The demand for AI-powered solutions in the banking industry has been growing steadily. Why? What value are banks getting from AI that is leading them to increase their investments?
Many banking institutions already employ AI to optimize their internal operations and improve their customer experience. In the next few years, AI is likely to revolutionize this industry even more. AI comes in handy in many spheres: general risk management, cybersecurity, personalization of services, and credit risk decisions. Let’s take a look at how exactly certain aspects of banking will benefit from AI.
When we talk about mobile banking today, we usually mean that people can use the mobile apps of their banks to check balances, transfer funds to third parties, and carry out other routine operations. Thanks to AI, mobile apps will become more proactive, personalized, and advanced. For instance, you might be able to use a voice assistant to send funds to someone and confirm the transaction using Touch ID. You won’t have to access the app and confirm the transaction.
These are AI-enabled conversational interfaces that communicate with clients on behalf of their bank. Modern chatbots can perform dozens of actions, which can include the following:
- Send users notifications
- Inform them about their balances
- Make recommendations for saving money
- Provide updates to credit reports
- Activate cards
- Check account balances
- Withdraw cash
Chatbots can also help clients make informed decisions and raise their bank’s brand awareness. On average, financial institutions save four minutes for each communication that the chatbot handles. In addition, banks save a lot of money because they don’t need to hire and train human support managers and pay them full-time salaries.
Experts say this automation tool can be characterized as “rudimentary” in its current state. In the future, chatbots will become more powerful and versatile. While developers are now teaching chatbots to recognize client’s emotions and react accordingly, in the future they will haveneed to learn to better analyze the context of these conversations.
Data Collection and Analysis
Organizations that rely on Big Data analysis manage to cut down their costs by 10 percent and boost their revenue by 8 percent. How? AI-based apps gather and analyze data to enhance the user experience, thereby reducing the workload of human professionals and automating many routine processes. AI helps banks to onboard new clients. It can auto-fill information and streamline the number of questions a person has to answer. Also, it enables banks to offer personalized services to their clients.
In the future, AI will streamline even more processes, providing seamless interactions between banks and their clients. One of the biggest challenges this technology needs to overcome is privacy risks. The more data a bank collects, the more eager hackers will be to steal it. Institutions will need to invest a lot of funds and effort to predict, analyze, and prepare to mitigate those risks. Security and fraud detection, data encryption, and increasingly more complex verification processes will become one of their highest priorities.
AI helps bankers decide whether they should give a loan to a particular customer. Namely, it does the following:
- Accurately appraises the customer’s credit history to avoid default
- Tracks financial transactions in the client’s mobile banking app and analyzes their user data
- Assesses the customer’s sources of income
- Maintains the confidentiality of the customer’s sensitive data when carrying out all these tasks
As a result, the bank will issue loans only to individuals who can pay them back. In the future, the algorithms for assessing customers’ creditworthiness are expected to become more precise.
Transaction Data Enrichment
Transaction data enrichment employs machine learning and artificial intelligence to turn hard-to-understand data into easy-to-read information. Banks and their clients need this information to know where they spent their funds and with whom. It can also reduce fraud research costs and the number of customer service calls. When a person fails to understand charges on their credit card bill, they can consult the information provided by AI instead of calling the support team.
Moreover, transaction data enrichment puts financial information into context and makes it easier for people to categorize and analyze their purchases. They can plan their budgets more reasonably, analyze their spending habits, and scrutinize their credit scoring. They can more accurately predict their future earning and spending issues. The ultimate goal of this method is to enable people to make the most of data related to their financial transactions.
The most common type of personal data theft today is credit card fraud. To prevent such crimes, AI analyzes the following factors:
- Location of the transaction
- Customer behavior
- Financial habits
Whenever AI detects any atypical activity, it triggers a security mechanism. In the future, AI will learn to take more factors into account, act quicker, and think outside the box. ABI Research experts predict that by the end of 2021, spending on AI and cybersecurity analytics will reach $96 billion.
Compliance in Banking
Banking institutions need to continuously update their work processes to comply with rules and regulations. Before the AI era, banks hired internal compliance teams who would maintain web pages and other internal documents to stay updated with new rules. Today, AI-based software products handle this task much more efficiently. They identify the rules that their banks need to comply with and take relevant measures. Such an approach enables banks to save time and money and reduces the risks that come with the human factor.
What’s next for AI in the banking industry?
In the future, the use of AI in banking is likely to expand dramatically. Artificial intelligence will help banks to remain competitive and increase customer satisfaction. It will continue to help reduce operating costs, maximize security, enhance customer support, and automate processes. A large-scale AI-driven revolution has already begun, and its perspectives look very encouraging.