AI and ML in Fraud Management: Key Trends, Perspectives, and Challenges
Description
AI and ML in Fraud Management: Key Trends, Perspectives, and Challenges
This IDC Perspective addresses how the deployment of AI- and machine learning (ML)–enabled fraud prevention systems brings benefits to banks' fraud management operations, optimizing the operational efficiency of the fraud detection effort. By adopting ML fraud prevention systems, banks can protect their customers from fraud, meeting their expectations for a frictionless customer experience at cash-out.Banks can leverage the overwhelming amount of data at speed and scale, thanks to AI and ML's ability to process huge data sets and to enable real-time data-driven decision making with actionable insights. Furthermore, machine learning is dynamic, and it is possible to leverage and fine-tune the models over time and to swiftly adjust to changing scenarios.The other advantage is the optimization of fraud analysts' time, which will be able to dedicate more attention to real suspicious transactions, without having to investigate too many false positive transactions, and to provide a better service in addressing the customers' claims."Banks adopting ML fraud prevention system enhance the customer experience and the consumer trust in electronic payments. Furthermore, the banks' involvement in voluntary info-sharing arrangements will enrich the available data feeds to be processed by ML-enabled fraud prevention solutions and will lead to an enhanced ability to discover new fraud patterns to the overall benefit of each individual customer," says Maria Adele Di Comite, research director, IDC Financial Insights Corporate and Retail Banking.
Please Note: Extended description available upon request.
This IDC Perspective addresses how the deployment of AI- and machine learning (ML)–enabled fraud prevention systems brings benefits to banks' fraud management operations, optimizing the operational efficiency of the fraud detection effort. By adopting ML fraud prevention systems, banks can protect their customers from fraud, meeting their expectations for a frictionless customer experience at cash-out.Banks can leverage the overwhelming amount of data at speed and scale, thanks to AI and ML's ability to process huge data sets and to enable real-time data-driven decision making with actionable insights. Furthermore, machine learning is dynamic, and it is possible to leverage and fine-tune the models over time and to swiftly adjust to changing scenarios.The other advantage is the optimization of fraud analysts' time, which will be able to dedicate more attention to real suspicious transactions, without having to investigate too many false positive transactions, and to provide a better service in addressing the customers' claims."Banks adopting ML fraud prevention system enhance the customer experience and the consumer trust in electronic payments. Furthermore, the banks' involvement in voluntary info-sharing arrangements will enrich the available data feeds to be processed by ML-enabled fraud prevention solutions and will lead to an enhanced ability to discover new fraud patterns to the overall benefit of each individual customer," says Maria Adele Di Comite, research director, IDC Financial Insights Corporate and Retail Banking.
Please Note: Extended description available upon request.
Table of Contents
9 Pages
- Executive Snapshot
- Situation Overview
- Overview
- Customer Needs: End Users
- Customer Needs: Merchants and Commercial Firms
- Banks Needs
- Evolving Technologies and Operational Models: Fraud Prevention
- AI and ML Open Challenges
- Advice for the Technology Buyer
- Learn More
- Synopsis
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