There is a lot of potential ways to use machine learning in the banking sector.
Artificial intelligence in banking is revolutionary.
The use of AI in society has grown significantly in recent years. What is machine learning? In short, machine learning is a subset of artificial intelligence. The concept was first coined in 1959 by Arthur Samuel who derived the concept from the study of pattern recognition and computational learning theory in AI. It is the science of enabling computers to learn on their own by automating analytical model building. These systems strive to learn from data, identify patterns and make decisions without being explicitly programmed to do by humans. As data continues to grow more prominent in our society, the need for machine learning grows as well.
Today, with the abundance of data, affordable processing power and inexpensive storage, machine learning is able to produce accurate and fast results, making it very appealing to many industries, especially the business world. According to the 2017 AI Index Report, the share of jobs that require AI skill sets have grown about 4X since 2013, showing that machine learning is a tool that has become essential for many fields. The finance industry is no exception to this growing interest and adaptation and there is good reason for it. Machine learning gives banking companies the opportunity to vastly improve their business by making processes more efficient and effective. Here are a few applications of machine learning in banking.
Customer service continues to be one of the biggest obstacles for the banking industry. At first, the speed of customer service needed to improve, however, with the development of automated phone support, banking customers have begun to complain about being unable to reach a human representative. This presents a great opportunity for banks to invest in machine learning technology. With an automated support system, companies benefit from cutting costs and potentially expediting the process.
However, one of the biggest flaws of this system is that customers have trouble reaching their solution if their problem is uncommon or complicated. By adding machine learning into the mix, the automated support system will drastically improve because patterns can be recognized and machine learning can develop a system that is very similar to a human representative. Machine learning has the potential to vastly improve customer service for banks through automated support systems.
One of the primary purposes of financial service providers is to protect their clients against fraud. However, even with all their efforts, Americans still lose about $50 billion annually to fraudulent activities. Even as cybersecurity continues to develop, thieves are still able to find flaws and break the bank. In order to beat these criminals, banks will need to be one step ahead at all times.
With machine learning, banks will be able to accomplish this by comparing each transaction with the account’s history to determine whether or not the information is fraud or not. Any red flags like large cash withdrawals or out of state purchases that fall out of the customer’s purchasing pattern will notify a banker to confirm the transaction.
Machine learning has the potential to help investment bankers better assess risk because it allows bankers to dig deeper and analyze applicants’ financial status and their current financial information. Compared to the traditional method of checking historic records, this analysis is far more accurate and can provide a prediction that determines the potential risk. AIs have plenty of applications in finance.