Artificial intelligence (AI) and machine learning (ML) can help financial organizations to get a competitive edge, increase customer satisfaction, and make more money. Read this article to get to know what exactly AI and ML can do for banks.

AI and ML technologies are rapidly evolving. More and more banking institutions rely on such solutions and are constantly looking for new ways of applying them. From this article, you'll get to know about the spheres and challenges of the banking sector where AI and ML come in especially handy. Plus, you'll discover growth predictions for such technologies and advice on integrating them into the workflows of a financial organization.

AI and ML Trends and Statistics

Here are a few numbers that should help you to realize the increasing significance of AI and ML solutions:

• $30 billion is the current global market value of the AI technology

• By 2025, its value is expected to reach $126 billion

• $6.67 billion was the estimated value of AI in the fintech sphere in 2019

• By 2025, the estimated value of AI in the fintech sphere is expected to reach $22.6 billion

The demand for AI and ML was growing fast enough, but the COVID-19 pandemics gave it an additional boost. People need solutions that will keep working even during lockdown times. Consumers want fast and personalized services. Financial institutions that appreciate this breakthrough opportunity will try to monetize it earlier than their competitors.

Below, we'll analyze those areas of application of AI- and ML-based solutions in the banking sphere that seem to have the best perspectives.

Process Control and Optimization (PCO)

Process control and optimization means that companies get rid of manual work and automate as many tasks as possible. Such an approach enhances the overall productivity of businesses and accelerates their workflows. PCO can penetrate various company departments.PCO can penetrate various company departments, from accounting to sales and company operations, from employee training to customer services. In the coming years, AI finance software will be able to communicate to clients more effectively, better analyze big data, and generate more insightful reports.

Customer Service

By employing AI in their customer service, companies strive to achieve two goals: provide a personalized client approach and minimize response time. AI- and ML-powered chatbots can answer customers within a few seconds after they submit a question to the chat. A high communication speed improves customer retention, and clients become more likely to close the deal. If a business doesn't make users wait, they won't get disappointed and won't turn to its competitors.

Plus, smart chatbots can gather large amounts of information about consumers thanks to performing the following types of activities:

• Collecting customer responses in chats

• Accumulating data from social networks

• Reviewing websites

• Sending personal emails to specific audiences for feedback

Businesses can analyze this information to improve their products and quality of service.

Robo-Advisors

These are not synonymous with chatbots. Robo-advisors can carry out the following types of tasks:

• Provide automated portfolio management

• Come up with personalized product recommendations with little to no human supervision

• Collect information from clients about their financial situation and goals

• Offer advice to consumers

• Adjust the marketing approach to the needs and preferences of each individual

For instance, if a robo-advisor knows that the client has a baby, it might recommend that the parent should start saving money for the kid's education. The current generation of solutions needs to enhance its accuracy and their developers need to make sure they always remain ethical.

Credit Scoring and Churn Prediction

Today, most financial institutions rely on outdated credit scoring systems that don't employ AI. They make decisions based on the information from customer bases that contain the following facts about clients:

• Age

• Demographics

• Marital status

• Possible preferences

Such a system can target consumers but would fail to collect any valuable data about them.

AI- and ML-powered credit scoring and churn prediction software, by contrast, strives to analyze real clients. It can reduce the number of lost customers by 45 percent and empower the whole marketing and sales campaign. These campaigns will take into account real consumer preferences and target each individual very precisely.

AI- and ML-powered keep in mind the following parameters of each consumer:

• The history of lending operations

• Debts

• Financial behavior

It helps the algorithms to understand where they should approve a loan to the consumer or not. In the future, the process of reviewing a loan application might take just a few hours if not minutes. AI might be able to boost the revenue of a financial institution by 37 percent and reduce non-performing loans up to 53 percent.

Security

Statistically, 95 percent of security issues and data breaches happen due to human errors. AI can rather easily prevent these problems as well as predict vulnerabilities in banking systems and eliminate them. Plus, it can help businesses withstand cyberattacks that get more frequent, sophisticated, and target-diverse each year. By 2025, 60 percent of companies are expected to report fraud during the last year. Fraud is a particularly burning issue for the lending domain. To prevent it, AI can detect issues within accounts, scrutinize documentation for account registration, and take other measures.

How to Integrate AI- and ML-Powered Solutions in the Workflow of a Financial Organization

If a business has never used AI before, its top managers might find it challenging to decide what to start with. It would be wise to single out just one solution that the company currently needs the most — such as a chatbot system or AI-based tools that help to prevent lending frauds. The company needs to integrate that solution and let its staff and clients get used to it. Then, the business can add another AI-powered tool into its workflows. AI and ML are complicated technologies that have just begun to evolve. Thanks to the step-by-step approach, companies should be able to make the most of these innovations.

Final Thoughts

Hopefully, you found this article informative and now you better understand the importance and perspectives of machine learning in banking. AI and ML can increase the security of banking operations, automate tasks, and accelerate business processes. They can improve customer service and maximize the efficiency of credit scoring. AI and ML can give banking businesses a significant competitive edge and help them earn more money.