Generative AI in Banking Industry: From current state of flux to invigorating mega use cases

Generative AI in Banking Industry: From current state of flux to invigorating mega use cases
Generative AI in Banking Industry: From current state of flux to invigorating mega use cases

AI has already infiltrated numerous banking functions. Chatbots answer customer queries, fraud detection systems analyze transactions, and algorithms power personalized financial products. To ride the full potential of Gen AI, banking industry needs to undergo full set of transformation.

This is an exclusive article series conducted by the Editor Team of CIO News with Sanjeev Joshi, Senior Director at Wissen Technology.

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.

  1. Introduction
  2. What is general AI? Generative AI encompasses algorithms and deep-learning models that can generate diverse forms of content, from images and music to text and code. It is always training itself on vast datasets to learn patterns and structures, making it a powerful tool for businesses looking to automate their creative processes. Unlike traditional AI, which excels at specific tasks, Gen AI aspires to mimic human-level intelligence. Gen AI can learn, adapt, and solve problems in a more generalizable way, making it ideal for complex financial scenarios.
  3. AI in Banking Today: AI has already infiltrated numerous banking functions. Chatbots answer customer queries, fraud detection systems analyze transactions, and algorithms power personalized financial products. However, these applications are often siloed and lack the holistic understanding that Gen AI offers.

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.

  1. Advantages of Gen AI Integration: By integrating Gen AI, banks can achieve:
  • Enhanced Customer Experience: Gen AI can anticipate customer needs and provide seamless support.
  • Improved Risk Management: AI can analyze vast datasets to identify fraud and assess creditworthiness more accurately.
  • Streamlined Operations: Automating repetitive tasks frees up resources for higher-value activities.
  • Data-Driven Decision-Making: Gen AI can extract insights from complex data, leading to better investment strategies and risk mitigation.

While the advantages are enormous, overall, the Gen AI strategy, application areas, and overall percentage spend suggest that the ecosystem is in some sort of flux.

Let us look at the core banking areas, drivers, GEN AI propagation, and what is more to come.

  1. Customer Service: AI Chatbots and Personalized Recommendations
  2. Chatbots for Efficient Support: Imagine a 24/7 customer service agent that understands your questions and resolves issues instantly. AI chatbots powered by Gen AI can handle routine inquiries, schedule appointments, and even provide personalized financial advice. This frees up human agents to focus on complex issues, leading to a more efficient and satisfying customer experience.
  3. Tailored Recommendations: Banks often struggle to recommend the right financial products to customers. Gen AI can analyze past transactions, income patterns, and financial goals to suggest personalized investment plans, savings strategies, and loan options. This not only benefits customers but also leads to increased product adoption and customer loyalty.
  4. Fraud Detection and Prevention: Fraud is a major concern for banks and customers alike. Gen AI can analyze customer behaviour, transaction patterns, and external data sources to detect anomalies in real-time. This allows banks to prevent fraudulent activity before it occurs, protecting both the institution and its customers.

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.

  1. Risk Management: Credit Scoring and Anti-Money Laundering
  2. Credit Scoring and Loan Approvals: Traditional credit scoring relies on historical data, potentially excluding deserving borrowers. Gen AI can analyze a broader range of data points, including financial behaviour and social media activity, to create a more touch points for holistic creditworthiness assessment. This can lead to fairer loan approvals and increased access to credit for underserved populations.
  3. Anti-money laundering (AML): Money laundering poses a significant threat to financial institutions. Gen AI can analyze vast transaction streams, customer profiles, and geographic locations to identify suspicious activity linked to money laundering. This allows banks to comply with regulations and prevent financial crimes.
  4. Predictive Analytics for Market Trends: Financial markets are complex and volatile. Gen AI can analyze historical data and market sentiment to predict future trends. This empowers banks to make informed investment decisions, manage risks, and optimize their portfolios while delivering better returns for their clients.

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.”

  1. Operations Efficiency: Streamlining Processes and Resource Allocation
  2. Automating Data Entry and Processing: Manual data entry is a time-consuming and error-prone process. Gen AI can automate tasks like data extraction, form filling, and document processing, freeing up human resources for more analytical tasks.
  3. Streamlining Account Opening Processes: Opening a new bank account can be a lengthy and tedious process. Gen AI can automate customer onboarding, verify documents, and manage compliance checks. This significantly reduces processing times and improves customer satisfaction.
  4. Optimizing Resource Allocation: Banks often struggle to allocate resources efficiently. Gen AI can analyze data on customer needs, workload distribution, and branch performance to suggest optimal resource allocation. This ensures that resources are directed where they are needed most, leading to improved operational efficiency.

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.

The next wave of GEN AI for banks to improve operational efficiency is embedded in the Algorithmic Loan Underwriter, the Generative Customer Journey, the Proactive Risk Guardian, the Generative Back Office Engine, and the Algorithmic Auditor.

Scope for realignment in the AI strategy

  • Like ERP, CRM, HRM, IT Management, Supply Chain, etc., mega use cases got opened a three decades ago. Such large-scale use cases have yet to emerge with Gen AI, so the so the ecosystem needs to move beyond “POC” to adapt to “Mega Use Case.”
  • Currently concentration of AI spend is on Semiconductors, Cloud infrastructure, Open AI.  AI application spending is very miniscule. AI applications make up less than 10% of the overall spend. It needs to go beyond 25%, at least, to make it a recognizable trend.
  • Enterprise Business Transformation can be done by designing a whole new set of bold workflows for key business processes and not just by automating the existing ones.
  • As Gen AI capabilities evolve, robust ethical frameworks and regulations are needed to address potential misuse and ensure responsible development.

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 unattempt by humans, create new forms of banking, and unlock a new era of innovation in the banking industry”.

Also readThe future of retail is all about tech-driven personalization and convenience, says Amit Kriplani, CTO at ace turtle

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