Residual credit fraud may represent “only” 10% of all fraud attempts, but its ongoing presence is a huge thorn in the side of credit institutions, not only in terms of revenue lost, but also when it comes to their reputation. Tackling this small 10% via more “traditional” methods has proven to be a frustrating uphill battle for most companies, so more and more are turning to AI and machine learning as an alternative. Use of these technologies as fraud-fighting tools is expected to triple over the next two years. Why such enthusiasm?
Most fraud can be detected manually with raw data, using rules set for 20-30 specific variables by the fraud prevention department. The problem is that this method produces a "simple" model, which only captures attempts that are... well, simple. But there is nothing simple about residual fraud.
Detecting it requires collecting much more data, cross-referencing it, contextualizing it, and updating it regularly to build and maintain a model that is as sophisticated and agile as the tactics employed by this new generation of cybercriminals.
However, as we will see, humans cannot tackle this feat alone.
Fighting residual fraud today requires more complex models, which also translates into many more variables to crunch. But while there is usually an abundance of data available, whether in-house, open source or from vendors such as Creditsafe, the real challenge lies in how to process it.
Beyond 30 or 40 variables, the formulas become too complex for the human mind to grasp. With hundreds of indicators to cross-reference and navigate, it can be hard to know which way is up.
Choosing which variables to add, determining how much weight each one should hold—and its relative influence, putting them in categories, establishing correlations between them (for example, to detect an over-indebted client) is a huge undertaking that eats up human and financial resources.
To get around the problem, more and more companies are adopting artificial intelligence and machine learning, which allow them to collect an unlimited amount of data, cross-reference it and calculate the relative influence of different variables with unprecedented speed, accuracy and reliability.
Humans are still in the driver’s seat, but they are no longer burning themselves out needlessly.
To remain effective, a model has to be tested and assessed regularly, in real-world conditions. It must be able to evolve at the pace of fraud, shifting tactics as needed, and taking advantage of every opportunity.
Here again, the human mind quickly reaches its limits. A model can be manually tested in house once or twice, but keeping it working requires ongoing maintenance... and therefore, ongoing manpower and financial resources to continue seeing results.
Companies need AI and ML if they want to truly automate the testing process, evaluate AI models regularly, and ensure they continue to perform with maximum ROI and minimal effort.
When it comes to fighting credit fraud, not all AI and machine learning solutions are created equal. Out-of-the-box or blanket AutoML models are simply not enough to counter the avalanche of traps set by scam artists and their increasingly sophisticated and subtle strategies.
Credit institutions need expert machine learning solutions designed with in-depth knowledge of the industry they are meant for—solutions that know how fraudsters work and which loopholes they’re likely to exploit; that know not only what data to include, but also how to put it into context and use it to build the right models; and that can create effective anti-fraud strategies to be implemented over time.
Corporate credit fraud helps illustrate this need perfectly. Many cases of corporate credit fraud can only be detected by closely observing commodity flow, or scrutinizing purchase and sale invoices. But without this inside knowledge, it would be very easy to make the mistake of examining only accounts filed with Commercial Court clerks... completely missing incidences of fraud.
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