Self-service that delights customers: How the IBM Partner Ecosystem is harnessing generative AI assistants in the banking and financial sectors

A data scientist at a midsize hedge fund told BI that generative AI models are a “superpower for coders.” One of his biggest use cases is solving coding problems in different coding languages. “It enables executives to get information in a comprehensive way faster, which allows you to make your decisions faster and quickly move toward execution,” Donahue said. AlixPartners, which advises private-equity firms, uses a proprietary, AI-powered diagnostics tool that draws on decades of the firm’s consulting work to help buyout shops evaluate potential acquisition targets.

These capabilities enhance the efficiency and accuracy of compliance processes, allowing financial institutions to respond proactively to regulatory requirements and potential risks. Additionally, LLMs can assist in training and onboarding by generating educational materials and interactive simulations for employees. Fintechs remain at the forefront of harnessing gen AI and many of their use cases and solutions are impacting financial services. For example, Synthesia utilizes an AI platform to create high-quality video and voiceover content tailored for financial services, while Deriskly provides AI software aimed at optimizing compliance in financial promotions and communications. Banks, nonbank financial services players, and FinTech or Big Tech are at different stages of the journey to harnessing Gen AI capabilities. Established financial institutions are experimenting with Gen AI use cases, initially in marketing and sales, customer support, risk and compliance.

Overlay data and analytics capabilities

What they did do, however, was allow people to focus on the more value-adding parts of their jobs. The first is the implementation costs — building out new apps, training them, integrating them into existing systems, testing them, putting them into ChatGPT production and so on. That all takes massive amounts of computing power, loads of data and access to highly skilled people. Centers of excellence may help balance that cost in the initial phases but will likely slow adoption in the long run.

• SaaS and cloud banking provider Temenos launches Responsible Generative AI for banking, promising enhanced data management, productivity, and profitability with secure, explainable AI solutions.
• As we navigate the transformative era of AI in financial services, it is evident that AI is not merely a technological upgrade but a catalyst for profound disruption across products, processes and operations in the sector.
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• Strong use cases will include “high-touch” activities historically owned by people, which leverage large datasets or require a generative response logic.
• OpenAI — the company that created ChatGPT — estimated 80% of the U.S. workforce would have at least 10% of their jobs affected by large language models (LLMs).

But, at the same time, they worry that the enterprise adoption of a new technology might create new attack vectors. While centralization streamlines important tasks, it also provides flexibility by enabling some strategic decisions to be made at different levels. This approach balances central control with the adaptability needed for the bank’s needs and culture and helps keep it competitive in fintech.

Current industry applications of LLMs: Overview of LLM use cases in financial services

While the human brain is excellent at reacting to immediate information and making decisions, GenAI can take a bird’s-eye view of an entire information landscape to surface insights hidden to the naked eye. This capability is useful for pairing customer caches with historical trend data to inform risk assessments or flag anomalous transactions indicative of potential fraud. BBVA has ChatGPT App already begun deploying 3,000 ChatGPT Enterprise licenses among Group employees in a bid to increase productivity and process efficiency, while stimulating innovation across the Group. The enterprise version of ChatGPT delivers the utmost security and privacy, combined with its unique ability to generate content or answer complex business questions, among numerous other features.

Among the use cases for gen AI at Bank of America outlined by Bajwa is improving developer efficiency and productivity within the bank’s large engineering organization of more than 10,000 developers. He also noted that it can help knowledge workers more efficiently ingest and process information by enabling knowledge discovery and summarization. Future potential use cases in customer-facing recommendations and automating customer service, generative ai use cases in banking though the bank is still in the early exploration phase for those types of applications. Burris at Bank of America leads a team of more than 30 data scientists, data engineers and reporting analysts who develop AI models that can decide, in real time, which transactions are highly suspicious and therefore should be blocked. Burris estimates this protects the bank and its customers from about $100 million worth of fraud every year.

To address these challenges, banks are also investing in robust AI governance frameworks, continuous monitoring and auditing, stakeholder engagement, and adherence to ethical guidelines and regulatory standards, she said. Additionally, board oversight can be complicated by a lack of clear regulatory direction, according to EY data. Regulators have expressed concern about embedded bias in algorithms used to make credit decisions and chatbots sharing inaccurate information, the firm said. On the flip side, GenAI’s ability to generate highly plausible, human-like communications is also making it easier and cheaper for criminals to defraud banks. GenAI could enable fraud losses to reach $40 billion in the U.S. by 2027, up from $12.3 billion in 2023, according to Deloitte’s Center for Financial Services’ “FSI Predictions 2024” report.

