More authentication and identity tech needed with fraud expected to increase – Help Net Security


The proliferation of real-time payments platforms, including person-to-person (P2P) transfers and mobile payment platforms across Asia Pacific, has increased fraud losses for the majority of banks.

identity tech

FICO recently conducted a survey with banks in the region and found that 4 out of 5 (78 percent) have seen their fraud losses increase.

Further to this, almost a quarter (22 percent) say that fraud will rise significantly in the next 12 months, with an additional 58 percent saying they expect a moderate rise in fraud.

“While the convenience of real-time payments is great news for customers, increasingly, banks have zero time to clear a transaction or payment. AI can’t slow down the clock, but it can help create systems that are radically quicker to recognize a transaction that smells likely to be fraudulent,” said Dan McConaghy, president of FICO in Asia Pacific.

“Banks will need to move beyond passwords and OTPs and add biometrics, device telemetry and customer behavior analytics to keep up with the changing payments landscape.”

Authentication and identity tech

When asked which identity and authentication strategies they used, the majority of APAC banks have a strategy of multi-factor authentication (84 percent). They increasingly use a wide range of authentication methods including: biometrics (64 percent), normal passwords (62 percent) and in last place behavioral authentication (38 percent).

Interestingly, nearly half of the respondents (46 percent) are currently only using 1 or 2 of these strategies, potentially leaving them more exposed to attack vectors such as identity theft, account takeovers, cyberattacks.

“Why try to crack a safe when you can walk in the front door?” explained McConaghy.

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“Criminals are trying to fool banks into thinking they are new customers or stealing account access by tricking people into making security mistakes or giving away sensitive information. When they are successful, criminals are making use of real-time payments to move funds quickly through a maze of global accounts.”

The survey bore this out with 40 percent of banks naming social engineering as the number one fraud concern when it comes to real-time payments. Account takeovers were ranked second, with false accounts and money mules also rated as problems.

New forms of biometric, multi-factor and behavioral technologies allow banks to stop payments being made, even if an account appears to be using the correct but stolen password or entering the right, but intercepted, one-time-password.

“Beyond this type of account take over, we also have authorized push payment fraud, such as when a customer is tricked into paying what they think is a legitimate invoice like a fake school bill or payment to a tradesperson,” said McConaghy.

“This type of social engineering is harder to stop but better KYC, link analysis to find money mule accounts and behavioral analytics to flag new accounts for a regular payee, are all examples of how to tackle it.”

Mitigating criminal behavior

Further to stopping fraud in real-time payment platforms, crimes such as drug trafficking, human smuggling, tax evasion and terrorism finance are also attracted to the irrevocable nature of instant payments.

The lack of visibility between jurisdictions has seen regulators encouraging banks to move quickly in this cross-border payments space to ensure payments are compliant and secure.

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In terms of mitigating this criminal behavior, more than 90 percent of APAC banks surveyed thought that convergence between their fraud and compliance functions would be helpful in defending transactions on real-time payments platforms.

“We estimate that there is about an 80 percent overlap in software functionality between legacy fraud and anti-money laundering systems,” added McConaghy.

“To tackle fraud and money laundering schemes that exploit real-time money movement you need to leverage all the available technologies, automate as much as you can and introduce models that can identify outlier transactions and customer behavior so your teams can spend their time investigating the riskiest of the red flags.”



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