Machine learning in fraud detection: from rule-based to model-based anomaly analysis

[Professor]

Abstract: The evolution of security systems: from simple IF-THEN rules ("if the purchase is > $1,000, then request confirmation") to complex ensemble ML models that analyze thousands of features and learn from new fraudulent schemes in real time.

Introduction: From a sentry with instructions to a neural network visionary​

Imagine you're guarding a huge, crowded train station. You're given a list of rules: "Detain anyone in a green jacket" or "Stop anyone running." It works at first. But then the bad guys put on blue jackets and learn to walk fast, not run. Your rules were outdated the day they were written.

That's how the first fraud detection systems in banks worked — rule-based. They were like a guard with a list. Today, we have an artificial intelligence system that doesn't just check the rules, but understands context, learns on the fly, and spots anomalies where a human would only notice noise. This evolution from "if-then" to machine learning (ML) is the most significant revolution in financial security in the last decade.

Chapter 1. The Age of Rules: Clarity, Simplicity, and the Achilles' Heel​

How did it work?
Security engineers manually coded logical conditions (rules) based on known fraud patterns and common sense.

• Example rule 1: IF (transaction_amount > $1,000) AND (merchant_country != "USA") THEN request_customer_confirmation
• Example rule 2: IF (number_of_failed_CVVs > 3 in 5 minutes from one IP) THEN block_IP

Advantages:

• Transparency and control: It's easy to understand why a block was triggered.
• Ease of implementation: No complex algorithms required, just a database and a rules engine.
• Effectiveness against known patterns: Catched brute-force, mass attacks well.

Flaws that became fatal:

1. High False Positive Rate: You're a law-abiding citizen who bought a plane ticket for $1,200 from a Spanish airline. Rule 1 is triggered. Your transaction is frozen, and your bank calls you. This annoys customers and costs the bank money.
2. Inability to adapt: Scammers quickly learned the rules and circumvented them. Can't buy for $1,000? Let's buy for $999. Can't make four attempts from a single IP? Let's use a botnet of 1,000 IPs.
3. Failure to take context into account: The rule doesn't know that you're on vacation, that this expensive purchase is your typical New Year's gift, or that you just logged into the app from your personal phone in Moscow.
4. Maintenance effort: Each new fraud scheme required the engineer to turn it into a new rule. It was a race in which the defense always lagged.

The system was reactive and crude. A proactive, adaptive, and nuanced approach was needed.

Chapter 2. How ML Models Work: Learn from Data, Not from Instructions​

Instead of programming rules, machine learning allows the algorithm to automatically identify patterns in historical data.

What is the data for the model?
It consists of thousands of features for each transaction:

• Demographics: Age, gender, client experience.
• Transactional: Amount, currency, time of day, day of week, merchant category (MCC).
• Behavioral: Average customer bill, typical spending categories, purchase geography.
• Session: Data entry speed, IP address, device fingerprint, previous transaction results.
• Network: Behavior of other users from the same IP, device, or BIN range.

During the training phase, models are shown millions of examples: here's a transaction that turns out to be fraudulent (labeled fraud=1), and here's a legitimate one (fraud=0). An algorithm (e.g., gradient boosting or a neural network) searches for complex, nonlinear relationships between thousands of features and the "fraud" label.

The training output is not a list of rules, but a complex mathematical function that, based on new, unseen data, can predict the probability that a transaction is fraudulent.

Chapter 3. Model Evolution: From Decision Trees to Ensembles and Deep Learning​

The complexity and power of models grew along with computing capabilities.

