AI Fraud Detection: The Many Ways in Which the Banking Industry Can Benefit from it

Banking is decidedly a vital part of the global economy — that much everyone agrees with, yes? And despite its importance and the role it plays, banking continues to be susceptible to high levels of risk of fraud. But thanks to the constant evolution of technology, especially artificial intelligence, and machine learning, this sector is on the precipice of a revolution. And even though that transformation is extensive, we will focus on technology and banking fraud. Yeah, so artificial intelligence and banking; AI has already facilitated a wide variety of apps in this regard, thanks to the advances it has made over the past few years. So, besides enabling automation of routine tasks, artificial intelligence has also enabled banks and financial institutions to fortify their defenses against all manners of financial fraud.

The extensive data that financial organizations gather and have access to, along with insights delivered by artificial intelligence and machine learning, has helped banks pro-actively deal with fraud. And in this context, the type of financial fraud is irrelevant, i.e., transactional fraud, application fraud, money laundering, credit card fraud, and more.

1. Transactional fraud: Often enough, there will be someone with a customer’s stolen card details and identity, who can execute fraudulent transactions.
2. Application fraud: This occurs when someone’s personal information is stolen to process applications such as that for credit cards, opening bank accounts, and more.
3. Credit card fraud: It is the most prevalent type of financial fraud and its incident rate has grown even more over the past few years owing to the emergence of mobile payments. Research suggests that losses arising from credit card fraud could touch $44 billion by 2025.

You get the drift — the scope to misuse stolen information is immense. But AI and ML enable banks to automate and accelerate the analysis of their customers’ behavior and identify any deviations or red flags. Machine learning makes these processes even better over time because the more data it has, the more it learns over time and that too without needing assistance from human beings. This ability then ensures the fraud detection systems driven by these technologies are also able to adapt quickly.

But if you are still unsure about how much AI and ML stand to help banks with fraud detection, these benefits will help convince you.

1. Machine learning, when leveraged correctly, can be used to identify transaction fraud before it even occurs.
2. Decision trees can be implemented to put together a specific group of rules based on ideal customers’ routine behavior. Such algorithms, once trained, can be used to detect any irregularities in customers’ behavior.
3. Neural networks, a formidable tool based on the workings of a human brain, can master normal behavior and then use that knowledge to determine fraudulent activities and that too in real-time.

As evidenced by the discussion above, any endeavor related to bank fraud prevention and detection will do well to be aided by avant-garde technologies such as artificial intelligence and machine learning.