Vendors
日本語
search
  2. //
  3. Reports & insights
  4. //
  5. Mitigating Fraud in the AI Age: Understanding the Challenge

Mitigating Fraud in the AI Age: Understanding the Challenge

Create a vendor selection project
Click to express your interest in this report
Indication of coverage against your requirements
A subscription is required to activate this feature. Contact us for more info.
Celent have reviewed this profile and believe it to be accurate.
We are waiting for the vendor to publish their solution profile. Contact us or request the RFX.
Projects allow you to export Registered Vendor details and survey responses for analysis outside of Marsh CND. Please refer to the Marsh CND User Guide for detailed instructions.
Download Registered Vendor Survey responses as PDF
Contact vendor directly with specific questions (ie. pricing, capacity, etc)
11 March 2025
Neil Katkov

Generative AI is providing fraudsters with new tools to perpetrate sophisticated social engineering-based deceptions on a large scale. In response, financial institutions will need to fight fire with fire and leverage more powerful AI-driven tools, including transformer models, to counter this growing threat.

Abstract

Celent estimates that AI was behind roughly 20% of the fraud perpetrated in 2024 and is poised to fuel even more fraud moving forward.

The good news is that AI models for fraud detection continue to advance, leading to higher capture rates. The next opportunity for moving the needle in fraud prevention lies in transformer technology and generative AI. Efforts by banks, card processors and their technology partners to apply generative AI to fraud operations are gaining steam. According to recent Celent research, fraud detection and prevention tops the list of functions where financial institutions are most actively exploring the use of generative AI. At the cutting edge, financial institutions and technology providers are looking to apply transformer models directly to fraud detection.

As machine learning and generative AI deliver ever more powerful solutions, the main stumbling block to better fraud prevention is not in the models themselves, but in the infrastructure they run on. Due to hardware, software, and networking constraints in large banking and payments environments, anti-fraud models typically run on only a fraction of real-time transactions, limiting their ability to detect fraud across their entire exposure.

In 2022 IBM helped solve this problem by introducing an innovative AI accelerator that ran on chips inside the mainframe environment itself. This allowed banks and processors to apply AI inferencing to all transactions in real time, greatly expanding their ability to detect and stop the fraudulent transactions running through their systems.

IBM has recently announced the second-generation of this technology. Moreover, according to IBM, their new AI accelerators can run traditional machine learning models and transformer models together, in real time, enabling financial institutions to fight fire with fire and apply the power of generative AI directly to fraud detection.

Client Access

Related Research

Dimensions: Financial Crime IT Pressures and Priorities 2024
August 2024

IT Spending on Risk Management in Banks: 2024 Edition
July 2024

GenAI-oneers in Risk & Compliance: Cross-Sector Survey and Spotlights
June 2024

Dimensions: Risk & Compliance IT Pressures & Priorities 2024 Edition
May 2024

IT and Operational Spending on Fraud: 2024 Edition
April 2024

Operationalizing Fraud Prevention on IBM z16: Reducing Losses in Banking, Cards, and Payments
April 2022