AI and ML-Based Crime Solutions: An Alternative Approach for Small Financial Institutions
20 January 2020
Financial institutions are discovering ways to leverage Artificial Intelligence (AI) and Machine Learning (ML) to better combat the rapidly growing challenge of financial crime.
The catalyst for application of these new technologies in crime risk management for banks and credit unions has been the advent of big data. Equipped with a large dataset that provides all of the variations and nuances of legitimate and fraudulent behaviors, systems can be trained to identify abnormal patterns and generate appropriate decisions and actions to mitigate the corresponding financial crime risk.
For large financial institutions, access to big data has been a game changer. The significant volume of data available to those firms is enabling AI and Machine Learning-based solutions to be applied to a broad range of critical business applications; enabling those tasks to be performed more accurately, quickly and cost effectively than manual methods. In turn, this capability has provided large banks with significant competitive advantage, in terms of greater protection and lower customer friction.
However, for smaller banks and credit unions that lack the resources or a customer base that’s large enough to generate a significant dataset, AI and ML-based solutions have failed to deliver the same benefits. Lacking the ability to leverage new technologies, these financial institutions have less ability to manage financial crime risk, which puts them at greater competitive disadvantage.
Small Banks and Credit Unions can complete without Big Data:
To compensate for the absence of datasets that are large enough to leverage AI and ML-based solutions, small and mid-sized financial institutions must develop alternative solutions that yield operational benefits that put them on an equal footing with their larger competitors.
That goal can be accomplished through a strategy that integrates these three tactics:
- Combine Technology with Subject Matter Expertise – Lacking automated solutions, human expertise must be applied to identify patterns of fraudulent activity, in order to develop and monitor appropriate rules. With the right people to support this function, smaller banks are in a position to gain an edge over large competitors, both in terms of reducing false positives as well as customer friction. If full-time staffing is a financial challenge, there are outside resources available that can be applied in a cost-effective manner.
- Establish and Maintain Improvement Guidelines – Having appropriate rules in place for identifying and managing fraudulent activity is critical, but it’s also just the starting point for crime risk management. To keep up with increasingly sophisticated attacks, and to reduce the overall load over time, a feedback loop with specific goals must be established as a means to consistently refine and improve the rules and to address issues in a timely and effective manner. This often overlooked “Do It Yourself” initiative is the cornerstone of an effective capability for financial institutions, regardless of size.
- Ensure Complete and Consistent Transparency – Whether systems are applied manually or through technology, it’s critical for bank staff involved in those applications to fully understand the rules and processes that are being applied, and to learn from the insights that are captured, so that institutional knowledge is enhanced. There should be no ‘black box’ solutions; only total transparency, to ensure that solutions keep pace with the increased rate and complexity of financial crime.
Despite the lack of data necessary for them to benefit fully from AI and ML crime solutions, small and mid-sized financial institutions are actually in a strong position to match or exceed the fraud risk management capabilities of their larger competitors. While capital requirements demand that big banks rely heavily on AI, ML and other technologies, smaller banks can gain competitive advantage through the application of solutions that are tailored to their resources and requirements, are cost effective, keep pace with growing crime risk, and enhance the customer experience.
– By N. Venu Gopal