The banking sector now mainly consists of computers and networks, which might be shocking to some. Most of the world’s wealth is stored in databases and each transaction is simply executed through the Internet by exchanging information in real time. Yet, no matter how impressive or frightening they may seem, artificial intelligence technologies aim to further revolutionize the banking systems, especially when it comes to relationships between banks and customers.
For some strange reason, we often find ourselves needing to contact the bank to solve a problem during times when they are closed – be it on public holidays or weeks. Our money’s not sleeping, so why should the banks do that?
Fortunately, one of the most interesting applications of artificial intelligence in the banking system is the implementation of virtual assistants, called chatbots, to engage with customers 24 hours a day, 7 days a week. This is not exactly the case everywhere, but in some countries, customers rely on chatbots even for private conversations regarding banking transactions, services offered by the bank and other activities that do not necessarily require human interaction.
For example, Bank of America introduced Erica, an AI assistant which helps its customers with their transactions – a move that has seen the bank’s ROI increase. Many banks have quickly followed suit, although the results are mixed.
In addition to customer support on individual transactions and services, banks have also achieved good results by using chatbots to inform customers of their additional offers. Many may not be aware of the full catalog of services that the bank offers and, through a targeting system, some banks use their virtual assistants to engage customers at appropriate times.
This is possible through a system that is the basis of marketing called segmentation. If creating a huge general catalog of customers was foolish in the past, it’s even worse nowadays! Using certain tags and behavioral analysis of the customer, which is achieved with the help of AI, banks are able to offer the right suggestion to the right customer, responding to their needs in financial terms.
The banking sector is one of the most highly regulated sectors of the world economy. Governments use their regulators to ensure that banks comply with a myriad of regulations that require them to know their customers, prevent money laundering, protect customer privacy, monitor bank transfers and comply with a number of additional regulations.
That’s why banks are looking for intelligent assistants who can monitor transactions at all times, keep an eye on customer behavior and record information for the various compliance and regulatory systems.
Fraud prevention through data analysis is already having a significant impact on credit card underwriting processes. With tools that automatically monitor customer behavior and habits, artificial intelligence-based systems help banks maintain regulatory compliance on a daily basis, minimizing risk.
Banks also use artificial intelligence systems to make more informed, safer and more profitable decisions. At present, many banks do not yet use credit scores, do not study credit history, and do not analyze customer references to determine whether an individual or company is worthy or not.
These systems are far from perfect and are often full of errors. Therefore, in addition to using the data already available, AI-based decision-making systems can also be more efficient than traditional systems, in particular through their constant review of customer behavior so that their credit history can be assessed.
However, there is an internal challenge to be solved: the use of artificial intelligence systems for lending decisions could lead to several discriminatory problems. It will, therefore, be necessary to “train” the AI systems that will be used, in order to eliminate any kind of negative influence. It would, therefore, be necessary to let go of algorithms based on logic conditions and exploit machine learning as much as possible.
Among other things, it should be noted that it’s already hard to explain the reasons for loan refusals for a bank employee, let alone when a neural network will be making the decision.
The banking sector is largely operational in the digital world, but it is still full of processes controlled by humans that are sometimes heavy, both in terms of work and in terms of operational costs and human error risks.
Artificial intelligence in the banking sector is therefore applied to eliminate much of the work and errors, in combination with process automation tools – increasingly used in back-office operations to manage a wide range of workflows. By replacing the human process with automation, banks can achieve greater control where it was not possible before. According to the data, banks that have implemented this process have seen a saving of 20-25% in their operations.