Smart Banking Solutions Powered by AI – 2022

Artificial intelligence can become the basis for creating the next generation of financial services. Let's look at how AI is already being used by banks and how it will change the customer experience in the future.

Table of contents:

• Why do banks use AI?
• AI use cases
• New opportunities for AI in banks
• Conclusion

Artificial intelligence is already being used by banks to provide services to customers and improve business processes. But AI’s heyday may still be to come. According to various studies, AI will be used for the next generation of financial services. Banks need to develop competencies in AI and other related technologies, such as machine learning, big data collection and processing, open APIs, and so on.

Why do Banks Use AI?

AI in banks has accelerated access to products for many customers and automated some steps in internal processes, which has also affected the speed of service. 

Another reason to use AI is cost optimization. The introduction of AI brings a financial effect of hundreds of millions of dollars annually—both in improved earnings and saved money.

AI Use Cases

• Customer scoring is automatic decision-making applied to  customer applications for loan products. Previously, an application for a loan from a large business was considered for two or three weeks, and this took the time and effort of many different specialists. Now it takes no more than 5–7 minutes from the client's request to the receipt of money. Everything happens remotely, without the use of paper documents, and the percentage of delays has decreased to almost zero.
• Voice assistants and chatbots are used when a client contacts a call center or a bank chat to reduce service time and optimize the work of employees.
• Anti-fraud and financial monitoring is AI used to counter financial fraud by analyzing the atypical behavior of individuals and legal entities.
• ATM Maintenance is when AI predicts ATM occupancy and reduces cash collection costs.
• Document processing uses AI to help banks automatically process and enter customer data by opening accounts and performing banking operations where identity verification is required.

New Opportunities for AI in Banks

From customer risk assessment to service personalization and emotional analysis

Some banks are working on the development of an emotional neural network, as it allows you to determine—without any surveys—whether the client likes the product or service.

The modification of banking services and products in the near future will be based on the personalization of services. Banks must not only meet but also anticipate the needs of each customer at every stage of the customer journey. Only AI can do this. Obviously, the conversion of sales of products personalized in this way is much higher than if all products were offered to everyone in a row.

Personalized offers work like this: a system based on machine learning technologies recognizes behavioral patterns in a client's transactions and his interests in the bank's products and services in a mobile application almost in real-time. Based on this information, the bank remains in the context of the client's life circumstances and offers a product that is really relevant to him.

For example, a sharp increase in spending and a request for a credit score can be markers that a customer is interested in a loan. And a client with free funds who viewed stories about investments in their bank account can be offered an investment product. In other words, banks are beginning to pay more attention to studying the behavior of the client based on the results of his activities on the bank's website or in remote service channels—in order to offer the best product in a timely manner.

With the help of recommendation models, the bank creates personal recommendations for clients: for example, it can remind you of purchases that the client usually makes at a certain time, or, when he sees that the client enters their PIN code incorrectly, promptly offer to go through identification and generate a new one.

Developers strive to make sure that when customers enter a banking application, they see that they have already thought for them and offered them the best solution to the issue. To do this, they focus on the development of personalization technology as much as possible.

AI should become an assistant to the client and at the same time be in a channel convenient for him. Following this logic, banks are launching chatbots based on neural networks and machine learning not only in online and mobile banking but also in Telegram and WhatsApp messengers. It is through these channels that many entrepreneurial clients choose to interact with business partners and discuss key topics that affect their business.

Determining where to open branches

There are banks where artificial intelligence does the bulk of the work in deciding where in the country to open new branches.

The new location intelligence technology is used to manage a network of branches. It aggregates data on all branches and subdivisions of the bank, assesses the potential and workload, and calculates the effectiveness of potential offices based on data on the activity of clients, competing banks, population, traffic on the city streets, and other statistical information. As a result, the bank has a “thermal” map for each city of presence with an assessment of the potential location of a branch at the level of walking (100 m) accessibility.

Determining the best working hours for employees

Other banks are using AI to schedule sales staff. Someone works better in the morning, someone, on the contrary, in the evening. The AI-driven system evaluates sales performance and schedules an employee's schedule in a way that improves their efficiency. Processes are also launched to determine the best time to communicate with the client.

AI is also being used to monitor bank advertising spaces, which greatly improves the effectiveness of advertising campaigns.

Conclusion

AI in fintech is in a period of massive piloting and testing. Today, banks mainly deal with Narrow AI technology, focused on highly specialized applied tasks. So, for example, in chatbots and voice assistants, AI helps to close large blocks of communication with customers, but so far the solutions are not always optimally trained and configured. On the other hand, in some areas, artificial intelligence shows itself as a fairly mature technology: in customer scoring, biometrics, computer vision, and antifraud.

Vital Shpakouski Philologist with higher education, professional translator, former volunteer and teacher, entrepreneur, and salesperson with 13 years of experience. Now I’m a copywriter in Internet marketing, writing about everything that helps businesses grow and develop. In my free time, I create music and songs that no one hears and take photos and videos that no one sees.