It’s just impressive how the human brain learns new information and uses it to solve problems. Such behavior is evident even in children as they learn from their environment and have their own way of interpreting things. Computers have been trying hard to emulate such intelligence and today, they are getting close. Such AI applications can positively impact our lives and are already present in some form around us. For instance, one of the most common AI applications can be seen in the form of Apple’s voice assistant Siri or Netflix’s content recommendation engine. These AI systems can learn from your behavior and assist you in solving your queries. At some level, AI integration is already a crucial part of our lives. Just imagine a system which can think the way as humans do, but perform millions of calculations per second. The number of problems they can solve can be limitless. And it comes as no surprise that companies such as AIBrain, Anki, Banjo, and iCarbonX are investing heavily in this technology to provide solutions in the banking sector. So what are the key AI applications that benefit the banking sector?
AML Pattern Detection
Anti-money laundering (AML) activities have been troubling governments all across the world. To crack down such activities, it is essential to think like them and develop countermeasures to tackle such issues. People involved in AML operate skillfully by depositing small sums of money which don’t trigger bank reporting requirements. AML detection is one of the most prominent AI applications in the banking sector as it can assign an AML threat score to detect such fraud. By profiling each customer’s banking transactions, AI systems can learn users’ banking behavior and detect anomalies to crack down on AML activities.
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Customer support is one of the crucial areas that ensure customer satisfaction in the banking sector. However, most of the customer queries are repetitive in nature, and it costs banks a lot to serve such customers. Uncontrolled spending can increase banks’ operations expenses and subsequently increase their cost of service. Chatbots are a welcome addition to this list of AI applications in the banking sector as it can simulate human chats. Such systems identify the content and emotions in the message and give an appropriate reply or even solve customer queries. After engaging in a lot of real-time chats, the system can learn to provide more accurate responses through machine learning. Bank of America, JPMorgan Chase, Capital One, Master Card, and many more banking companies are already using this technology to improve customer satisfaction.
Many people make millions of dollars in micro trading. Such people deal with a single stock and buy and sell shares multiple times within a single day. Such trading requires people to process vast amounts of information. AI systems can replicate such transactions by making accurate calculations to trade and turn in a profit. Numerous reports state that 70% of the trading today is carried out by automated AI systems.
One of the key AI applications in the banking sector has been in the area of fraud detection. It has done so with superior results. AI systems process multiple inputs and learn from user behavior to distinguish regular transactions from that of fraudulent ones. Although fraud attacks have become more sophisticated, AI and machine learning systems have always been in the driver’s seat. AI and machine learning skim through all transaction data to identify visible patterns and detect anomalies to identify fraud. Even the early data analysis technique FICO Falcon fraud assessment system has been refined to tackle modern-day fraud.
Similar to how the content recommendation engine works seamlessly to suggest to you the music or video you like, players in the banking sector can use customer recommendation. Using past data on users and how they behaved towards various offers and promotions from the bank, AI systems can recommend suitable users based on their history with the bank. Such customer recommendation systems help banks to grow their revenue stream and match customers to particular products.