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To make modern fraud detection technology more accessible to community banks, Jack Henry & Associates Inc. late Thursday announced plans to launch early next year Financial Crimes Defender, a cloud-native platform for detecting fraud and other suspicious account activity that can hurt consumer’s financial health, as well as financial crimes, such as money laundering.
The platform, which was developed in conjunction with data-science company Feedzai, will leverage data across community and regional financial institutions within Jack Henry’s network. The aim is to gain visibility into fraud across all channels using such technology as artificial intelligence and machine learning to detect fraud in real-time and stop it before criminals can steal the funds.
The platform is also expected to help detect money-laundering schemes. For example, when indicators of an increase in a customer’s Bank Security Act (BSA) risk profile are detected, these indicators can be immediately reviewed for inclusion into the financial institution’s overall transaction-monitoring model.
“In most solutions today, the BSA risk of the institution and its customers does not inform transaction monitoring, which creates siloes and unnecessary investigations on lower-risk clients,” Liz Abbott, managing director of financial crimes at Jack Henry, tells Digital Transactions News by email.
Jack Henry says it developed the platform as part of its cloud-native technology strategy to help community and regional financial institutions offer applications that can differentiate them in the market and better serve their customers’ needs.
So-called cloud-native apps leverage distributed computing offered via a cloud-based delivery model. Unlike cloud-enabled solutions, these apps do not require any computing infrastructure on site.
While there has been significant progress in fraud and suspicious-activity detection in recent years, a lack of data and high costs often preclude many community banks from leveraging these technological advances, Abbott adds.
“One of the many advantages of using machine-learning models for fraud detection is the amount of data that a model can leverage for accuracy. Financial Crimes Defender will use streaming data from a variety of sources in real time, including digital providers, for fraud detection within the models,” Abbott says. “For example, a would-be fraudster changes a password on an account prior to an abnormal withdrawal. Having the information that a password was changed would indicate a higher likelihood of fraud in this scenario.”
The need for financial institutions to keep pace with fraud-prevention technology has become increasingly important as fraud attacks have intensified with the increased use of digital banking and faster payment rails, Sarah Grotta, director, debit and alternative products for Mercator Advisory Group, says by email. “As a result, speed of detection is paramount,” she adds.
Consumers’ expectations that their financial institution will protect them from fraud have increased significantly in the past couple of years. “Fraud mitigation is table stakes—this is a baseline expectation consumers have for their financial institution relationships,” says Grotta. “We have conducted numerous consumer surveys over the years that show that when these expectations are not met, that often results in the consumer or small business choosing to take their business elsewhere.”
Effective fraud controls are also an enabler of innovation that delivers the digital experiences consumers expect, such as peer-to-peer payments and online wires. “There must be a strong fraud-control framework in place to protect those higher risk transactions,” Grotta adds.
