AI & Machine Learning: The New Baseline for Financial Crime

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Ever since the dawn of the technology era, financial institutions have evolved alongside it to better protect against the growing sophistication of financial crime. Traditionally, banks and credit unions used solutions that were rule-based to fight financial crime threats. But using rule-based models alone are no longer sufficient. That’s because there are three main problems with rules:

  1. Rules get stale quickly
  2. Rules are not dynamic
  3. Rules tend to be inefficient from an investigation perspective

All types of FIs need to become more proactive in improving efficiency, hence why many of them are turning to AI and ML to solve their issues.

It’s not Skynet, AI can Truly Improve Efficiency

As scary as the idea of a machine having the ability to grow and adapt was once upon a time, it has become crucial for banks to adopt a stronger AI presence.

Through improving both operation efficiency on one side by limiting administrative tasks, and the accuracy of recognizing risky behavior of an individual, AI has allowed FSOs to better focus on meaningful initiatives and catching the bad guys.

Machine Learning: An Important Tool in Detecting Threats

One of the biggest benefits of machine learning is the ability to monitor and detect threats with as few resources as possible, which sounds like a godsend for anyone managing threats day-to-day. However, there are different types of machine learning. In the evaluation process, it’s important to understand the differences and, more importantly, understand what type of effort is required from them to ensure these models are working effectively.

  • Supervised machine learning:

This is where one trains the models with labeled data, so they know what’s “good”, or lower risk, and what’s “bad”, or higher risk. This style of machine learning is designed to focus on a specific type of pattern or threat – meaning one will miss unknown threats not accounted for.

  • Unsupervised machine learning:

Compared to supervised, unsupervised machine learning doesn’t require labeled data to train it. Rather, it learns a user’s behavior, then identifies and groups anomalies together, which is especially useful for unknown threats that you haven’t experienced before. Even though it doesn’t need examples, it will still detect anomalous behavior.

To get the most out of their financial crime solutions, any financial institution needs to evaluate what their own needs are to decide which style of machine learning is best for them.

What was once unheard of in common procedures of yesterday, today banks and credit unions are flocking to utilize the adaptable capability of AI and machine learning to work smarter, not harder, and stay one step ahead.

It’s time to embrace unsupervised machine learning and AI for your digital transformation.