Problem Statement
Fraud Detection
Today, fraudulent banking transactions happen pretty frequently. However, it is impractical (in terms of cost and efficiency) to look into every transaction for fraud, which results in subpar customer service.
PS Number: PSAIML004
Domain Bucket: Artificial Intelligence
Category: Software
Dataset : NA
As one may train machine learning models to highlight transactions that seem fraudulent based on particular features, such fraud detection using machine learning can help banks and financial organisations save money on disputes/chargebacks.
Background of the Problem
Fraudulent banking transactions are quite a common occurrence today. However, it is not feasible (in terms of cost involved and efficiency) to investigate every transaction for fraud, translating to a poor customer service experience.
Objective
It allows you to deploy resources in an area where you will see the greatest return on your investigative investment. Further, it also helps you optimise customer satisfaction by protecting their accounts and not challenging valid transactions. Such fraud detection using machine learning can help banks and financial organisations save money on disputes/chargebacks as one can train Machine Learning models to flag transactions that appear fraudulent based on specific characteristics.
Summary
Machine Learning in finance can automatically build super-accurate predictive maintenance models to identify and prioritise all kinds of possible fraudulent activities. Businesses can then create a data-based queue and investigate the high priority incidents.