Machine learning for fraud detection: essentials, use cases, and guidelines

Machine learning for fraud detection: essentials, use cases, and guidelines

September 26, 2023

Aleksandr Ahramovich

by Aleksandr Ahramovich,

Head of AI/ML Center of Excellence

Machine learning-based fraud detection systems rely on ML algorithms that can be trained with historical data on past fraudulent or legitimate activities to autonomously identify the characteristic patterns of these events and recognize them once they recur.

Explore the nature, payoffs, and applications of this technology in common fraud scenarios, along with some guidelines to streamline its adoption. Finally, find out how machine learning development experts can help you protect your company and customers from ever-evolving fraud threats.

Table of contents

ML-based vs rule-based fraud detection

ML-enabled anomaly detection applications differ from traditional software in terms of detection technique:

UserTransactionsML modelOngoing model fine-tuningPatterns anomaliesDetectionMachine LearningFraudsterFraudRulesHigher frequencyAddress mismatchOver limitDetectionRule Based

ML-based fraud detection

  • ML solutions autonomously identify and use more complex and variable rules than traditional systems. To do so, ML algorithms process data on past fraud cases, discover patterns and relationships between data points, and build models trained to identify those patterns once they recur in future datasets.
  • ML systems can predict imminent criminal actions by identifying anomalies, namely subtle and unconventional behavioral patterns that humans would probably overlook but that still deviate from the norm, which could be clues to upcoming fraud.
  • ML-powered solutions improve with experience, refining their models over time as they process new data, including unmapped data points. So, if they encounter new fraud scenarios, machine learning-based anomaly detection systems will quickly adapt to such threats, automatically integrating and updating the existing rules without human intervention.
Example
The fraud detection system of an ecommerce platform runs into a suspicious credit card transaction that doesn't fit its user’s behavioral patterns based on multiple subtle parameters, such as the product pages browsed before placing an order.

Rule-based fraud detection

  • Traditional rule-based solutions follow an "if/then" logic and trigger a response when a predefined condition is violated.
  • These tools must be manually instructed to detect fraud cases by compiling libraries with hundreds of rules.
Example
The fraud detection system of a bank flags supposed customers who use credit cards in unusual locations, or increase their payment frequency.

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Technical overview of ML for fraud detection

ML approaches

Do machines need human intervention to learn, or can they just observe the surrounding reality? The main approaches to training machine learning algorithms are supervised, unsupervised, and reinforcement learning, depending on the degree of human involvement and control over the ML training process.

Supervised learning

ML-based fraud detection systems are trained with large amounts of labeled data, previously annotated with certain labels describing its key features. This can be data from legitimate and fraudulent transactions described with "fraud" or "non-fraud" labels, respectively. These labeled datasets, which require rather time-consuming manual tagging, provide the system with both the input (transaction data) and the desired output (groups of classified examples), allowing algorithms to identify which patterns and relationships connect them and apply such findings to classify future cases.

Unsupervised learning

These algorithms are fueled with unlabeled transaction data and have to autonomously group these transactions into different clusters based on their similarities (shared behavioral patterns) and differences (typical vs unusual patterns which can correspond to fraudulent activity). This approach, typically associated with deep learning, is computationally demanding but can be the only choice when facing fraud attempts that have never been met before and therefore unlabelled.

Reinforcement learning

This trial-and-error approach involves multiple training iterations in which the algorithm performs a fraud detection task in different ways several times until it can accurately identify fraudulent and non-fraudulent attempts. Since it does not require labeled inputs, reinforcement learning can be applied without prior knowledge of the current fraud scenario. However, it requires considerable computing power.

Input Raw DataAlgorithmModel TrainingLabeled dataProcessingClassificationOutputSupervised learning