Fraud Detection with Machine Learning
Overview
Fraud is a billion-dollar business, and it is increasing every year. The banking, insurance, and healthcare sectors suffer the most. Traditional methods of data analysis have long been used to identify fraudulent users. It requires complex and time-consuming investigations.
Taking advantage of recent technology advances, we use algorithms and strong Data Science expertise to help fraud analysts detect anomalies and patterns in transactions.
Challenge
Our client was one of the leading payment processing companies. Fraud identification was carried out manually, based on rules written and defined by fraud analysts. Because fraud is an adversary system, these rules had to be updated regularly, as fraudsters work 24/7 to find ways to keep performing undetected fraudulent transactions.
Lisbon,
Portugal
Opportunity
EAI was responsible to build, test and deploy machine learning algorithms to predict the risk of fraud for a high throughput of transactions. The proposed solution should enable continuous monitoring of financial transactions, allowing the company to take real-time measures in each case.