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A Guide to AI in Banking and Financials Services

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Emerging AI Use Cases in Banking in 2024 – A Comprehensive Outlook on the Future of Financial Services

In 2024, the banking sector is witnessing a paradigm shift, propelled by the integration of Artificial Intelligence (AI). This transformative journey is characterized by AI's multifaceted role in enhancing customer service, refining fraud detection methodologies, and reimagining banking operations using Generative AI technology such as a banking chatbot. This article provides an in-depth exploration of the emerging AI use cases in banking, focusing particularly on the transformative roles of chatbots and generative AI.

How are Banks Leveraging AI in Banking in 2024?

McKinsey & Company forecasts that by 2030, AI could potentially generate up to $1 trillion in annual value for the banking sector. This optimistic outlook is underpinned by the diverse applications of AI in various banking functions.

  • Fraud Detection and Prevention: AI is a game-changer in the fight against financial fraud. Banks are deploying AI-powered systems that can identify and prevent fraudulent transactions in real-time. These systems analyze patterns and flag anomalies, ensuring rapid response to potential threats and safeguarding both the bank's and customers' assets.

  • Risk Management: In risk assessment, AI plays a crucial role, particularly in credit scoring. AI-driven models provide a more nuanced and accurate assessment of credit risk, leading to better-informed lending decisions. This advanced approach to risk management is crucial for maintaining the financial health of banks and protecting the interests of borrowers.

  • Investment Advice: Robo-advisers, powered by AI algorithms, are providing personalized investment advice. They take into account an individual's financial goals, risk tolerance, and investment horizon, offering tailored investment strategies. This democratizes access to investment advice, previously the domain of high-net-worth individuals.

Revolutionizing Banking Interactions and Customer Support with AI

The integration of AI into customer service, especially through a banking chatbot, marks a significant change in the banking experience. The banking chatbot impact is significant, expecting to save approximately $11 billion by 2023​​, underlining their significant impact on operational efficiency and customer satisfaction by delivering responsive, efficient, and personalized customer interactions. These chatbots represent a leap in customer engagement and operational efficiency. They are not merely reactive tools; they are proactive agents in understanding customer needs, offering personalized solutions, and enhancing the overall banking experience.

The transformation in customer support driven by AI is profound. AI-driven tools are enhancing the efficiency and quality of customer interactions, crucially shaping a more responsive, customer-focused banking environment. This shift is more than technological; it represents a new era of banking where customer experience is paramount, and personalized service is the norm.

Generative AI: Pioneering Banking Operations and Data Management

Generative AI is at the forefront of banking innovation, redefining operations and data management. This technology is pivotal in changing how banking operations are conducted and how data is managed, projecting an addition of $200 billion to $340 billion annually to the banking sector.

For example, Citibank's use of generative AI in 2023 to create synthetic financial data for risk modeling and stress testing is a testament to this. These innovations encompass a wide range of applications, from automated financial advice generation to sophisticated credit risk assessments.

Generative AI's ability to create and simulate data and scenarios is instrumental in enhancing risk management, developing new financial products, and improving decision-making processes. This aspect of AI not only optimizes existing procedures but also paves the way for novel approaches in handling complex financial scenarios.

Recent Breakthroughs in Generative Banking Use Cases

Deutsche Bank is leveraging deep learning technology to analyze the investment portfolios of its international private banking clients, identifying those who may be over-invested in specific assets. The bank aims to align customers with appropriate funds, bonds, or shares, with human advisers delivering AI-generated recommendations, subject to regulatory compliance. Kirsten-Anne Bremke, the global lead on data solutions at Deutsche's international private bank, expressed enthusiasm for the synergy between artificial and human intelligence.

In a parallel development, JPMorgan is pursuing a similar initiative. The bank submitted a patent application in May for a ChatGPT-like tool designed to assist investors in selecting equities. This information comes from a source familiar with the project but not authorized to speak publicly. The project is still in its infancy.

Morgan Stanley is also embracing AI, allowing various business units to experiment with open-source large language models. These models, trained on vast amounts of internet text, are being utilized for diverse applications within the firm. In April, Morgan Stanley announced a patented AI and deep learning model to interpret communications from the Federal Reserve as being either hawkish or dovish, aiding in the prediction of monetary policy directions. Yuriy Nevmyvaka, head of the bank's machine learning research group, highlighted the importance of this technology for every business, trading desk, and investment group, noting its operation in a secure and controlled environment within the bank.

Wells Fargo is implementing large language models to streamline regulatory reporting for clients and enhance business processes. Chintan Mehta, the chief information officer and head of digital technology and innovation at the firm, noted that this technology not only reduces repetitive tasks but also accelerates compliance processes.

French banks are also integrating AI into their operations. BNP Paribas employs banking chatbots for customer inquiries while utilizing AI for fraud and money laundering detection and prevention. Societe Generale's Cast system, operating in 26 languages, uses its computational capabilities to monitor for potential misconduct in capital markets. It processes a staggering 2.5 million hours of conversation and 347 million emails annually, according to the bank.

Experience a banking chatbot in action

The implementation of AI chatbots in the banking sector provides a practical demonstration of their capabilities. Sendbird's tutorial on building a FinTech and banking chatbot powered by ChatGPT demonstrates how these tools can be seamlessly integrated into banking services, providing an interactive and responsive customer service experience. They exemplify how technology can be employed to improve service delivery, making banking more accessible, convenient, and user-friendly.

In addition, to see a banking chatbot in action, Sendbird's SmartAssistant AI demo offers a practical demonstration. This resource showcases the capabilities and impact of a banking chatbot in real-world scenarios.

Conclusion: A Forward-Looking Perspective

The integration of AI in banking is a clear indication of the sector's evolution towards more innovative, efficient, and customer-centric services. AI, particularly through chatbots and generative AI, is not just enhancing operational efficiencies but is also paving the way for more secure, personalized, and engaging banking experiences. As AI continues to evolve, its role in the banking sector is expected to expand, revealing new opportunities and challenges.

In conclusion, the future of banking in 2024 and beyond looks promising, with AI playing a central role in its evolution. The emerging AI use cases in banking are a testament to the industry.

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