The New Baseline for Financial Crime

The New Baseline for Financial Crime

Financial services organizations (FSOs) must continually evolve to protect against the growing sophistication of financial crime. Traditionally, FSOs 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: Stale rules lead to a lot of false positives, meaning you’re likely to block a lot of good customers and may lose their business.
  2. Rules are not dynamic: Threat vectors are constantly evolving, meaning the library of rules must expand with it. Beyond that, you need to tune those models on a regular basis to ensure they’re still generating meaningful alerts. Conventionally, FSOs would reach out to their vendor to continuously update and fine-tune the rules, leading to a costly and time-consuming process.
  3. Rules tend to be inefficient from an investigation perspective: False positives create a lot of noise. So, in practice, only a fraction of AML alerts progress to becoming suspicious activity reports (SARs) – yet your team is spending the majority of their day reviewing them. This is a heightened issue for mid-sized banks, community banks and credit unions because they usually don’t have a big team of analysts and investigators.

FSOs need to make the most of their resources and become more proactive in improving efficiency -- that’s why many of them are turning to AI and machine learning to solve their issues.

It’s not Skynet, AI can Truly Improve Efficiency

In the past, the idea of a machine autonomously learning and adapting was quite intimidating. But it now has become crucial for FSOs to adopt an AI-first approach. To better understand how exactly AI can help, it’s best to look at the two main areas where it most drastically improves efficiency: operations and detecting threats.

  • Operational efficiency:

AI helps automate operational processes. Looking at AML and SARs as an example, AI helps pre-populate these reports with relevant information and case details – meaning your staff doesn’t get bogged down by these administrative tasks. They can focus on meaningful initiatives, prevent losses and catch criminals.

  • Detection efficiency:

AI allows you to not only gain much richer insight into customers and transaction patterns but also detect suspicious activity earlier on to stay ahead of evolving threats. Through these insights, you make much more granular analyses, without requiring more resources.

AI (and machine learning by extension) enables autonomous analytics at scale for every customer account to ensure that alerts are only generated for true, risky behavior.

Machine Learning: A Crucial 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 is the goal 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 you to ensure these models are working effectively.

  • Supervised machine learning:

This type of machine learning requires labeled data. You would need to train the models with examples and patterns indicative of the “good”, or lower risk, and the “bad”, or higher risk. This style of machine learning is designed to focus on a specific type of threat – meaning one will miss unknown threats not accounted for. So, supervised machine learning models are just fine when you know what type of financial crime you want to look for, but it’ll do just that. New threat vectors will not be detected because your models weren’t trained to look for them.

  • Unsupervised machine learning:

Compared to supervised, unsupervised machine learning doesn’t require labeled data to train it. Rather, it self-learns a user’s behavior, then identifies and groups patterns together. By understanding each user’s behavior, it’s able to effectively identify anomalies unique to that user – reducing unnecessary alerts and false-positives. Unsupervised machine learning is especially powerful in detecting unknown threats that you haven’t experienced before. 

To get the most out of your financial crime solutions, you need to understand the differences between rule-based models and machine learning models. More importantly, you must understand how this impacts effort, operational efficiency and detection efficiency. Take the time to challenge your vendors and ask what type of machine learning they’re using before deciding which is best for you.

Financial crime will continue to increase, but the same can’t be said for the size of your staff or budget. You need to make sure your tools and technology is empowering you to do more with less. That’s why FSOs are quickly adopting AI and machine learning to work smarter, not harder, and stay one step ahead.

To learn more about working smarter and not harder, download our Xceed eBook here.

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