Fraud has long been a major issue for financial services institutions. And as global transactions have increased, the danger has too. Fortunately, artificial intelligence has enormous potential to reduce financial fraud. As automated fraud detection tools get smarter and machine learning becomes more powerful, the outlook should improve exponentially.
security company McAfee estimates that cybercrime currently costs the global economy some $600 billion, or 0.8% of the global gross domestic product. One of the most prevalent forms and preventable types of cybercrime is credit card fraud, which is exacerbated by the growth in online transacting. The speed at which financial losses can occur when credit card fraud takes place makes intelligent fraud detection techniques increasingly important.
Because of the availability of large volumes of customer data, together with transactional data that is updated as transactions occur, AI can be used to effectively identify credit card behavior patterns that are irregular for specific customers.
My company created an automated, generalizable predictive algorithm that specializes in matching customers and products. I believe it is possible that a similar model could help in the fight against cybercrime. Cybersecurity companies could focus on implementing deep learning to create user and transaction fingerprints by identifying underlying relationships between data points and reducing them to their core components, which they can then cluster together using mathematical models and (depending on a user cluster) can then monitor behavior patterns in relation to other users in that cluster at any given time.
An added advantage of a more sophisticated model is its potential ability to use a wide variety of data points (like Mastercard has already done) to continually fit different customers and transactions into the best-suited clusters for accurate comparison. Thus, as the life circumstances and spending habits of a customer changes, the model would automatically adjust what it views as potentially fraudulent transactions. This could reduce actual fraudulent transactions and minimize false fraud flags (false positives).
False positives occur regularly with traditional rule-based anti-fraud measures, where the system flags anything that falls outside a given set of parameters. For example, if you are planning a trip abroad and you start buying airline tickets and accommodation, this may trigger a fraud warning. A smarter system as described in the two previous paragraphs, that can better understand the underlying patterns of human behavior, could potentially use the new customer data (your travel purchases) to match you with a different cluster of users (for example, holiday travelers). It can then test your behavior against transactions typical to that of the new cluster of users, holiday travelers in this example, before automatically raising a fraud flag on your account.
This should increase customer satisfaction by limiting the number of times that a customer can’t complete a transaction due an incorrect flagging and reduce the operational overheads of the financial institution, by preventing unnecessary interactions with such customers.
The potential for electronic fraud is getting larger with the increased use of advanced technology and the global nature of many transactions. Add to that the newfound ability of cybercriminals to utilize unregulated cryptocurrency exchanges to cash out the return of their criminal online activities, and it becomes clear that it is imperative to use the most advanced techniques available to fight cybercrime.