Using adaptive AI to stop hackers

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Posted on: May 8, 2020 5:58 pm EDT

AI can protect gamers and others accustomed to making online payments

Artificial intelligence (AI) offers a broad range of possibilities to enhance every aspect of society with its amazing capacity to learn from data and experiences. Regarding the functions of AI in the financial sectors, there are forms of AI that can be adapted to detect patterns of fraudulent behavior. This is achieved through using the AI software to learn from the behavior of known legitimate deposit account holders, which will make hackers stand out when they make an irregular move. This adaptive way to use AI to stop fraud has been introduced by the new FI Fraud Decisioning Playbook, with the collaboration of PYMNTS and Simility, and can be especially beneficial to online gamers and others who routinely make payments over the Internet.

This innovative FI Fraud Decisioning Playbook is able to notice asymmetries between good customers and the sneaky cyberthieves; both groups have a different data footprint that the AI software can identify. “Fraud decisioning strategies are more effective when the data gathered and analyzed includes high-quality evaluations of legitimate customers,” the playbook states. “These customers build long digital transaction histories across online and mobile platforms as well as through purchases made in stores with card and app payments. Artificial intelligence (AI)- and machine learning (ML)-powered tools can analyze this data to better determine legitimate users’ behaviors, and these advanced technologies can often catch small behavioral details that may elude even the most-talented human analysts. A clear fraud decisioning framework with robust data gathering and analysis can power FIs, protecting them from losing billions to fraud and helping them gain customers’ trust.”

This tool brings a new way of understanding customers’ patterns in a way that can favor the process of Know Your Customer (KYC) requirements, which are mandated by law. Any deviation from those set patterns will work as the predictor of intrusion.