Adaptive fraud detection: Embedding machine learning for intelligent decisioning
Amelia Teh
Senior Data Analytics Consultant, Global Fraud Solutions, GBG
Every fraud team in the financial institutions today faces the same paradox - with more data, controls and monitoring systems than ever before in place, fraud continues to evolve faster. This paradox drives institutions to cast a wide net in their detection systems, often flagging suspicious activity that turns out to be legitimate. As a result, it is not uncommon to see more than 80% fraud alerts end up as false positives.
The modern threat landscape does not just require faster systems or more sophisticated tooling, it requires smarter decisioning or actions that adapt to context and intent.
Rules establish governance, machine learning adds foresight
When the topic of artificial intelligence (AI) or machine learning (ML) arises, one question always follows:
“Should we replace rule-based systems with machine learning?”
It is the wrong question. Fraud detection should not be about choosing between rules or ML, but about how both complement and work together.
Rules bring clarity and control. They encode domain expertise, enforce known fraud scenarios and compliance logic, as well as providing transparency. They set the foundation upon which more intelligent, adaptive layers can be built.
Machine learning, on the other hand, brings adaptability. It learns from behavioural signals, detects subtle shifts, and scales pattern recognition beyond human reach.
ML models can:
- Detect emerging fraud patterns that have not yet been codified
- Score risk at customer, channel or device level, not just per transaction or application
- Learn from verified investigation outcomes, continuously refining its accuracy
The most effective fraud defence today does not choose between the two. It blends them, creating an intelligent decisioning where rules enforce governance and ML provides continuous adaptability.
Adaptive fraud detection workflow with embedded ML
The diagram below shows how an adaptive workflow unfolds in practice:
The goal is not to just have AI or deploy more models, but to create a decisioning workflow with a clear purpose at each layer:
- Rules define control
- ML adds adaptability
- Feedback loops sustain intelligence
The workflow is versatile as it adapts to the bank’s maturity, where it can start with ML enriching alerts and evolve into real-time adaptive decisioning.
Reducing false positives with context
One of the biggest operational wins of embedding ML is reducing false positives.
Rules, by nature, execute on fixed logic, that is “if X and Y, then flag”. But fraud is contextual, not binary.
ML models assess risk relative to personalised behavioural norms, learning what is typical for each customer or device. For instance:
- A $5,000 international transfer may look risky by rule, but ML recognises it as part of a customer’s usual transaction pattern
- A 9:00pm mobile login from a new device may appear safe by rule, but ML notices it is paired with unusual transaction velocity
As a model learns, it updates the personalised norms as a customer’s behavioural patterns evolve. This results in more meaningful alerts, i.e., those more likely to indicate true fraud, reducing review time, improving customer experience, and freeing analysts to focus on more complex investigative tasks.
Detecting risks missed by the rules
As fraudsters become increasingly sophisticated, often studying and adapting to known static rules to avoid detection, it is clear that rules alone aren’t enough. They test limits, change channels, and hide in the grey zones between rule logic. Machine learning fills that gap, not by replacing the rules, but by spotting patterns and anomalies that static rules may overlook.
ML can uncover:
- Subtle behavioural drift but consistent changes that precede fraud
- Connections across customers, devices, and merchants invisible to rule logic
- Behaviours that look legitimate in isolation but suspicious in sequence
These are the hidden patterns that rules cannot capture. ML surfaces them early, and in doing so, enables fraud teams to identify new risk signals they can formalise into future controls.
Learning from every decision
Adaptive systems improve through structured feedback that is based on verified outcomes.
Every case reviewed adds a data point that refines the model’s understanding of genuine versus fraudulent activity. Rules are tuned or retired based on false positive performance. Over time, the system improves precision and recall naturally because it grows more accurate with every validated decision.
The future is adaptive intelligence, not just detection
When rules and ML work in harmony in a layered intelligence framework, institutions can achieve control with agility.
The next phase of fraud mitigation evolution will not be defined by tighter rules or more complex AI/ML models, but by how well institutions orchestrate decisions across people, data and systems.
Rules will continue to define boundaries and accountability, and ML will define context and intent. Together, they transform traditional fraud monitoring to adaptive fraud decisioning, where the system learns alongside the people who manage it.
