Two findings from a recent industry study highlight the benefits AI and machine learning and what role these technologies play in the digital transformation of community banks and credit unions.
This report underpins the impact COVID-19 is having on financial institution cloud technology investments and how advancements in machine learning-driven software will help them weather the storm. These insights, gathered from a poll of top banking executives, points toward the need for greater digitization across the financial services ecosystem.
Of course, this will also open up the floodgates for another problem: New channels for fraudsters to exploit and monetize. We've broken down how financial institution leaders can assess their fraud detection strategies, where machine learning plays a pivotal role in mitigating risk and why this matters for 2020's fraud trends.
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 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.
To fully understand where technology investments need to be made, financial institution leaders must first get a pulse on what their current fraud detection processes are, and how they can thinking about adapting to keep up with emerging fraud threats. To do so, we've broken down seven categories and corresponding questions to consider.
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 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 or IT resources.
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:
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.