Fighting Financial Crime Using AI

Fighting financial fraud often feels like an uphill battle, and the battle is only becoming harder

According to a PwC analysis, nearly half of the organizations polled – 47 percent – had encountered fraud in the previous two years. This comes at a hefty price: $42 billion in total. And it appears that financial fraud has only gotten worse in the last year and a half. According to the 2021 Payments Fraud and Control Survey conducted by the Association for Financial Professionals (AFP), 65 percent of financial professionals who have seen an increase in payments fraud activity link it to the pandemic.

Companies invest in technology to combat financial fraud, but it isn't always effective. When it comes to AI in financial crime and machine learning, in particular, provide significant benefits, but they won't operate unless the proper foundation is in place first.

Machine learning and AI in the fight against fraud

The use of machine learning and artificial intelligence (ML/AI) to combat fraud is not new. It's been around for a while, and these technologies have a number of distinct advantages.

However, many of the AI in financial crime and machine learning solutions currently in use fall short of the technology's potential. They're all doing something valuable, but they all have the same problem: they're built on a rules-based system with some machine learning added on top to improve it.

The reason for this is that fraudsters have quickly adjusted their behavior in reaction to how these solutions' controls work, and the systems themselves – albeit having some machine learning built in – are essentially static in what they do. They're really focused on one thing, and they're quite skilled at it – but once they discover they're being blocked, the fraudsters alter their approach.

Responding to this new method of operation (MO) requires a complete system overhaul, meaning banks are always several steps behind the fraudsters.

Ingredients for success

More adaptive systems are required, which do not require a total reconfiguration when the MO changes.Instead, they have more discovery and holistic monitoring baked in, so that when fraudsters switch to a new approach, the system can respond automatically rather than having to start over. This is a relatively new feature with a lot of potential.

These are the five characteristics of a successful AI in financial crime/ML-based fraud detection capability:

Data agnosticism: Organizations must be data agnostic because no one can predict what will happen next year.
Automation: As much as possible should be automated. Most older systems are manually programmed by professionals, but information extraction should be automated.
Relationships to be understood: Different types of fraud may require you to look at the last six months of a customer's history. Transaction-based systems may look at your current transaction and a few recent transactions, but different types of fraud may require you to look at the last six months of a customer's history. This includes relationships, such as looking at who they've done business with and being able to not only track but also model it.

Rules that run in parallel with machine learning models: You'll need the capacity to execute rules in parallel with machine learning models, as well as an overall alerting strategy framework that takes everything into account.

Algorithms that adapt to historical fraud patterns without becoming overly reliant on them: This allows you to immediately detect new fraud.
To construct a comprehensive monitoring system, a mix of supervised, semi-supervised, and unsupervised learning models is required.

Establishing a solid foundation

Prior to adopting an ML/AI-based fraud detection solution, it's critical to establish organizational goals. This includes objectives for how responsive you want to be and how quickly you can adapt. You want to set a goal for yourself. For example, if it takes you a year to adjust to a new fraud strategy, establish a goal of completing it in a month and work toward it.
Accomplishing these objectives necessitates the use of technology, but it also necessitates a review of your current processes to ensure that they are not impeding your development.
You'll be playing catch-up for the rest of your life if you don't take these actions.

Goal-oriented

Fraud costs businesses tens of billions of dollars every year. Organizations must have the proper technical elements in place to reap the benefits of fraud-detecting AI, but AI can play a significant role in preventing fraud.Make sure you lay a solid technological basis to eventually move ahead of the scammers. This includes a mix of supervised, semi-supervised, and unsupervised learning models, as well as a list of measurable goals to guarantee your AI is performing properly.

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