The Most Valuable Applications of Banking AI in 2023
Automation is finally paying off for banking, financial services, and insurance (BFSI) sector. Think of JP Morgan’s COIN program, which saved 360,000 work-hours and countless instances of human error with automated filing tasks. Or even AI-powered chat, which offered customers answers and informed their decision-making. These examples of artificial intelligence have transformed abundant data into enhanced financial operations, forecasting, and profits.
The challenge now is building upon the successes so far to overcome the hurdles of 2023. Inflation, heightened customer expectations, elevated cyberthreats, and other industry hurdles offer both challenges and opportunities for financial service companies. For those organizations that already have some digital banking transformation strategy in place (that’s at least 60% of the sector), here are some next-level applications of artificial intelligence that can boost efficiency, revenue, and customer satisfaction.
Upgraded Customer Chat
The ability to automatically answer customer questions and requests with speed and accuracy has been a major BFSI goal since the beginning of natural language processing (NLP). At first, banks like Wells Fargo were reliant on platforms like Facebook Messenger for their chat function. Though it had limitations, this capability was a step in the right direction, providing customers with timely responses they’d otherwise have to unearth through web and social channel search.
Since those early experiments, organizations have advanced their capabilities. For example, Bank of America has paired artificial intelligence features with human agents, creating a streamlined handoff. Consumers can get basic information they need on products and services from BoA’s Erica chatbot and answer more complex questions or escalated pain points with the help of live support specialists. However, recent innovation might soon remove human agents from online interactions altogether.
Chatbots powered by the AI-engine can speak to people with all levels of financial understanding and reading levels, offering a wealth of knowledge in seconds. The potential to save money, accelerate resolution times, and enhance customer satisfaction is limitless.
Enhanced Security Measures
The surplus of sensitive and lucrative data within BFSI makes it the third-most targeted industry by hackers. Last year alone, there were data breaches across established banks, mortgage lenders, and mobile payment services that impacted millions of consumers. The participants and targets may change each time, but the song remains the same. The good news is that the industry is gradually hardening their defenses, managing access, and enhancing privacy compliance.
As is, the sector is ahead of the curve, even as it still exhibits clear limitations. In an eight-year evaluation of hundreds of companies’ cybersecurity readiness, BFSI enterprises on average scored a 2.2 out of 5 on the U.S. Department of Defense’s Cybersecurity Maturity Model Certification. Though that still counts as a failing grade, no sector surpassed a 2.5, in part because of the demands of risk management and cybersecurity best practices enterprise-wide. Artificial intelligence could elevate the industry’s defenses.
One area of vulnerability BFSI organizations can streamline is data classification and risk. Traditionally, humans have needed to classify data files by their sensitivity, tagging them under certain categories and criteria to grant them private or even restricted classification. Human error can provide data leakers or hackers with easy wins if they breach lower security systems.
By applying AI-enhanced data classification solutions, businesses can automatically classify data and documents into accurate and secure categories. Some providers have pre-trained machine learning models to enhance the ability and efficiency of AI categorization. This can help BFSI organizations of all sizes to save time, money, and human power on a task these tools can continuously improve over time.
Reduced Risk to Businesses
At the end of the day, industry leaders will always be looking to improve the seamlessness of transactions while lowering risk to financial institutions. Robotic process automation started the journey to broader BFSI automation, empowering organizations to enhance verification checks and online applications to reduce liability or even diminish abandon rates. Now, the technology of artificial intelligence is evolving alongside other trends in digital transformation to further mitigate uncertainty.
A recent example is emerging through an open banking API program. J.P. Morgan and Mastercard have collaborated to create Pay-by-Bank, an open banking tool, to increase the seamlessness and security of bank-to-bank ACH payments. The tool itself allows for the sharing of consumers’ financial data between trusted parties as a way of simplifying and securing these interactions. Where artificial intelligence comes into play is through the use of machine learning models in Mastercard’s Smart Payment Decisioning Tools, which can independently evaluate an individual consumer’s transaction behavior and risk patterns to prioritize important payments, lessening the chance of overdrafts or insuffici