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Fraud detection’s slow speed and silos can’t keep up with emerging threats

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There is no doubt fraud prevention has become more important than ever before. With cyber and e-commerce attacks at an all-time high, merchants and banks must protect themselves and their customers.

The cost of fraud for U.S. financial services companies has risen by an average of 9.3% from 2017 to 2018. For every dollar of fraud, financial services companies now spend $2.92, compared with $2.67 one year ago.

Institutions are often told of the need for a multilayered risk strategy, but the truth is that the benefits of those strategies are being stripped away by the siloed organizational structure of risk prevention organizations.

By defining an approach that sees a holistic view of each layer as interweaving threads managed by easy to use self-service UIs, the latest fraud and risk management solutions are fighting back.
With suites of self-service neural technologies available as cloud services, users can create multiple neural models and multiple rule sets for true omnichannel coverage. This enables fraud teams to respond quickly to threats by building targeted models in real time.

Using machine learning to analyze payment data, the latest technology automates workflow and decisions, making fraud detection quicker and more efficient. This gives banks, merchants and payment provider greater flexibility and control so they can launch new models quickly, recognizing new patterns of fraud and optimizing alert levels to match their review capacity.

The leading fraud and risk management solutions empower users to build their own neural models, shrinking model development and deployment time frames from as long as six months to less than a single day. Case studies show this sort of approach can also slash associated operating costs by 70%, by a reduction in rule set size, complexity and the number of false fraud alerts generated.

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