Jimmy Hennessy has worked in the field of Data Science and Software Engineering for over 18 years. Working across many sectors including medical device and fintech, he has designed, developed and implemented machine learning models to prevent fraud and contribute to revenue growth. Jimmy currently heads up a global Data Science team at ACI Worldwide, a payments industry leader, specialising in the prevention of card fraud across the company’s merchant and bank portfolios. He is an advisory member of the Analytics Institute of Ireland.
Articles by Jimmy Hennessy
Incremental Learning: The Real-Time Hero Taking on Fraud
In the fast-moving world of merchant fraud, we can no longer rely on the past to predict the future. The speed and scale of change is such that traditional machine learning (ML) methods, which analyze historical fraud trends, can’t keep up.
Why Incremental Learning Is a Gamechanger for Payments Fraud
Any innovation that helps merchants outfox fraudsters can’t come soon enough. An innovative, industry-first approach to machine learning, incremental learning represents a step change in fraud prevention. It identifies patterns earlier and more accurately, automating decisions and actions to keep merchants better protected than ever.
Introducing Incremental Learning: An Industry-First Boost for Fraud Prevention
In our previous blog on machine learning, we sought to clarify its role in fraud prevention for merchants. To summarize, it can be an extremely effective way to identify patterns of fraud in a manner and at a speed that humans cannot. It is a critical tool in the fight against fraud, especially when used as part of a multi-layered fraud solution.
Machine Learning: Separating Fact from Fraud Fiction for Merchants
Machine learning is a broad discipline about which many claims, sometimes extravagant, are made. In recent years, it has often been hailed as the most effective answer to stopping payments fraud. At ACI, we’ve been working with machine learning models to prevent fraud for over two decades – and we know they can play a critical role in improving fraud detection accuracy. Here we bring together a few insights on how models can be used most effectively.
