Financial institutions outlook

The future of banking: How AI is revolutionizing operations

Financial institutions should focus AI efforts on 4 key areas

May 09, 2023

Key takeaways

Loan application processing, compliance, fraud detection and customer service are key use areas.

Loan application processing, compliance, fraud detection and customer service are key use areas.

Be cognizant of AI’s limitations. Concerns include ethical considerations and algorithm bias.

Be cognizant of AI’s limitations. Concerns include ethical considerations and algorithm bias.

Teams need to consider both internal strategies and goals to determine how to implement AI.

Teams need to consider both internal strategies and goals to determine how to implement AI.

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Financial services Generative AI Economics Financial institutions

Artificial intelligence tools such as ChatGPT—though many are still nascent—are rapidly shifting the landscape in numerous industries, including the financial institutions space. The hype around these technologies may have some organizations prepared to run full speed toward implementing them across the entire business, but four areas stand out as transformational opportunities for financial institutions.

Loan application processing, compliance and risk management, fraud detection, and customer service each tends to depend heavily on human capital. Stringent standards and increasing regulations for institutions mean individual employees often spend hours on repetitive tasks. AI will not only streamline those areas and create operational efficiencies, but also help free up those employees to focus on higher-value responsibilities, which will further promote a successful organizational culture and aid employee retention.

Loan application processing

AI can play an integral role in loan application processing, both internally at the bank and in customer-facing functions. Such tools can answer customers’ questions in the initial loan application stage and provide helpful guidance throughout the application process.

During underwriting, AI can also add value by automating tedious tasks while underwriters address more nuanced aspects of the process. Given that the approval process is normally performed by various individuals, AI can also analyze predetermined key metrics to identify risk factors potentially overlooked by a human.

AI will not only create operational efficiencies, but also help free up employees to focus on higher-value responsibilities, which will further promote a successful organizational culture and aid employee retention.
Brandon Koeser, financial services senior analyst, RSM US LLP

Compliance and risk management

A significant area where institutions can leverage AI is compliance and risk management. To ensure that employees obtain a thorough understanding of the compliance requirements, laws, and regulations governing financial institutions, AI can provide the proper training and education.

Further, since most financial institutions follow a manual process to collect data and create regulatory reports, AI can help automate this process and provide report templates. Once an AI tool is trained to understand the data and the activity, it can identify transactions that violate regulations, alleviating employees from performing this process manually.

Fraud detection

Using AI to analyze customer data and transactions can help pinpoint unusual, suspicious or fraudulent activity that may be overlooked by a human. AI can automate the data collection process and significantly improve the speed of response to any of these detected activities. The moment a suspicious transaction occurs, AI will sound the alarm and notify the appropriate financial institution personnel.

This detection can also provide visibility into the more common types of bank fraud, such as credit card abuse, account-opening fraud, overdraft abuse, payer-payee collusion, and account takeover. Using AI to simultaneously mitigate and prevent fraudulent activity can help alleviate not only the oversight burden but the financial loss as well.

Customer service

When dealing with customers, AI can take over some of the simpler tasks, ultimately reducing the number of customer representatives needed. Such tasks can include providing customers with account or loan details, answering routine questions, and providing automated responses to online reviews. With more complex functions, AI can also analyze customer complaints and feedback in a quick and efficient way and summarize the key metrics of the data. This will enable financial institutions to understand trends and pinpoint areas of emerging risk, ultimately allowing them to serve customers better.

While implementing these cutting-edge advancements will almost surely reshape the ecosystem, it is important to be cognizant of AI’s limitations. Crucial concerns include ethical considerations and algorithm bias. AI potentially producing a wrong or inaccurate response poses another risk that institutions will need to address.

While implementing these cutting-edge advancements will almost surely reshape the ecosystem, it is important to be cognizant of AI’s limitations. Crucial concerns include ethical considerations and algorithm bias.
Angela Kramer, financial services senior analyst, RSM US LLP

Acting now

It has been some time since AI capabilities were merely a daydream; a handful of institutions have already implemented these types of tools. To avoid falling behind the competition, management teams will need to consider their internal strategies and their short- and long-term goals to determine the best and most efficient areas in which to implement AI. Key topics for brainstorming include:

  • What areas in your institution can benefit the most from development and improvement? Which of the more contentious issues might AI be able to address?
  • Who are the right people to help address a given problem? Building a team to create the AI solution will require diverse skill sets, some of which are not commonly found at financial institutions. Business analysts will need to collaborate with engineers and data scientists to develop and/or implement the right solution.
  • What data does your institution have that an AI tool might be able to harness? AI teams build solutions based on a treasure trove of data. The more data available, the more effective the tool will be.

Once management has formulated a plan in response to these three main questions, the selected team can dig further into the details and generate a powerful tool that will propel their institution forward as a differentiator in the marketplace.

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