The challenge of fighting fraud is that fraudsters are often intelligent, learn from their mistakes, and continuously create new workarounds. For this reason, machine learning is more important than ever for fraud prevention – we need techniques that can capture known fraud patterns as well as new types of fraud.
Here you will learn how to leverage machine learning for more effective and efficient fraud and anomaly detection.
Fraud and Anomaly Detection Using Machine Learning
A first step is to embed domain expertise and known fraud patterns into entity features and fraud risk scores that can be automatically computed and used to systematically and automatically rank suspects. In a second and more sophisticated step, supervised machine learning (classification and association learners) can be used to learn detection models for known types of fraud. These models can then be deployed to automatically identify new instances, cases of known fraud patterns, and types of fraud in the future.
In order to also detect potential new fraud patterns and types, we leverage unsupervised machine learning (for example, anomaly detection and outlier identification techniques). Machine learning and predictive analytics can be used to not only identify fraud after it has already happened, but to also prevent it from occurring.
An Example of Fraud Detection
A good example of this is predicting expected treatments, medications, quantities, volumes, and costs and comparing them with the actual transactions and requested payments. In this case, the health insurance can decide to postpone or deny payments, if transactions or combinations of transactions seem highly suspicious. Then, they can perform investigations before processing payments.
Overall, machine learning can help auditors and fraud busters to focus on high risk cases instead of wasting time with random checks, integrate and consider various data sources, create meaningful features and scores, provide context and explanations, detect networks of fraudsters, and assist the auditors and fraud busters. All of this leads to more effective and efficient fraud and anomaly detection.