Feedzai introduced Railgun, its next-generation AI engine designed to target and intercept financial fraud before it can occur.
In order to scale, today’s risk engines force financial institutions to limit the data they use to make risk decisions – typically by looking only at a limited history of data, and by using only a subset of relevant data inputs. Railgun removes these constraints, so financial institutions can now use a complete history of all relevant data – enabling them to assess the likelihood of fraudulent activity with unprecedented accuracy.
As sophisticated criminals embrace new techniques using generative AI, and as financial crime surges, new AI-based fraud-fighting technologies like Railgun will be critical to protect both consumers and financial institutions. Railgun’s breakthrough technology is the result of years of intensive research and development, and the product of multiple Feedzai patents in the area of streaming analytics.
Fraud detection engines typically rely on risk profiles that capture customer information, such as transaction history over specific periods, and assess the likelihood of a fraudulent payment based on a real-time comparison between current transactional activity and past typical behaviors.
In the past, fraud detection suffered from a paucity of data to build these risk profiles, but now our digital age ensures almost limitless information. However, risk engines have struggled to keep up with this change, because storing profiles for numerous customers, cards, and terminals requires massive amounts of low-latency memory space at scale. As a result, current systems simplify data inputs and limit historical context, diminishing fraud detection accuracy in order to achieve scalability.
And because updating risk profiles is an offline process that is time consuming and expensive, these systems are also less agile, taking more time to adapt to rapidly evolving fraud patterns. Altogether, the result is a failure to prevent significant financial crime.
Railgun’s patented technology addresses the compromises inherent in today’s fraud detection systems, delivering:
- Enhanced accuracy – Railgun enables real-time calculations based on data across much longer time windows, providing financial institutions with better observability and understanding of customer behaviors, resulting in more precise detection of suspicious activities.
- Greater agility – Early production results show that Railgun improves the speed of risk strategy updates by 4x or more, enabling swifter responses to emerging fraud threats. New rules become effective almost immediately and risk recalculation no longer imposes a heavy burden on data science teams.
- Scalability and lower latency – With Railgun financial institutions can confidently handle increasing transaction volumes without compromising accuracy or decision latency.
Railgun is the latest innovation from Feedzai, which has invested over $100M to build its RiskOps platform, the world’s most comprehensive suite of solutions to combat financial crime.
Feedzai invests heavily in basic research, and in the last three years has created nearly 100 patents and pending patents in the US and Europe, covering innovations in a wide variety of areas including applied AI and machine learning, fraud detection, streaming data processing and analytics, money laundering detection, rules management, and AI explainability and fairness.
Pedro Barata, CPO at Feedzai said, “As technology continues to evolve and tools such as AI become more readily accessible, fraud detection systems need to be able to keep pace and reliably combat criminal activity. Railgun is a significant weapon in the fight against financial crime, allowing banks and other financial institutions to accurately and cost-effectively turn the tide on the rising levels of fraud. With Railgun, banks no longer have to make assumptions or generalizations when it comes to risk profiling – helping protect millions of people worldwide.”
