As the financial services industry increasingly recognises the transformative potential of Generative Artificial Intelligence (Gen AI), many institutions are beginning to implement these tools in various capacities. Despite many current applications being small-scale pilot projects, the promise of Gen AI to revolutionise banking operations and enhance customer experiences is becoming clear.
This article delves into some of the most promising use cases of Gen AI in banking, examining how it is already reshaping the sector through advancements in customer service, data-driven decision-making, risk management, and operational efficiency.
By exploring real-life examples and emerging trends, we can better understand how Gen AI is not just a technological innovation, but a catalyst for profound change in the financial landscape.
1. Customer Service Enhancement
1.1 Advanced chatbots
Gen AI-driven chatbots can provide 24/7 support, eliminating long wait times and complicated phone menus. These smart virtual assistants can autonomously handle basic customer requests, such as recommending financial services, showing deposit options, checking account balances, and completing transactions.
For example, Wells Fargo’s Gen AI virtual assistant, known as Fargo and powered by Google Cloud AI, has reportedly handled 20 million interactions since its launch in March 2023 and is projected to reach 100 million interactions annually. The app is designed to answer customers’ everyday banking queries and execute tasks such as giving insight into spending patterns, checking credit scores, paying bills, and offering transaction details.
1.2 Personalized financial advice
AI-based chatbots can also significantly assist the frontline staff by suggesting client-specific actions based on real-time customer interactions and transactions. Gen AI can analyze vast customer data, including transaction history, account balances, spending patterns, investment portfolios, and financial goals, to build comprehensive profiles. This allows banks to enhance their service operations by offering hyper-personalized recommendations based on each customer’s specific circumstances, creating customized financial plans, and providing tailored financial advice and product suggestions.
For example, Morgan Stanley’s AI assistant based on OpenAI’s GPT-4, provides its 16,000 financial advisors instant access to a database of about 100,000 research reports and documents. The AI model aims to help financial advisors quickly find and synthesize answers to investing and finance queries and offer highly personalized insights on the spot.
2. Data-driven decision-making
2.1 Real-time market analysis
There is a rich potential for Gen AI tools to considerably assist in strategic decision-making. For instance, Gen AI tools can analyze market trends, financial data, and economic indicators in real-time to test different scenarios, evaluate new trading strategies, and generate investment recommendations. This helps banks to quickly identify profitable investment opportunities and risks so as to maximise investment returns and minimize losses.
While the potential for Gen AI in these areas is significant, the growing interest is still in its early stages. Jason Napier, Head of European Banks Research at UBS noted in 2023 that “a lot of the potential of AI appears really nascent at this stage”.
2.2 Fraud detection
Gen AI can promptly identify transaction anomalies indicating fraudulent activity, such as transactions initiated from odd locations, unknown devices, or which exhibit unusual spending patterns, and automatically flag potential hazards.
Technologies like Generative Adversarial Networks (GANs) can also create synthetic fraudulent transactions to provide a more diverse set of scenarios for training fraud detection models. This can prove critical in improving the robustness and accuracy of fraud detection algorithms.
Mastercard has recently launched a new Gen AI model to enable banks to better detect suspicious transactions on its network. The technology promises to double the detection rate of compromised cards, reduce false positives by up to 200%, and increase the speed of identifying merchants at-risk by 300%. Mastercard has ample training data for the model, given the 125 billion transactions that pass through the company’s network annually.
3. Risk Management
3.1 Automating KYC verification processes
Banks and other financial institutions need to verify the identity of their clients to avoid commercial relations with synthetic businesses or people involved in fraud, corruption, or money laundering. As part of this process, they also need to satisfy the requirements set by Know Your Customer (KYC) compliance regulations in multiple different countries.
Gen AI can streamline customer due diligence by analyzing large amounts of personal data, reducing onboarding time, false alarms, and enhancing risk assessment while also ensuring compliance with the most stringent anti-money laundering (AML) and KYC regulations.
Airwallex, a global payments company, introduced a Gen AI copilot to accelerate the company’s KYC assessment and onboarding, reducing false-positive alerts by 50% during the KYC due diligence phase and speeding up the KYC onboarding process by 20%.
3.2 Complying with changing regulatory requirements
Keeping up with increasingly frequent regulatory changes is challenging as it requires a vast amount of manual and repetitive work to interpret new requirements and ensure alignment with regulatory standards.
Gen AI can help banks to comply with ever-evolving regulatory requirements by continuously monitoring regulations in real-time, identifying compliance risks, and generating accurate and timely reports.
For example, Citigroup has recently started employing Gen AI to evaluate the effects of new U.S. capital regulations. The bank’s risk and compliance team used Gen AI to sift through and summarize 1,089 pages of new capital rules released by the federal regulators. Moreover, they are looking to use large language models to parse regulations in the countries they operate in to ensure they are compliant in each jurisdiction.
4. Operational Efficiency
Gen AI will boost the productivity of operational staff. Accenture forecasts that by 2028 the banking industry will witness a 30% increase in employee productivity. This makes sense given Gen AI’s ability to understand context, summarize content, generate natural language, and make logical inferences and predictions.
Gen AI can augment the back-office workforce, freeing them up to focus on customers rather than repetitive number crunching. It can do this in various ways:
- Accelerating report generation
- Summarizing data from unstructured documents
- Compressing lengthy reports into pithy summaries
- Speeding up complicated post-trade processes
- Processing loan applications by analyzing data, including the applicant’s credit score and financial history
- Drafting minutes of business meetings and phone conversations
Gen AI will not only augment productivity, but also automate tedious activities and routine tasks.As an MIT Report report noted, “Banks and insurance are among the industries with the greatest proportion of their workforces exposed to potential automation”. Since personnel expenses account for a sizable amount of total costs, the introduction of Gen AI automation into banking operations has the potential to substantially reduce operational costs.
Several banks are already using Gen AI to automate routine tasks:
- Fujitsu and Hokuhoku Financial Group envision using Gen AI technology to generate responses to internal inquiries, create and check various business documents, and build programs.
- OCBC Bank has rolled out a Gen AI chatbot for its 30,000 global employees to automate a wide range of time-consuming tasks, such as writing investment research reports and drafting customer responses. The bank has reported a 50% increase in productivity during the trial period.
- TD launched a Gen AI programming assistant to augment the work of engineers. The AI-assistant is designed to streamline simple tasks and accelerate code development. Engineers can prompt the AI-assistant in natural language and get responses to coding questions, explanations of complex coding topics, and suggestions for security recommendations.
- Morgan Stanley is piloting an AI-based tool designed to create automatic summaries of client meetings, draft follow-up emails, and schedule follow-up appointments.
The bottom line
The rapid adoption of Gen AI in the financial services industry signifies a transformative shift, offering immense potential across customer service, data-driven decision-making, risk management, and operational efficiency.
As banks and financial institutions harness these advanced tools, they are poised to significantly enhance the customer experience, reduce operational costs, and significantly enhance employee productivity.
For management consultants, financial professionals, and high-achieving students, understanding these developments is crucial. Gen AI not only represents a technological leap but also a change in the financial landscape.
Jason Oh is a Senior Manager at TD Bank’s Enterprise Strategy team. Previously, he was at Strategy&, EY, and Novantas as a strategy consultant, where he led / contributed to a range of high-impact projects to improve both top- and bottom-line across the financial services sector.
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