We hear many common pain points from fraud analysts and managers.
“I don’t know what fraud risks are coming my way…I worry about the high-dollar events I can’t see coming…fraud analysis takes too long…I miss too much fraud until after it happens…I can’t do my job without an entire team of fraud analysts.”
The list goes on and on — and these pain points escalate when big fraud events hit.
With the right implementation of AI, machine learning and big data to know where, when and how the biggest merchant risks are impacting your cardholders, these pain points can be proactively alleviated. Instead of relying on reactive strategies that cause your fraud team to respond to incidents as they are occurring, or after it’s too late to stop their spread, the application of the right data and technology can help your team get early warnings about where fraud is occurring before it hits your institution.
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.
Community banks and credit unions, which are becoming bigger targets from fraudsters since they typically have less sophisticated fraud technology tools, are tasked with keeping up with the latest fraud detection tools to protect their customers and members. 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, unfortunately, involves a lot of upfront time, financial investment and analysis, only to fall short in being able to accurately pinpoint where an organization's biggest risks and knowing 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. We've broken down where financial institutions leaders can proactively start thinking about how to protect their customers and members.
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.
From a financial institution leader'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 efficiencies 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 efficiencies that deliver more impactful, data-driven results without needing to invest in more manpower or IT resources.
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