Rippleshot Blog

Why Community Banks and Credit Unions Need AI and Machine Learning

Posted by Anna Kragie on Oct 17, 2019 7:00:00 AM

The push for greater digitization across the financial services ecosystem has created another challenge for financial institutions: New channels for fraudsters to exploit and monetize. 

Banks must keep up with customer demand and offer more to compete, but they must also be mindful of the security measures needed to keep up with these trends. This has left many FIs with a Catch-22. Fraud trends are changing as fraudsters get more sophisticated in the methods they use to breach personal and financial data.

Big banks are proactively working to get ahead of fraudsters, but many smaller institutions are still relying on time-consuming, manual methods to spot fraud patterns — or count on their call centers to alert them when fraud occurs. This approach involves a lot of upfront time, money and analysis, only to fall short in being able to accurately pinpoint where an organization's biggest risks and how to get ahead of those trends. 

 The bigger banks with deep pockets are gaining a FinTech edge with teams of data scientists and sophisticated software tools to keep their fraud detection tools aligned with what the market demands. Smaller FIs know to compete they must embrace new technologies such as AI and machine learning. But knowing how and where to start can be the biggest hurdle.

The Value of AI and Machine Learning A Financial Institution of Any Size

Through the application of high-performance software, machine learning technology has created advanced computing abilities that have a broad-scale reach for community banks and credit unions that allow them to better compete against the bigger banks. This has created a new reality for financial institution leaders looking to enhance their fraud detection tools beyond basic what's readily available in the marketplace today.

Relying on outdated manual tools and home-grown spreadsheets won't cut it anymore. To compete in the market, community banks and credit unions must find a way to leverage data-driven tools that are both cost-effective and easily on-boarded with limited resources. 

From a FI's perspective, machine learning technology is useful because of its ability to automatically processes data to create predictive fraud models — enabling issuers to strategically manage their fraud loss and reissue management strategies. But Machine learning isn’t just about fraud detection — it helps issuers gain access to, and have a better understanding of big data, and how to apply it to real-world scenarios on a daily basis. It also helps reduce cost by driving more efficiences that can be achieved through better tools that don't require adding more FTEs. That last part is key. Implemented properly, AI and machine learning tools can drive incredible operational efficiences that deliver more impactful, data-driven results without needing to invest in more manpower. 

As more investments across the public and private sectors are put into technologies like machine learning that allow data scientists (like those at Rippleshot) to fully make sense of millions of data points in order to help organizations better understand the patterns that exist within their own data — and what they can do with that data — the better off the entire payments ecosystem will be. 

Of course, AI and machine learning aren’t entirely new concepts for banks — but in 2019 they are gaining rapid traction across the financial services ecosystem. The power, promise, and performance, however, for Main Street banks presents different challenges and opportunities than multi-national banks.

Here a few core reasons community banks and credit unions need to consider where an AI/Machine Learning approach can fit into their business plans: 

• Payment Fraud is Getting More Complex: Financial institutions need the sophistication of technology that combines big data, AI, machine learning, rules-based logic and predictive models. Those tools alone aren't useful, but combined can be exceptionally effective at spotting, stoping and preventing the spread of fraud.

• AI Technology Is Faster Than Humanly Possible: Today's payment fraud needs solutions that can act faster and at the scale at which fraud spreads itself. Addressing fraud risk and evolving fraud methods needs to be faster than manual analysis and humans can can compute today.

• AI Acts In a Matter of Seconds to Spot Patterns: There is rich insights that exist within data patterns. The problems many financial institutions face is that they don't know what to do with all their data, don't have the access to enough data or don't have a dedicated fraud team to manage this complex process. AI and predictive technology tools find anomalies at scale in a way that manual processes can't compete with.

Forbes Contributor Louis Columbus gave his insight into what's driving these trends in a recent column: "AI’s ability to interpret trend-based insights from supervised machine learning, coupled with entirely new knowledge gained from unsupervised machine learning algorithms are reducing the incidence of payments fraud. By combining both machine learning approaches, AI can discern if a given transaction or series of financial activities are fraudulent or not, alerting fraud analysts immediately if they are and taking action through predefined workflows."

Where AI and Machine Learning Fit Into the Evolution of Card Fraud

The very nature of Machine Learning is to learn from the data it is processing, adapting to changing trends or relationships in the data. Detecting and mitigating fraud to manage risk involves in-depth data analysis to identify relationships and trends to pinpoint where and when the fraud originated. 

Relationships and trends are becoming leading indicators of outcomes (like fraud). As these leading indicators emerge in new data, outcomes can be predicted and acted upon. A data analytics approach equips issuers with the tools to understand what’s happening across their own card portfolio — and how to detect risk. But you have to have access to that data and be able to make sense of it all. 

Rather than waiting on manual review processes, or for alert systems to provide reports in a few weeks, banks and credit union leaders should be demanding more in their card fraud compromise detection solutions. AI is driving is the future of fraud detection and banks and credit union leaders should be embracing opportunities with more sophisticated technology in the market.

Solutions need to be able to streamline the data feed process, automate the analytics process, offer continuous model refresh, and deliver actionable results within hours — and continue to do so on a daily basis. Successful AI and machine learning solutions for financial institutions automates the tasks of gathering and checking data, detecting fraud, and validating the results. This frees managers to make strategic decisions, as opposed to getting mired in the mechanics of AI and machine learning.

PYMNTS research also indicates some key findings about how financial institution leaders view the value of AI for fighting fraud. The key points highlighted in PYMNTS' latest report on the subject are:

  • "FIs see AI’s potential to more effectively fight fraud, but most don’t use it": Of those who do use it, they use it to fight fraud often; 45.5 percent use it as part of their fraud prevention efforts.
  • "FIs believe AI’s real benefit is that it reduces manual review and exception processes": 66.2 percent of FIs’ fraud specialists see reducing the need for manual review.
  • "FIs have concerns about AI’s complexity and transparency": One apprehension to adoption is that many believe AI is too complicated and time-consuming compared. This is changing as AI becomes more common at the major FIs. 
  • "FIs express strong interest in using AI’s dynamic capabilities to improve fraud prevention." 66.7 percent of FI respondents believe the solution would help reduce manual review.

Deployed properly, machine learning that continuously sifts through millions of data points, patterns and variables to deliver timely results to help financial institutions more cost-effectively address growing fraud trends. As financial institutions continue down their digitization transformation — and invest in innovative technology — this opens the floodgates for more touch points for fraudsters to breach, particularly as it relates to card fraud. Machine learning and AI-driven software solutions can help FIs learn what risks exist in their own card portfolio, which can help identify a strategy to proactively get ahead of the spread of fraud. 

Topics: Fraud