Exploring Agentic AI's Impact on Bank Profits: A Wise Investment?

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An Intriguing Crossroads for Asian Banks

Asian banks currently find themselves at an intriguing crossroads. They are grappling with increasing business complexity. This complexity arises from the expansion of new asset classes, digital channels, and ecosystem partnerships. Additionally, the challenges of softening interest rates, credit pressures, and geopolitical uncertainty loom large.

The Rise of Technology Investment

In response, technology investment, particularly in AI, has become a focal point. It’s all about driving efficiency, resilience, and revenue growth. Recent surveys from IDC reflect a notable rise in AI-related spending throughout the region. Thus, the focal question isn’t if banks should invest in AI. Rather, it’s about how this investment can yield a measurable return on investment (ROI).

The Role of Agentic AI

Agentic AI promises banks a path from mere experimentation to real returns. By deploying autonomous AI agents, banks can transform complex processes like credit decisioning and risk management. This approach accelerates decision-making, ensures consistency, and scales automation while still adhering to governance.

What Is Agentic AI?

Agentic AI in the banking sector involves AI systems comprising multiple autonomous agents. These agents can independently analyze data, make informed decisions, and execute actions within workflows. Unlike traditional AI, which offers recommendations, agentic AI orchestrates end-to-end processes.

Why Banking Is Ripe for Agentic AI

Banking processes are inherently complex, often involving multi-stage decisions and risk assessments. They already employ probabilistic model-driven decision engines, making them suitable for agentic AI architectures. Thus, the potential for streamlining is immense.

Agent	Function
Credit Check Agent	Specializes in assessing credit against risk acceptance criteria.
Exposure Estimator	Calculates the maximum unsecured exposure underwritten by the bank.
Supervisory Agent	Evaluates outcomes and operates as the decision approver.

Challenges Banks Face

Despite the potential, there are challenges to overcome. Firstly, banks need to ensure their data and infrastructure are up to scratch. Patching data during trials might be feasible, but won’t suffice in live production. Additionally, scaled AI systems demand dedicated infrastructure.

Case Study in Credit Approval

Consider a streamlined credit approval process. It employs multiple agents to manage credit checks, exposure estimations, and final approval. This not only expedites decisions but also ensures consistency and governance.

Keys to Unlocking ROI

Selecting the right use cases is crucial. Many banks dabble in exploratory AI projects, driven more by enthusiasm than potential business value. Success requires anchoring initiatives in measurable business outcomes.

Building Trust and Governance

The AI trust deficit remains a formidable barrier. Overcoming this demands robust governance frameworks, transparency, and continuous monitoring. Thus, a human-in-the-loop approach is pivotal.

Transforming Investment into Dividends

The potential isn’t just theoretical. Numerous case studies affirm that agentic AI offers substantial opportunities to the banking sector. According to the IDC FutureScape report, by 2027, the AI-driven innovation in Asia-Pacific banks will increase significantly.

Conclusion

Banks that seize the opportunity now—by focusing on impactful use cases and ensuring readiness—will reap measurable outcomes. IDC is actively working with banks to help them navigate this transformative journey confidently.

Register for IDC’s live webinar here on 24 February 2025.