To grow their user base, banks will need to implement AI to power their analytics capabilities to deliver smart, personalized solutions.

The banking sector has been developing rapidly in the past few years. As select banks and fintechs evolved, clients’ expectations have risen, primarily due to neobanks that have tailored their services to their customers’ specific needs. Today, clients expect seamless omnichannel experiences and personalized services.

In order to improve processes and meet customer expectations, machine learning (ML) and artificial intelligence (AI) gained the spotlight as they allow financial institutions to gain better insights into how important it is to automate a large number of processes. According to McKinsey research, more than 89% of top-level bankers believe that ML and AI will become the key components of their business in the coming years.

Since many of our clients come to us with questions on how to utilize ML and AI to grow and improve their fintech services, we’d like to provide the rundown on how to include these technologies in your digital processes.

Where to Use AI in Banking?

According to McKinsey’s report, AI technology has the power to help baks to boost their revenues using increased personalization and customization, lower costs and more efficient processes and create new opoortunities for developing better services. Howevr, this is only possible when AI is used in the right way and in sectors where in this moment it can make the biggest difference.

Here, we want to share a few examples that show how big banks levergaed the power of AI and ML to boost their business and be ready to respond to all customer’s demands.

1. Digital Support for Investments and Mortgage

ML and AI are a big part of gaining insights into investment banking, and learning what tech-savvy clients want their banks to offer can be the difference between high client satisfaction and retention rates and customer turnover.

For example, the Royal Bank of Scotland found that many of their potential clients found the process to secure a bank-issued mortgage long and tiresome. The bank aimed to solve this problem by creating an AI-powered platform that works with digital mortgage support and chatbots to ensure the accuracy of information presented to the customer. After implementing these digital mortgage tools, the bank saw a 10% increase in call length and 20% improved customer Net Promoter Score (NPS).

With the help of AI software that identifies investment data patterns and trends, banks can offer valuable investment recommendations to their clients. While the software doesn’t guarantee an investment’s success, it does make it easier for clients to understand investment trends and make better decisions with the available information.

2. Improved User Experience

When large banks have a clear vision of their future development, it allows them to make bold, data-driven decisions. For AI and ML to bring value to an organization, the business must be a part of a digital ecosystem that has the ability to provide sufficient data for AI and ML tools to show their full potential.

For example, by using AI to analyze enormous data sets of their customers’ journeys for loans, purchases, cards, and buying insurance, the State Bank of India better understood how to create a platform that could serve 64 million customers.

On their YONO platform, the State Bank of India continues to use AI, automation, rules, and cloud-based solutions to ensure their customers can receive real-time insights and so they can offer a frictionless and comprehensive user experience for all of their customers.

3. Advanced Data Analysis

Manual errors are inevitable in vast amounts of data. However, AI-based decision management systems lower these risks by creating different logic streams. By using vast amounts of information about clients, banks can use AI to create new tools that would support the decision-making process in volatile situations.

Additionally, when it comes to banking, nothing is more important than security for users, especially during payments and transfers. The Austrian bank, Raiffeisen, recently employed a new AI-based solution called Advanced Analytics Workspace, which is an explorative, on-demand analytical environment.

The Advanced Analytics Workspace provides analysts with a secure, cloud-based analytical environment that is user-friendly and fully automated. Now Raiffeisen’s analysts can choose from a variety of analytical tools, enjoy unlimited storage and CPU, and use cloud capabilities.

Moving Towards Modernized Banking

Since the COVID-19 Pandemic, the finance sector has witnessed some dramatic changes in customer behavior. With the higher demand for digital banking services, financial institutions have had to improve their infrastructure with omnichannel experiences and use AI to create scalable platforms for data analytics to give users a more secure and satisfying experience.

Financial institutions will need to continue to find new ways to drive value for their users. Whether that means introducing new types of services, using advanced bots to help users select the right investments or mortgage, or implementing other services that would appeal to new generations of consumers.

It is clear that AI and ML are here to stay. These powerful technologies are capable of forever changing how financial institutions use and understand data to offer their clients a better experience.

About the Author:

Sofija Vidjikant
Sofija Vidjikant is a Content Writer at Softjourn. As a creative extrovert, Sofija found herself in the intersection of marketing and technology and invests time in researching as well as producing high-quality content in the tech, finance, and ticketing industry.

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