8/7/2018 2:04:27 PM
Machine learning for the banking industry helps reduce criminal risk
Financial Solutions,Machine Learning,Fraud Detection
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App Developer Magazine

Machine learning for the banking industry helps reduce criminal risk



Richard Harris Richard Harris in Artificial Intelligence Tuesday, August 7, 2018
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Machine learning algorithm for banks to detect criminal activity has been developed by Mindtree to reduce the risks of the banking industry.

Mindtree is using artificial intelligence and machine learning technology to help banks improve their ability to detect financial crimes and enhance reconciliation management. These service offerings are made possible through a partnership with Tookitaki's machine-learning-powered platform.

Banks and other financial institutions are challenged by both the rising sophistication of financial crimes worldwide and increasingly complex regulations requiring strict operating and reporting standards. The ongoing efforts to manually detect money laundering, dealing with false alarms and fragmented reconciliation processes are costly and time-consuming. There is an urgent need for these institutions to automate many of these processes, reducing errors and accelerating their response times to incidents.

Mindtree and Tookitaki services now include:

  • Smart Alert Management: A completely automated, dynamically-adaptive model based on artificial intelligence and machine learning technology to detect suspicious cases more accurately. It reduces false alerts, increases true positives (suspicious cases missed by rules/legacy systems), lowers costs, and enhances the productivity of analysts. Banks can improve the anti-money laundering process using machine learning.
     
  • Smart Reconciliation Management: An end-to-end automated approach to reconciliation management across business functions. Using machine learning and analytics, it increases match rates, resolves exceptions, recommends adjustment amounts and generates an audit trail for thorough business understanding. This shifts reconciliation from being subjective and error-prone to objective and more accurate. Banks can automatically handle exceptions and correct source systems while staying compliant.

"There is a compelling need for banks today to automate many traditionally manual, intensive, error-prone tasks," said Kamran Ozair, Executive Vice President and Head of Banking, Financial Services and Insurance at Mindtree. "This partnership combines Tookitaki's predictive modeling capabilities and Mindtree's deep expertise in helping enterprise clients capitalize on artificial intelligence and machine learning to help banks run their business more efficiently."

"Rapid development in artificial intelligence and robotics technologies has brought in massive adoption of automated technologies across industries," says Abhishek Chatterjee, Founder, and CEO of Tookitaki. "For banks especially, who are dealing with strict regulations and little room for error, automation can drive quality, productivity, and profitability. Our partnership with Mindtree has made it easier and more efficient for customers in the financial services industry to introduce artificial intelligence and machine learning capabilities into the critical space of regulatory compliance."



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