AI Use Cases

Five Agentic AI use cases in banking and financial services industry

Agentic AI benefits and five transformative use cases in banking and finance for operational efficiency.

Secufusion Inc.

The Agentic Workspace Company

Agentic AI Use Cases Transforming Banking and Financial Services Industry

In this multi series blog we began identifying key applications in the banking and financial industry that can benefit from AI integration.

In the second blog we discussed The Role and Impact of Agentic AI in revolutionizing banking and Finance and in the third and final blog of this series we shall discuss benefits of Agentic AI in banking & finance and five Agentic AI use cases in banking and financial services industry that is bound to transform the operational efficiencies.

Benefits of Agentic AI in Banking & Finance

  • Enhanced Customer Experience: Agentic AI can provide highly personalized services by understanding customer preferences and behaviours in real-time. This leads to tailored financial advice, automated portfolio management, and proactive customer communication and satisfaction.

  • Operational Efficiency: By automating complex processes such as loan processing, compliance checks, and fraud detection, Agentic AI reduces manual labour and speeds up operations. This allows banks to handle higher volumes of transactions and services more efficiently, and reducing the need for manual intervention, helps banks cut operational costs and allocate resources more effectively.

  • Risk Management: Agentic AI continuously analyses market data and identifies potential risks, enabling banks to make proactive decisions and mitigate threats. Agentic AI can analyse market trends and execute trades or investment decisions in real-time, optimizing financial strategies and potentially leading to better returns.

  • Innovation and Competitive Advantage: Banks that leverage Agentic AI can stay ahead of the competition by offering innovative services and adapting quickly to changing market conditions. Agentic AI systems can scale operations to meet growing customer demands without compromising on service quality or efficiency.

  • Enhanced Compliance: Agentic AI ensures that banks comply with regulatory requirements by continuously monitoring transactions and generating compliance reports, reducing the risk of violations.4

Five Agentic AI use cases Transforming banking and financial services industry: 

JP Morgan Chase has implemented an advanced AI system to streamline the loan approval process. This system uses machine learning to analyse various data points, such as credit history and transaction data, to enhance the speed and accuracy of credit worthiness assessments. This reduces manual processes and improves overall customer experience.5

Bank of America's AI-powered financial assistant, Erica, uses natural language processing to offer personalized financial advice and customer support. Erica helps customers manage their finances more efficiently and answering inquiries in real-time.6

BNP Paribas has partnered with Mistral AI to implement Agentic AI for backend automation and cyber security. This aims to automate complex processes and enhance security measures, improving operational efficiency and reducing risks.7

BBVA has deployed Chat GPT with Open AI to enhance customer engagement and provide personalized services. This application of Agentic AI helps the bank offer tailored financial advice and support to its customers, improving overall satisfaction and loyalty.

Franklin Templeton is working with Microsoft to build an advanced financial AI platform. This platform leverages Agentic AI to automate historically labour-intensive processes, such as portfolio management and financial analysis, enabling the bank to operate more efficiently and effectively by expected cost benefit of 48%.8

Conclusion 

In summary, Agentic AI is moving beyond simple automation to enable intelligent, autonomous systems that can drive customer-centricity and growth.

And the banking and financial sectors continue to solidify their reputation as pioneers in adopting cutting-edge and bleeding-edge technologies.

Their forward-thinking approach has not only positioned them as trendsetters but has also created meaningful impacts in countless ways, shaping industries and driving innovation for the future.

References:

Other References:

  • I.
    O'Neil, C. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
  • II. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi. A survey of methods for explaining black box models.
  • III.
    Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V & Vallée, T. AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
  • IV. Reports from regulatory bodies like the Financial Stability Board (FSB) and the European Banking Authority (EBA) on AI in finance.
  • V. Regulations such as GDPR and CCPA.
  • VI. Gartner and Forrester reports on AI trends in customer experience
  • VII. Biggio, B., & Roli, F. Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 183-195.
  • VIII. ”How AI is impacting the dynamics of Financial Services” by Raj Singh
  • IX. “Navigating the realities of AI in Financial 1services” by Theodora Lau
  • X. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330-347.
  • XI. European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act).
  • XII. Publications on robotic process automation (RPA) and AI integration in banking