Artificial intelligence (AI) is finding its way in many spheres of modern life and the banking sector is not an exception. Banks use AI to improve the quality of their services and accelerate service provision. However, they are only at the beginning of a long journey. Experts agree that the next generation is going to reap the fruit of contemporary developments in such areas as machine learning, open API, and so on.
AI is currently tested with the help of various pilot projects in the banking industry. The ‘Narrow AI’ technology allows solving narrow tasks and this is the primary technology in use today. For instance, chat bots actively use AI and it helps a lot but it is unable to solve all the tasks that a client might have. On the other hand, AI shows itself as a mature technology in some other spheres such as client scoring, biometrics, computer vision, and antifraud.
How banks use AI
AI accelerates access to many banking products for the clients and this is one of the most important benefits that it brings to the industry. In addition to that, AI helps banks to save millions on operational costs. As a result, the banks are able to make more attractive offers to the clients. Below we provide some examples of the uses of AI in the banking industry.
Client scoring
Client scoring by AI allows making automatic credit decisions. In previous times, if a large business company applied for a loan, several bank officers had to process the application and it took them two or three weeks. Today, when AI considers the applications for loans, the processing takes about 7 minutes. Everything is done remotely and no paperwork is involved. Applicants do not have to wait for an answer for long and this is a great achievement.
Chatbots
Many banks also use chat bots these days. Chat bots accelerate the consultation process and allow the banks to save on operational costs. At the same time, there is certainly space for improvement in this area because chatbots can handle only some issues. In more than 50% of all cases, a human operation is required to help the client with an inquiry.
Antifraud and financial monitoring
Artificial intelligence in banking is also used to combat financial fraud. It analyzes the behavior of a client and sends alert messages if the client’s behavior is atypical. This concerns both individual and corporate bank clients.
ATMs
AI can analyze the workload of each particular ATM and say exactly how much cash has to be put in each ATM. This is one more way for the banks to reduce their operational costs.
Document processing
Some banks use AI to process applications for bank account opening and other financial operations that require client identification. AI is capable of extracting about 70 pieces of data from scans and photographs within two seconds. It can check the client’s identity by approximately 15 different parameters.
From risk assessment to personalized services and emotion analysis
Only a couple of years ago, AI was used mostly for credit scoring, risk assessment, and chat bots. Today, it is also used for personalizing client experience and analyzing clients’ emotions. ‘Emotional’ neural networks allow finding out if the client is happy with the banking services without conducting any surveys.
Personalized banking services is another promising area. Large banks have millions of customers and hundreds of banking products. If they were to offer all their services to all their clients, their productivity would be extremely low. AI, however, is capable of picking the service or the services that a particular client is likely to be interested in based on his or her data. This improves the efficiency of the bank’s work significantly.
Technologies involving machine learning recognize behavioral patterns in the transactions that the client makes, which allows making personalized offers to the client almost in real time. AI analyzes the situation of the client and offers products that should be of interest to the client. For example, if a person has spent a considerable amount of money recently and he/ she has been busy assessing credit ratings of different banks, it can be an indication of the fact that the client is looking for a loan. On the other hand, a client with spare cash reading stories on social networks related to investments might be interested in one of the investment products that the bank has.
AI analyzes the activities of each client on the bank’s website and the mobile application and creates clients’ profiles. Depending on the characteristics of the client’s profile, AI offers this or that banking product to him/ her. What makes this opportunity especially attractive is the fact that no human bank officer is involved in profile creation and product offering.
AI is helpful not only for the bank but for the bank’s client as well. For instance, it can remind the client about a certain purchase that he or she usually makes at a certain point in time. Besides, it can warn the client if it ‘sees’ that he or she is entering the wrong PIN code. If the bank clients feel that the bank cares for them, their affection for the bank will naturally grow.
Another important factor that banks have to take into consideration is the venues that clients use for performing banking operations. Some banks realize that a large number of entrepreneurs like to solve their business issues via popular messengers, for instance. For this reason, they install AI not only on their websites and mobile applications but also on popular messengers.
Location intelligence
AI can help decide where to open a new bank office. The location intelligence technology allows aggregating the information from existing bank offices and using it for assessment of potential workload and efficiency of a prospective new bank office. The technology takes into account the types of clients’ activities, the characteristics of the competitors in the area, the number of the residents, the traffic situation during the day, and so on. As a result, AI offers the most suitable location for a new bank office.
Bank officers’ productivity
Finally, AI can make schedules for bank officers engaged in sales. Some of them work more efficiently in the mornings while others demonstrate higher performance after lunch. It is easy for AI to analyze each worker’s sales results and suggest a personal schedule for each bank officer.
The achievements of AI in the banking sector are already impressive but it still has a long way to go.
