Role of Artificial Intelligence and Machine Learning in Bank Fraud Detection
Emerging technologies like Artificial Intelligence, Machine Learning and Neural Networks hold great potential in Banking / Financial Fraud Detection. The technology companies are getting aggressive to increase cybersecurity thereby reducing economic losses due to cybercrime.
With exponential technologies creating disruption in the industrial ecosystem, there is hardly any domain left that is not thinking of reaping the benefits. In one of my previous posts, I showcased how Financial Accounting is taking advantage of emerging technologies. Today, I would like to talk about financial fraud and how technology can intervene for the better.
Banking and financial services have never been able to stay aloof of fraud, a major issue which has been crippling the sector for long. With markets getting more open to online global transactions, the fraudulent aspect has become quite significant with impending dangers.
Enterprises are resorting to artificial intelligence to detect and minimize financial fraud. With automated fraud detection tools getting smarter and machine learning becoming more powerful, the outlook should improve exponentially.
According to its latest report, 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 the online transaction. The velocity at which financial losses can occur when credit card fraud takes place, makes intelligent fraud detection techniques increasingly important.
Technology companies are implementing artificial intelligence in making use of the large volumes of customer data available in conjunction with transactional data that is updated as transactions occur, to effectively identify credit card behavior patterns that are irregular for specific customers. Automated, generalizable predictive algorithms that specialize in matching customers and cards are helping in the fight against cybercrime. Cybersecurity companies are focusing 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 behavioral patterns in relation to other users in that cluster at any given point of time.
A cutting edge of a more sophisticated model is its potential capacity to use a wide variety of data points to consistently retrofit different customers and transactions into the best-matching clusters for an accurate comparison, the way Mastercard has already done. Thus, as the real-life scenarios and spending habits of a customer change, the model would automatically adjust what it visualizes as potentially fraudulent transactions. This could minimize actual fraudulent transactions and reduce false fraud alerts.
False fraud warnings are pretty common with traditional rule-based anti-fraud applications, where the system flags anything that doesn’t come under a given set of parameters. For example, if you are having a family wedding and you start purchasing expensive jewelry online, this may trigger a fraud alert. 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 wedding purchases) to match you with a different cluster of users (for example, wedding buyers). It can then test your behavior against transactions typical to that of the new cluster of users, wedding buyers 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 is unable to complete a transaction due to an incorrect flagging and reduce the operational overheads of the bank or financial institution, by preventing unnecessary interactions with such customers.
The potential for electronic fraud is getting stronger with the increased use of emerging technologies and the global nature of numerous transactions. You may add to that the nascent ability of cybercriminals to utilize unregulated cryptocurrency exchanges to cash out the return of their criminal online activities, and it becomes evident that it is imperative to use the most advanced techniques available to combat cybercrime.
With so much happening on the technology front, it is really exciting for those who hope to reduce fraudulent activity even further. Now they have a new generation of algorithms in place which are based on the way people think. These are known as Convolutional Neural Networks and are based on the visual cortex, which is a small segment of cells that are sensitive to specific regions of the visual field in the human body. In effect, these neural networks use images directly as input, functioning in the same manner as the visual cortex. This implies that they are able to extract elementary visual features like oriented edges, end-points, and corners.
This new development in Artificial Intelligence makes algorithms that were already intelligent infinitely smarter. This technology can study the spending data of a credit card holder and based on this information, be able to determine, whether the cardholder actually performed the most recent transaction on his/her credit card or if someone else was using the credit card data. Significant potential lies in the ability of neural networks to learn relationships from modeled data. Implementing this type of solution to nail down cybercrime, for example, will minimize the economic losses tremendously.
Financial or banking fraud cases have always existed since time immemorial and have become more complicated with advancement in technology. However, by leveraging technology, especially the neural networks, authorities can identify these fraudulent activities and stop them before they take mammoth shape to cause harm.
Timely detection of fraudulent activities will reduce overall costs for banks and financial institutions. Also improve their reputation with clients and investors, who are likely to be more loyal to an institution that better protects their money. And there is even the possibility that the financial institutions or banks could channel some of the cost savings they make from reducing fraud back to the customers, in the form of lower transaction fees or reduced interest rates.
Artificial Intelligence, Machine Learning together with neural networks have huge potential to create a radical shakeup of the entire banking and finance industry. It’s an interesting space to keep a watch on, in hope of better fraud-free banking…