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The financial services industry has had several similar moments in the past decade, yet not all of them have resulted in true transformation. For example, blockchain in 2017 led many to believe it would completely revolutionize banking, but nearly a decade later, it remains a theoretical interest. “Banks are naturally cautious in embracing generative AI to the full and want to ensure they do so responsibly. A proven track record in delivering responsible AI will be vital for financial institutions to confidently experiment and deploy generative AI models for critical business functions across the enterprise. In Financial Crime Mitigation, the AI assists business users in extracting intelligence from data, enabling quick cataloging of information such as financial crime alerts. This can identify themes or root causes that would take humans much longer to analyse manually.

Deliver consistent and intelligent customer care with a conversational AI-powered banking chatbot. Sustainable energy consumption, costs and regulations are also challenges when it comes to generative AI. If your organization is ready to explore the possibilities of IBM watsonx Assistant and related technologies, try watsonx Assistant for free or embed watsonx in your solutions. This 2024 IBM IBV CEO Study revealed that product and service innovation is CEOs’ top priority for the next 3 years, with generative AI opening the door to a new universe of opportunity. Leon now handles more than 97% of customer conversations without requiring redirection to human agents. As a result, Generali Poland is saving approximately 120 person-hours monthly and has shortened customer consultants’ working time by one hour per day.

In 2024, 58% of banking CIOs surveyed reported they had already deployed or are planning to deploy AI initiatives this year, according to Jasleen Kaur Sindhu, a financial services analyst at Gartner. A shift to a bot-powered world also raises questions around data security, regulation, compliance, ethics and competition. Since AI models are known to hallucinate and create information that does not exist, organizations run the risk of AI chatbots going fully autonomous and negatively affecting the business financially or its reputation. As the first European bank to form an alliance with OpenAI, BBVA is leading the way in integrating disruptive technologies within the financial sector. This collaboration will enable OpenAI to share its expertise and help BBVA unlock the full potential of generative AI, setting a benchmark for other financial institutions. Identifying a use case necessitates substantial effort in prioritization, cost-benefit analysis, and strategic considerations regarding technology and data architecture.

• The first billion interactions took four years, but the second billion were achieved in just 18 months, indicating a significant increase in client engagement.
• Ensuring that their AI systems do not violate privacy, prevent bias from creeping in, and remain secure keeps enterprise CXOs awake at night.
• Currently, there is a growing need among Indian banks to utilize Gen AI-powered virtual agents to handle customer inquiries.
• Generative AI assistants are an ideal entry point for organizations in the financial and banking sectors looking to gain a foothold in this exciting new world.

There is high momentum for using AI technology, including GenAI tools, for fraud detection and regulatory compliance. Machine learning can be used to analyze data in real time to look for unusual patterns and flag new fraud tactics. GenAI is used to model normal banking behavior and identify activities that deviate from the norm, enabling banks to spot emerging threats. BBVA’s dedication to data and technology is evident through its decade-long establishment of AI Factories in Spain, Mexico, and Türkiye. As we navigate the transformative era of AI in financial services, it is evident that AI is not merely a technological upgrade but a catalyst for profound disruption across products, processes and operations in the sector.

Understanding the cost

By extracting valuable insights, detecting patterns, and recognizing correlations, AI algorithms can help identify potential risks and market disruptions that may impact financial institutions’ operations and investments. This enables institutions to make informed decisions, take proactive measures, and manage their risks effectively in response to changing market conditions. Today, banks of all sizes have access to a considerable amount of customer data that’s processed and stored on a daily basis, from credit history to buying activity. The integration of AI into the cybersecurity framework of the banking sector encapsulates the technology’s dual nature as both a potential risk factor and a critical defensive tool.

Gen AI is now catalyzing a significant shift, with 78% of surveyed financial institutions implementing or planning Gen AI integration. You can foun additiona information about ai customer service and artificial intelligence and NLP. Around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness. Globally, institutions foresee a 5 to 10 year timeline for full automation harnessing, strategically investing in areas with immediate benefits, such as customer service and cost reduction.

In a competitive landscape, banks are constantly seeking to reduce costs, pioneer new products and services that gain customer support, and advance their market share. Banks are increasingly adopting generative AI to elevate customer service, streamline workflows and improve operational efficiency. To seize the GenAI opportunity, banks should reimagine their future business models based on the new capabilities GenAI enables and then work backward to prioritize near-term use cases. New AI-enabled capabilities across the business can create new opportunities to monetize data, expand product and service offerings, and strengthen client engagement. We have also set up a responsible AI taskforce comprising senior leaders from multiple disciplines to assess and address these risks prior to any use case being deployed in production. Our existing responsible data use framework for AI continues to provide us with guardrails as we look at new use cases.

It now handles two-thirds of customer service interactions and has led to a decrease in marketing spend by 25%. Rather than reactively engaging when customers have a request or issue, it could eventually anticipate and proactively reach out to customers before they even know something is wrong. AI engines can help with detecting frauds in large datasets by looking for correlations and trends. This can help financial institutions prevent security detect fraudulent activity before it becomes a major problem.