Early Detection with Rippleshot can Stop 60% of Potential Fraud Losses

Machine learning and artificial intelligence offer credit unions a robust solution to fight fraud.

11/5/2019

Hooded man hacking into a computer.

Data breaches and emerging fraud tactics are ramping up to new levels, placing the personal, identifiable information of credit union members at greater risk than ever before. Relying on traditional network alerts can be costly in terms of fraud loss and impacted member experience.

Strategic Link partner, Rippleshot, offers a cloud-based technology solution that leverages machine learning and artificial intelligence to mitigate fraud loss. Its research indicates that early breach detection can stop 60% of fraud losses.

To help avoid such losses, credit unions can partner with Rippleshot to quickly and easily define a strategy for implementing machine learning and artificial intelligence. It also offers solutions to overcome internal obstacles and deliver the keys to successful implementation.

“AI is driving the future of fraud detection, and credit union leaders should be considering solutions using more sophisticated technology,” said Jason Smith, VP of Strategic Resources at Northwest Credit Union Association. “Relying on outdated manual tools and home-grown spreadsheets won’t cut it anymore. Credit unions must find a way to leverage data-driven technologies that are both cost-effective and easily onboarded with limited resources.”

Too often, by the time information about fraud or a breach is realized, the amount of fraud loss and compromised card fraud has already reached high levels. Canh Tran, co-founder and CEO of Rippleshot, suggests credit unions should consider employing machine-learning-driven data algorithms to identify how to achieve better results for themselves and their members.

Tran has identified five hurdles credit unions may face when seeking solutions to address fraud detection: 

  1. Lacking access to big data: Most credit unions don’t have enough data to fight fraud effectively, as it is not easily accessible. It’s imperative to ask more from your processors.
  2. Privacy challenges: Consumer and personal identifiable information is very sensitive.
  3. Resources: Credit unions may believe that AI is too complicated and time consuming to implement, especially for IT departments that are already stretched thin for resources.
  4. Actionable data: Data analytics need to be actionable and deliver measurable return on investment results.
  5. Keeping pace with algorithms: Fraud and fraud patterns evolve and change more rapidly than credit unions can keep pace with.
How Machine Learning and Big Data Addresses These Gaps

The very nature of machine learning is to use the data it is processing and adapt to changing trends or relationships identified by that data. Detecting and mitigating fraud to manage risk involves in-depth data analysis to understand relationships and trends, and to pinpoint where and when the fraud originated. Fraud teams can no longer rely on manual analysis of network alerts to get the job done.

As financial institutions continue their digital transformation, and invest in innovative technology, the floodgates are open to more touch points for fraudsters to breach. Thanks to machine learning, the digitization of data, and AI, credit union leaders have access to the infrastructure and industry-leading tools necessary to fight fraud — if they’re willing to invest money where it counts.

Editor’s Note: Rippleshot can help credit unions formulate and implement fraud-fighting strategies. It is a portfolio company of CMFG Ventures – a  subsidiary of CUNA Mutual Group. If your credit union is interested in partnering with Rippleshot, contact Jason Smith, VP Strategic Resources, at 208-286-6794.