fraud detection in 2017; the FSB/OECD summary noted fraud detection and financial-crime prevention in 2024. |
| Trading and asset management | Trade execution and model back-testing, as discussed by the FSB in 2017; the FSB/OECD summary noted trading in 2024. |
| Customer service and operations | Customer interaction, regulatory compliance, surveillance, and data-quality assessment, discussed in the FSB’s 2017 report. |
The FSB’s 2017 report surveys a range of possible financial applications. The FSB/OECD summary published in 2024 reports topics discussed at a May 2024 roundtable; it is not a controlled survey of adoption or outcomes. The Bank for International Settlements (BIS) paper considers financial intermediation, insurance, asset management, and payments, and describes AI developments from rule-based systems through ML to generative AI.
How to choose between rules and ML
There is no established head-to-head benchmark here for accuracy, cost, or speed. Compare the approaches against the specific task rather than assuming that one is categorically more accurate, cheaper, faster, safer, or easier to explain.
- Can the decision conditions be written down? If the task has stable, specifiable conditions, explicit rules may be a natural fit. If useful signals are complex or difficult to specify manually, ML may be worth evaluating.
- What data is available? A rules-based process depends on the quality and completeness of its rules and inputs. ML depends on relevant, representative data, appropriate model design, and ongoing monitoring.
- How often do conditions or data change? A rule set can miss changed patterns if it is not reviewed. ML performance can also change as data or conditions shift, so it needs monitoring and reassessment.
- What are the costs of different errors? Consider the consequences of false positives and false negatives for the affected people, the institution, and the process. The more material the potential harm, the more care validation and oversight require.
- What explanation does the decision need? Rule conditions may be directly inspectable, but that alone does not establish that the outcome is sound. ML explainability varies by method; assess whether the system can support the explanations and review the use case requires.
- Can the organization govern the system? Consider validation, monitoring, input-data governance, fairness and privacy assessment, third-party dependencies, and accountable human oversight in proportion to the system’s risk.
A practical decision rule is to use the simplest approach that meets the task’s needs and can be validated and governed for its impact. Consider ML when data-driven pattern detection offers a justified benefit and the organization can manage the accompanying risks.
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What risks and controls matter for ML in finance?
ML can support information processing, data analysis, pattern recognition, and prediction, but its use also raises governance and risk questions. The BIS analysis discusses privacy, discrimination, market concentration, and interconnectedness. The FSB’s 2017 report highlights interpretability and auditability, privacy, conduct, cybersecurity, and third-party dependencies. The 2024 FSB/OECD summary also flags model risk, data protection, governance, privacy, ethics, opacity, complexity, and potential financial-stability implications.
- Validate and monitor: Test whether the system is fit for its intended purpose, then monitor performance and outcomes as data and conditions change.
- Govern data: Assess whether inputs are appropriate and representative, and address privacy and data-protection concerns.
- Assess fairness and effects: Examine whether decisions create discriminatory or otherwise harmful outcomes, especially where decisions affect access to financial services.
- Manage dependencies: Understand the role of external providers and the risks associated with third-party systems and services.
- Assign accountable oversight: Make clear who reviews the system and responds when it fails, changes, or produces concerning outcomes.
Rules-based automation is not automatically safe. Explicit rules can encode poor assumptions, omit relevant cases, or fail to reflect changed conditions. Rule logic and its downstream effects need review, version control, and governance too.
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What the U.S. Federal Reserve model guidance does—and does not—cover
The Federal Reserve’s supervisory guidance offers a useful but specifically U.S. banking distinction. For the guidance’s model-risk approach, deterministic rule-based processes and software without statistical, economic, or financial theories underpinning their design or use are excluded from its model definition. That boundary is not a universal legal definition, and it does not mean rules require no controls.
The guidance says model-risk management should reflect factors such as an institution’s risk profile, size and complexity, model exposure, purpose, and materiality. It describes model risk as the potential for adverse financial consequences from decisions based on model outputs. The guidance itself says it does not set enforceable standards or prescriptive requirements. Read Federal Reserve SR 11-7.
Regulatory expectations are not identical across jurisdictions. A BIS Financial Stability Institute analysis from December 2024 says existing frameworks address many risks while identifying areas that may need regulatory attention, including governance, expertise, model-risk management, data governance, non-traditional players, new business models, and third-party providers. This is not a statement that every jurisdiction has the same AI-specific law or supervisory expectations.
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