Busting the Bias Myth: How AI Is Smarter and Fairer Than Rules-Based Systems

Despite ongoing concerns around bias, well-designed AI models often outperform traditional rule-based systems by delivering more objective, fairer, and more effective results.

When it comes to adopting artificial intelligence in financial crime compliance, one myth keeps resurfacing:  “AI is biased and will unfairly target certain groups.”

Let’s break this myth down and look at how advanced systems, especially Cognitive AI, are shifting the paradigm in favor of fairness, transparency, and better risk decisions.

Legacy Systems Are the Real Culprit

Rule-based systems tend to rely on fixed assumptions and oversimplified logic, which can inadvertently embed historical biases. For instance, Customer Risk Assessment models using static rules might label all customers from certain countries or sectors as “high risk” without nuance. Similarly, transaction monitoring systems often trigger alerts purely based on fixed transaction thresholds, ignoring context.

Bias in financial crime compliance isn’t new—and it certainly didn’t begin with AI.
Rules-based systems, long considered the gold standard for AML, rely on static thresholds and hard-coded assumptions. These rules often reflect outdated views of risk that unfairly flag customers based on nationality, transaction volume, or geography, without deeper context.

For example, certain regions may be subject to heightened monitoring simply due to historic risk classifications, regardless of recent behavioural data. These blanket assumptions result in over-reporting, de-risking, and in some cases, financial exclusion—particularly in cross-border payments.

Properly designed AI can help fix these shortcomings. 

How AI changes the game by going beyond limitations

Unlike conventional AI models that rely on static historical data, Cognitive AI dynamically learns from vast, evolving datasets. It identifies anomalies by understanding the context of behaviour, rather than flagging transactions based solely on thresholds or known typologies.

This means fewer unfair flags and more accurate detection of genuinely suspicious activity, even when it doesn’t match established patterns.

Contextual and Customized Risk Scoring

Imagine two companies from the same country both flagged for adverse media. A rule-based system would treat them equally as “high risk”. An AI system, however, can differentiate between a closed, minor civil case and an active criminal investigation, assigning tailored risk scores that better reflect the true threat and enable more precise compliance actions.

Reducing False Positives in Transaction Monitoring

Consider an import/export business that regularly sends payments abroad. In a rule-based system, a sudden payment exceeding $10,000 to a medium-risk country would automatically trigger an alert. However, an AI model trained on the client’s historical transaction data can recognize this as routine behavior, since this client typically processes payments of this amount to the same vendor during regular business hours, thereby avoiding unnecessary alerts.

Continuous Learning Adaptability

Unlike static rules that require manual updates whenever regulations evolve or new criminal patterns emerge, AI models are capable of continuous learning. By analyzing historical outcomes alongside updated risk typologies and regulatory requirements, AI can automatically identify emerging threats and adapt to changing compliance standards, greatly reducing the ongoing effort and complexity involved in rewriting and maintaining rules.

Peer Group Comparison for Improved Detection

Rule-based systems treat every customer identically, ignoring how behavior compares within similar groups or individual history. AI models analyze deviations related to peers and past behavior, improving the accuracy of identifying genuinely suspicious activity.

Transparency and Auditability

Contrary to the “black box” perception, well-crafted AI systems offer  richer explanations than simple rules. For example, instead of labeling a customer with high risk solely due to their location and sector, AI incorporates multiple factors like recent adverse media on a subsidiary balanced against a decade-long clean record with no suspicious activity report resulting in a more balanced, transparent, and defensible risk assessment. 

This level of insight is made possible by leveraging Cognitive AI techniques such as natural language understanding and contextual reasoning. These capabilities enable AI models to analyze unstructured data sources like news articles or regulatory filings and generate human-like explanations that enhance transparency, reduce bias, and improve the overall quality of decision-making.

Key Principles for Building Bias-Resistant AI

To effectively avoid bias, an AI system should be built on diverse and representative

data, carefully designed features that minimize proxies for sensitive attributes, and

regular fairness testing across different customer groups. It must also incorporate

explainability tools to clarify decision factors, maintain human oversight for review and feedback, and ensure transparent documentation and ongoing monitoring to detect and correct any bias that may develop over time.

Real-World Proof: Santander and Clicksend Now

Two recent use cases highlight just how transformative and fair Cognitive AI can be.

• Global Bank Santander deployed ThetaRay’s Cognitive AI for transaction monitoring across its international payments. Instead of relying on pre-defined typologies, ThetaRay’s Cognitive AI flagged suspicious transactions which the bank’s compliance team investigation concluded to be a pattern of transactions potentially tied to a human trafficking ring. ThetaRay’s transaction monitoring wasn’t influenced by location or customer profiles; it simply spotted abnormal flows that separately appeared to be legitimate. But weighing in a variety of risk features, including high volume of advertisement payments to adult services websites, purchase of many SIM cards to cellular providers, many expenses occurring between 22:00 and 7:00, and bulk purchases of women’s underwear and cosmetics, triggered a suspicious activity alert. The investigation uncovered a connected customer, leading to a SAR report being filed. The bank’s detection earned industry recognition for Best Use of Data to Combat Modern Slavery, featured at The Banker podcast, Can AI help banks combat human trafficking?.
  

• African fintech Clicksend Now faced a different challenge: identifying subtle money laundering via card payments. ThetaRay’s Transaction Monitoring Cognitive AI model flagged an unusual pattern—multiple senders making high-value transactions to a single beneficiary. The fintech was able to intervene quickly, stopping illicit activity in its tracks. Again, no rules were breached—but behaviour didn’t fit the norm.
  

In both cases, Cognitive AI didn’t rely on assumptions. It relied on pattern recognition and dynamic learning—proving how bias-aware models can improve outcomes, even in high-risk regions or scenarios.

Closing the Bias Gap: What Compliance Leaders Can Do

If you’re a Chief Risk Officer, Head of AML, or Compliance Lead, bias in AI isn’t something to fear—it’s something to manage proactively. Here’s how:

1. Start with diverse, high-quality data

Ensure your AI models are trained on representative datasets that reflect the full range of customer types, geographies, and behaviors, reducing the risk of skewed outcomes from the outset.

2. Design with fairness in mind 

Avoid using features that act as proxies for sensitive attributes (like nationality, gender, or ethnicity). Instead, ask your vendor to design features that are behavior-based and risk-relevant.

3. Test for fairness regularly

Run ongoing fairness audits across different segments, geographies, customer types, and risk levels to identify and correct any emerging bias in model performance.

4. Implement explainability tools

Ask your vendor to use interpretable AI models or integrate explainability layers that clearly show how decisions are made, especially for high-stakes outputs like customer risk ratings or transaction alerts.

5. Maintain strong human oversight

Ensure that AI outputs are reviewed by trained analysts who can provide context, challenge outcomes, and feedback into the model, keeping decisions accountable and human centered.

6. Document and monitor continuously 

Maintain detailed records of model design, data sources, feature selection, performance testing, bias audits, and updates. This should be governed under a formal Model Risk Management (MRM) framework, which aligns with regulatory expectations for transparency, accountability, and ongoing validation of AI models.

Bias in compliance is real—but the right AI can make things better, not worse. With dynamic learning, contextual understanding, and transparent decision-making, Cognitive AI offers a path toward fairer, smarter, and more inclusive financial crime compliance.

So the next time compliance professionals say “AI is biased,” you can respond with confidence: Not all AI is created equal. It depends on how the AI is trained. The key is to know who’s who in the AI tech vendor crowd.