AI tackles bank fraud

A new US report looks at how financial institutions can better leverage customer data and advanced machine learning techniques to achieve the twin goals of dealing with fraud and providing a better customer experience.

Time is money when it comes to fighting fraud, according to research from US research house Aite Group, which interviewed 28 senior fraud and data analytics executives at 20 North American banks in August and September. 

“The volume of these attacks continues to increase, since there is very little in the way of adverse consequences such as jail time. Organized crime rings are systematically and methodically targeting the financial services value chain with sophisticated card fraud, application fraud and account takeover attacks," it found.

Moreover, Aite Group found that these threats continue to escalate, just when financial institutions are under intense competitive pressure to make the banking experience easier and frictionless. 

In the face of this seemingly contradictory set of mandates, many banks are turning to machine learning analytics or artificial intelligence to stave off a threat that costs the global banking sector US$67 billion per annum, according to the Association of Certified Fraud Examiners. 
 

Pain points

Sixty-five percent of banks interviewed say the priority for investment in AI analytics for fraud mitigation is very high and is a key area of investment, the survey found.

Another 35 per cent say that the investment priority is moderate; while it’s on the roadmap, other fraud solutions will take priority. Nearly every bank or insurer interviewed included retail account takeover among its top pain points. Application fraud comes in second, with 10 banks saying that was a key pain point, followed by the spectre of faster payments fraud.

Ten per cent of banks interviewed are using AI analytics to help orchestrate authentication today, while another 30 per cent are in the process of implementing an analytically-driven orchestration of authentication.

Eighty per cent of the respondents had in-house data scientists dedicated to the fraud team, although the quantity of resources available to fraud varies widely. Forty percent of the banks interviewed have a machine learning-enabling platform deployed in production, while another 10 per cent have one or more proofs of concept underway.

Twenty per cent say that the deployment of an enabling platform is on their one-to-two-year roadmap, while one in five of the banks interviewed have no plans to build an enabling platform in the next couple years.

“Effective fraud prevention is now a competitive issue for financial institutions,” said Aite Group research director Julie Conroy. “Early adopters of advanced analytics are able to increase their fraud detection, and the associated improvements to the customer experience give them a decided edge over their competitors that lag in these investments."
 

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