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.