Fraud & AML Machine Learning Platforms: Financial Crime Detection’s Next Frontier

The tide of financial crime is rising. As FIs seek to transform their legacy control frameworks, adoption of machine learning platforms will increase in parallel. 

Boston, August 19, 2021 – The impact that financial crime has on the financial services industry continues to expand and with it, the pressure to find innovative strategies and solutions for striking a more optimal balance among loss reduction, client experience, operating efficiency, and regulatory compliance. Among the most significant developments of this evolving market are the emergence of fraud and AML detection solutions that provide FIs with the capability to optimize the performance of their controls by way of applying advanced analytical techniques to discover, develop, test, deploy, and tune highly customized detection logic and policy administration.

This Impact Report explores the evolving market for these fraud and AML detection solutions, as well as the factors that FIs should consider in their pursuit of transforming their control frameworks. Information to produce this report was collected through product demonstrations, vendor surveys, and interviews with financial crime executives at FIs across the globe, along with additional desk research. 

This 52-page Impact Report contains 15 figures and nine tables. Clients of Aite-Novarica Group’s Fraud & AML service can download this report, the corresponding charts, and the Executive Impact Deck.

This report mentions ACI Worldwide, Acuant, Symphony AyasdiAI, BAE Systems, Bleckwen, Bottomline Technologies, Brighterion, DataVisor, Featurespace, Feedzai, FICO, GB Group (GBG), Genpact, IBM, Inform GmbH, iSoft, LexisNexis Risk Solutions, NetGuardians, NICE Actimize, Oracle, Pelican, PwC, Quantexa, Risk Ident, SAS, Simility, ThetaRay, TigerGraph, Tookitaki, and Verafin.

Related Content

Get Summary Report

"*" indicates required fields

This field is for validation purposes and should be left unchanged.