AI in Banking: The New Frontier of Financial Services Innovation
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Last month, Open AI and BNY, USA’s oldest bank, signed a multi-year agreement. Open AI aims to test its models with real-world complex tasks. But the interesting point here is BNY’s willingness to invest in generative AI. Specifically, wanting to leverage intelligence tools like Deep Research to strengthen its in-house AI platform Eliza, which was built to enhance client service and operational efficiency and drive a company-wide cultural transformation. Financial institutions have been trying to leverage AI for a few years now. But today, there is greater emphasis and focus on AI even as the technology continues to evolve and improve, opening up new avenues for innovation.
Current Applications of AI in Banking
Over 60 percent of CEOs are open to incurring significant risks in order to leverage advanced AI and automation strategies, as they consider AI to be a competitive advantage. Many banks have been using AI to improve customer engagement and personalization efforts. For example, HSBC has been leveraging AI to transform customer service with round-the-clock support, personalized, accurate responses, and shorter wait times. Their AI platforms leverage Natural Language Processing (NLP) to handle a range of queries, ranging from requests for account information to loan applications. Citibank is leveraging generative AI to strengthen its wealth management services. Wealth.com, is an estate planning platform that uses gen AI to transform its earlier manual, error-prone, and expensive wealth management practice. The platform can extract relevant data from multiple estate-related documents in just 95 seconds and provide wealth managers with possible outcomes or scenarios, as well as recommend next steps.
Organizations have also been using AI for improving customer support – Bank of America’s virtual assistant Erica is a good example. And some, like Goldman Sachs and Lloyds Bank, are using AI to assess loan applications, understand risks, and improve credit scoring. Other banks like JP Morgan Chase and Wells Fargo have been using AI for real-time fraud detection and risk management. AI-powered processes have helped them improve risk detection and mitigation efficiency, reduce operational costs, and improve their security posture.
From Chatbots to Wealth Management: The Expanding Role of AI
Effective use of AI can improve the banking sector’s profits by 9 percent to reach USD 170 billion by 2028. Now is the time for some well-thought-out, robust AI strategies that take advantage of the tremendous advancements and diversification of the technology.
Of course, most financial institutions today are focusing on leveraging generative AI for automating routine tasks and improving productivity. A report by Citigroup states that "nearly two-thirds of all work done in banking and insurance has a high potential for AI-driven automation or augmentation." The profit boost will be primarily from productivity gains, as AI helps automate routine tasks and augment human capabilities. ?
The natural progression of generative AI is voice AI that has the potential to transform voice-based customer support. After all, the banking and financial services sector contributes 25 percent of total expenditure on global contact centers. Voice AI systems can help banks scale up quickly to meet high demand, ensure 24/7 availability, and break language barriers. For example, it can communicate just as easily in Spanish with a customer from Mexico as it can in English with a customer from UK. It can deliver a far better customer experience by communicating in the customer’s language and remaining available for as long as they need. And it can do all of this at a fraction of the cost of human employees.
Agentic AI is the next big thing in AI progression. Agentic AI agents can operate autonomously and make independent decisions without human intervention or prompts. In the banking context, agentic AI can transform the way banks carry out deal management, pricing personalization and management, advisory services, and customer service. In addition to talking in the customer’s preferred language, agentic AI systems could also quickly analyze their account information and financial behavior to offer hyper-personalized, real-time advice on managing their money.
Challenges Banks Face in Adopting AI
The future of AI in banking is exciting and full of possibilities but adopting AI strategies is not without some challenges. The biggest concern for banks right now is that of data privacy and security. AI runs on data and a single breach somewhere in the ecosystem can compromise sensitive customer and enterprise data. Bad actors are also increasingly leveraging AI to launch sophisticated attacks that banks must be equipped to address. Regulations on the use of AI are still a work in progress as it is a new and fast-evolving technology. Banks must keep pace with changes in regulations and new standards coming into force.
The newness and fast-changing nature of AI also pose some considerable human resource challenges. Banks need skilled engineers who understand not just the technology but also how it can be applied in banking and financial services. And at the same time, organizations need to be cognizant of AI bias that can happen if models are not trained with diverse data sets. AI systems must be used in as transparent and fair manner as possible, and banks must be prepared to oversee decisions or recommendations made by AI systems, especially in areas like lending or credit scoring.
One of the most significant challenges facing banks today is their legacy core systems. These systems power some of the most fundamental and crucial banking processes but lack the agility and robustness required for implementing AI strategies. Data is often siloed within these systems, which is bound to hinder the effective application of AI. And replacing or modernizing legacy cores is expensive, time-consuming, and highly risky.
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Preparing for the AI Era in Banking
Banks must now focus on strengthening their data management and governance practices to ensure ensure data quality, security, and compliance with global data protection regulations. Robust cybersecurity measures that are also powered by AI can help them mitigate the privacy and security risks. AI-powered security systems can detect threats in real time and automate responses for quicker and more efficient risk management.
Ongoing training and reskilling initiatives will help address some of the staffing challenges. This includes upskilling employees in data science, machine learning, and AI ethics, as well as fostering a culture of continuous learning. And banks must also establish clear guidelines on the ethical and secure use of AI with a strong focus on transparency and accountability.
The question of the right technology foundation is possibly the easiest challenge to solve, as banks no longer need to overhaul their legacy cores to implement AI. All they need to do is deploy a robust, cloud-native, microservices-based middleware solution that can sit over their legacy cores and power AI-based innovation.
This is the AI era, and it holds tremendous possibilities and potential for growth and innovation for the banking and financial services sector. Banks must now recalibrate their strategies and technology infrastructure to cash in on emerging AI trends to improve operational efficiency, productivity, and customer engagement.
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