10 game changing use cases of AI in banking and financial services sector
In recent years, the world of banking has undergone a significant shift with the emergence of Artificial Intelligence (AI). AI has become a game-changer, revolutionizing both retail and wholesale banking sectors. Just like there are "digital natives" who grew up with technology and "cloud natives" who live in the world of cloud computing, there are now "AI natives." These are people and institutions that are experts in using AI, and they're leading the way in the banking industry's AI revolution. This revolution is now even further intensified with advent of generative AI.
As per a recent report from ‘Verified Market Research’, AI in Banking Market size was valued at USD 5.13 Billion in 2021 and is projected to reach USD 64.03 Billion by 2030, growing at a CAGR of 32.36% from 2023 to 2030. Banks, both retail and wholesale, have embraced AI and technology to offer a wide range of convenient services to their clients. Here are some areas AI is going to transform banking and financial services marjket.
Online and Mobile Banking: The rise of online and mobile banking, augmented by AI, has revolutionized the banking experience. AI-driven personalization tailors user interfaces, offers customized financial advice, and provides intelligent chatbot assistance to customers. This along with customer insights are the top 2 areas where AI is heavily used. E.g. Absa bank is investing into conversational UX by taking their chatbot to the next level of maturity using GenAI.
Customer Insights: AI-driven customer relationship management (CRM) systems analyze customer data to offer personalized marketing, customer service, and product recommendations, improving customer retention and satisfaction as well as improving the cross-sell revenue for the banks. GenAI has shown immense potential in this area. E.g. Deutsche Bank is looking to leverage GenAI for insights to financial analysts, driving operational efficiencies and execution velocity.
Payment Processing Systems: AI is integral to payment processing systems, detecting fraudulent transactions in real-time, optimizing routing for cost-efficiency, and enabling voice-activated payments. E.g. SWIFT in collaboration with other technology firms is building a foundational technology platform capable of powering Gen AI solutions for cross-border payments.
Cybersecurity and Data Protection: Close on the heels or the above 3 areas, robust AI-backed cybersecurity measures are increasingly adopted by the banks. Advanced intrusion detection systems and threat intelligence, protect banks from cyber threats, safeguarding sensitive financial information and transactions. E.g. AI in Cybersecurity Market is projected to hit USD 96.3 Billion at a 22.50% CAGR by 2030 as per a report by Market Research Future.
Regulatory Technology (RegTech): To meet complex regulatory requirements, banks today heavily employ AI-driven RegTech solutions for automated compliance processes, regulatory reporting, and adherence to evolving financial regulations. E.g. RegTech in Finance Market Size is projected to reach USD 44.54 billion by 2030, growing at a CAGR of 22.6% by a recent report from Straits Research.
Risk Management Tools: Wholesale banks employ advanced risk management technology to assess, monitor, and mitigate financial risks. This includes managing credit risk, interest rate risk, and market risk as well as now the ESG risk. Complex financial instruments like derivatives are used for risk hedging. AI enhances risk management by providing predictive analytics, scenario modeling, and stress testing, helping banks assess, monitor, and mitigate complex financial risks. E.g. ANZ Institutional has incorporated AI into a business intelligence tool used by frontline bankers to stay abreast of market developments and associated risk.
Core Banking Systems: Core banking systems are the heart of retail banking technology. These software platforms manage customer accounts, process transactions, and maintain financial records. Often these are on legacy technology stack. Banks are now looking to infuse GenAI for quicker migration of these systems to new tech stack. They are also leveraging AI to efficiently manage the data for customer accounts, process transactions, and maintain financial records. E.g. Temenos – one of the leading core banking platform has embedded AI/ML capabilities into their banking platform.
ATMs (Automated Teller Machines): Banks are now investing in AI-equipped smart ATMs to provide customers with highly personalized experiences, including predictive cash withdrawals based on spending patterns and facial recognition for secure cardless transactions. E.g. Lloyds Bank has tapped a startup for digital identity solutions that leverage AI for identity fraud.
Trading and Investment Platforms: Wholesale banks engage in trading activities across various financial markets. Sophisticated trading platforms and algorithmic trading systems enable them to execute large-scale transactions efficiently. AI-driven trading platforms help provide real-time market data and analysis, optimizing trading strategies. E.g. Citi is looking to use AI to personalize their treasury and trade solutions teams.
Capital Market Infrastructure: Wholesale banks are deeply involved in capital markets, participating in initial public offerings (IPOs), managing bond offerings, and providing liquidity to financial markets. This involves the use of technology for underwriting, settlements, and market making. Like core banking systems, these systems are on legacy tech stack. Banks are now exploring to leverage AI in these systems for streamlining the handling of complex financial instruments as well as making movement to the new tech stack quicker. E.g. HSBC in partnership with AWS developed an AI-based index investment process.
As banks transition into the AI era, a multifaceted transformation is imperative. For example, investment in advanced AI technologies, robust data infrastructure, and cybersecurity is paramount. The change banks need to adopt is not a mere integration of AI tools but a profound reconfiguration of their organizational DNA. This holistic approach empowers banks to navigate the digital landscape, staying competitive and relevant while delivering enhanced services and experiences to their customers in an increasingly AI-driven world. Collaborative effort between the banks and the technology companies who bring in the expertise in these areas will actively shape the future of the banking industry to meet the challenges and opportunities of the digital age head-on.
Ram Khizamboor
Ram Khizamboor is Chief Delivery Officer — BFSI at LTIMindtree.
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