Generative AI in Banking Industry: From current state of flux to...
The financial landscape is undergoing a revolution, driven by artificial intelligence (AI). While traditional AI applications have served the industry well till now, the emergence of general AI (Gen AI) promises to unlock a new level of intelligence and automation, fundamentally reshaping how GenNext banks will operate in the coming decade.
This article sheds light on the current level of propagation of Gen AI in the banking industry and what more is required to claim Gen AI has disrupted the industry. The article also discusses key trends, drivers, and upcoming disruption. The article tries to connect the dots, leading to a high-level strategy for the tech leaders on how to navigate the GEN AI era as various mega-use cases are not yet touched by GEN AI. The article shall help CIO’s, CXO’s, and CDO’s to ride the Gen AI wave & redesign how banking will be done in the future.
Leading Examples
Leading bank JPMorgan Chase is applying Gen AI across various areas, including fraud detection, loan approvals, and even generating reports. HSBC is using Gen AI to enhance back-office tasks and streamline operations. Deutsche Bank is leveraging Gen AI for tasks like risk management and client service automation. Royal Bank of Canada (RBC) is at the forefront of utilizing Gen AI for personalized customer offerings and data-driven insights. Most of the leading banks are using Gen AI in some form or another. A high-impact, high-value use is still missing, and current propagation is at the second and third-tier business processes. Large scale business process re-engineering is yet to happen leveraging Gen AI.
Case Study
Case Study: JPMC Chase implemented a chatbot named “Ask JPMC” powered by AI. The chatbot successfully handles over 50% of customer inquiries, reducing wait times and improving customer satisfaction. Benefit to JPMC: JPMC experiences a significant increase in the number of processed loan applications without compromising accuracy. Faster loan approvals lead to higher customer satisfaction and business growth for small businesses. Gen AI empowers loan officers to focus on building relationships with clients and providing valuable financial advice.
While all this sounds great, chatbots have a long way to go to overcome shortcomings such as limited understanding of context and nuance, difficulty in handling open-ended questions and complex requests, data biases and lack of transparency, limited emotional intelligence.
Example
Example: HSBC uses AI-powered AML solutions to analyze transactions and customer profiles. This has helped them identify and prevent suspicious activity, improving their compliance with global regulations. The industry needs to take on head-on mega-use cases such as ‘Mega risk identification and risk planning,’ ‘Regulatory Compliance on Autopilot,’ and ‘AI-Powered Stress Testing.”
Example: Citibank has implemented AI-powered document processing workflows. This has resulted in a 70% reduction in processing time for loan applications, significantly streamlining their operations.
Scope for realignment in the AI strategy
To conclude, GEN AI is in an initial stage of propagation in the banking industry, but what I see is a future brimming with potential. Generative AI will become a powerful tool for progress, helping banking leaders solve complex problems left unattempted by humans, create new forms of banking, and unlock a new era of innovation in the banking industry.