1. Decision trees and random forests: An early but effective step. A decision tree asks a series of questions (Sum > X? Country = Y?). A random forest is a set of such trees "voting" on the final decision. They are better than rules, but still relatively interpretable.
2. Gradient boosting (XGBoost, LightGBM, CatBoost): The gold standard of modern fraud detection systems. It's an ensemble of thousands of simple models (usually trees) that sequentially improve on each other. Each successive model learns from the mistakes of its predecessors. This results in incredible accuracy, the ability to capture complex patterns, and the ability to work with heterogeneous data.
3. Neural Networks (Deep Learning):Used for the most complex tasks, especially with unstructured data:
   • Text analysis (NLP): To detect fraudulent descriptions in translation fields or deception in support chats.
   • Graph Neural Networks: The cutting-edge technology. The model analyzes not individual transactions, but entire networks of connections between clients, accounts, devices, and phones. It sees that clients A, B, and C don't know each other, but they are linked by a single phone number or device from which they all "confirmed" transactions. This allows for the uncovering of complex schemes involving multiple accounts and simulated "drops."

A key feature is unsupervised learning for anomaly detection. Sometimes fraud is so new that there is no labeled data. Then, the models look for outliers — transactions that significantly deviate not only from the behavior of a given client but also from global patterns across all clients. This allows for the discovery of entirely new, unknown patterns.

Chapter 4. Real-time ML: What it looks like for the system and for the client​

The work cycle of a modern ML fraud detection system:

1. Real-time feature collection: When a payment attempt occurs, the system collects and calculates thousands of features for a given transaction in milliseconds.
2. Model run: A transaction is "run" through several models (for example, one assesses risk for a given client, another for a given device, a third looks for anomalies in the network of connections).
3. Calculating a single risk score: Models produce probabilities that are aggregated into a final risk score (e.g., from 0 to 999).
4. Decision making:
   • 0-300 (Low Risk): Frictionless authentication. Payment is instant, without additional confirmation.
   • 301-700 (Medium Risk): Challenge Flow. Request biometrics (Face ID) or confirmation in the bank app.
   • 701-999 (High Risk): Transaction is declined and may be called by security.
5. Continuous Learning: The result of this transaction (whether confirmed by the client or rejected) along with its attributes is fed back into the system. Models are constantly retrained, adapting to new trends. Today's new fraudulent scheme will be factored into the algorithm tomorrow.

For the client this means:

• Convenience: 95% of legitimate purchases go smoothly.
• Security: The system catches complex attacks that are not described in any rule.
• Personalization: The system knows your personal patterns and protects you, not an abstract “bank client”.

Chapter 5. Victories and Horizons: What has changed and where is ML defense heading?​

What ML has already changed:

1. Dramatic reduction in false positives: Thanks to context, legitimate but non-standard customer transactions (large purchases, payments abroad) are no longer automatically blocked.
2. Early detection of new threats: ML models detect unknown patterns based on anomalies, ahead of analysts.
3. Scalability: A single trained model can process millions of transactions per day, replacing thousands of static rules.
4. Proactive protection: The system can block suspicious activity before the fraudster can cause damage.

Where is technology heading?

• Federated Learning: Train models on data from different banks without sharing the data itself, which improves the overall security of the ecosystem.
• Explainable AI (XAI): The development of methods that not only generate a decision but also explain it to humans ("We blocked the transaction because the combination of features X, Y, and Z indicates fraud in 99.7% of cases"). This is critical for regulators and internal audit.
• Predictive analytics: Predicting not a specific fraudulent transaction, but rather the vulnerability of a client or channel to a specific type of attack for proactive measures.

Conclusion: From protection by template to protection by meaning​

The evolution from rule-based to model-based systems is a shift from formal logic to intuitive understanding.

Previously, the system asked, "Is the rule broken?" Today, it asks a much deeper question: "Does what's happening make sense

? Does it fit within the normal life of this person and millions of others?" Machine learning hasn't displaced humans from the security process. It has empowered them, freeing them from the routine of checking false positives and giving them a tool to combat intelligent adversaries. A security analyst no longer writes "if-then" rules, but rather examines complex clusters of anomalies detected by the model, searching for new, even more sophisticated features to train it on.

Ultimately, we — the clients — win. Our finances are no longer protected by a sentry with a memorized phrase, but by a living, learning, and insightful intelligence that knows us and our digital lifestyle well enough to distinguish us from those who are pretending to be us. And it does it so seamlessly that we can simply live our lives, trusting technology to do what it does best – find patterns in chaos.