Every year, organizations in the financial services and other sectors lose millions of dollars to fraudulent activities. With widespread digitization and technology adoption in these sectors, the complexity and types of fraudulent behavior have also evolved, and left unchecked these crimes can have far-reaching effects on not just business revenues, but also on the brand reputation of impacted organizations.
The increased risk of fraud and tightening of regulatory requirements around fraud detection have prompted organizations to become more diligent in identifying suspicious behavior that deviates from regular usage patterns. However, conventional fraud prevention systems are still based on static rules and require significant manual involvement to identify fraudulent behavior.
The resulting process is slow and yields a high degree of false positives (non-fraudulent actions tagged as fraudulent). As one can imagine, blocking innocent transactions or wrongly alerting customers about fraudulent activities from their account is bad for business, and consequently, organizations are allocating significant resources to refine their fraud detection solutions.
With AI and Machine Learning, organizations can build accurate, sophisticated fraud detection solutions that can automate existing processes and make it easier to identify suspicious activities. By reducing manual involvement, businesses can also cover a larger spectrum of transactions for more thorough fraud detection.
Amazon SageMaker is a machine learning service that organizations can use to build, train, and deploy ML solutions for different use cases. In this article, we will showcase the development of a fairly simple, but highly accurate, Fraud Detection model using Amazon SageMaker. To illustrate the process, we have also included snippets from the Jupyter notebook used for this model.