“Why Will Agentic AI Succeed in Credit Automation When Past Technologies Failed?” - A COO’s scepticism that cuts through the hype

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12 November 2025

For once, the promise of AI in banking is compelling. We are examining how recent advancements in agentic AI technology have enabled a more robust, scalable, and institutionally aligned approach to transforming credit operations.

Credit Ops remains costly even after two decades of automation attempts

Commercial banks, on average, report a CIR (Cost-to-Income Ratio) in the range of 40-50%*, with credit operations accounting for approximately 20–25% of the total operational expenditure in commercial and business banking segments. This cost concentration has made credit functions a recurring target for automation and process optimisation initiatives.

To reduce Credit Ops costs, banks have invested heavily in optimising and automating credit operations. The cost of such transformation initiatives runs into millions of dollars for large banks. Over the last two decades, multiple innovations have been introduced, including digitisation, Robotic Process Automation (RPA), and early Machine Learning models, all with the promise of automating credit processes.

Despite these attempts over the last two decades, credit processes remain costly, manual, compliance-heavy and rigid.

Agentic AI: A breakthrough in technology, but banks are proceeding carefully

Before we understand how Agentic AI can solve the problems mentioned above. We need to understand how Agentic AI is different from other solutions, summarising the notable changes we can notice how Agentic AI is distinct from the technologies that preceded it:

No.

Feature

Pre-Agentic Era

Agentic AI Era

1

Ability to handle data

Structured only

Structured + Unstructured

2

Adaptability

Fragile bots

Self-learning agents

3

Workflow Coverage

Siloed tools

End-to-end orchestration

Ability to handle data: By the "Pre-Agentic Era," we refer to the period before the emergence of Agentic AI. Automation technologies were predominantly limited to structured data during this time. Unstructured data, such as annual reports, emails, and scanned documents, required manual intervention, significantly limiting scalability. In contrast, the "Agentic Era" has Agentic AI platforms that are capable of ingesting, interpreting and summarising unstructured data at scale, enabling broader coverage of data points.

Adaptability: In the Pre-Agentic Era, legacy automation tools, particularly RPA-based bots, were highly sensitive to interface and process changes. Minor updates in upstream systems often led to breakdowns, requiring frequent reconfiguration and increasing maintenance overheads, thereby eroding the intended cost benefits. However, Agentic AI is adaptive, i.e., it learns new layouts, adapts to process variations without constant re-coding; LLM-based agents need less upkeep once trained on enterprise data.

Workflow Coverage: In the Pre-Agentic Era, automation was introduced in silos (RPA for data entry, BPM for workflows, imaging for docs). Each tool optimised only part of the process, requiring multiple human handoffs. Agentic AI platforms function as orchestration layers that bridge legacy infrastructure with modern systems. This eliminates the need for fragmented tooling and manual handoffs, enabling end-to-end automation across the credit lifecycle.

Despite the promise, adoption remains a challenge

According to a joint McKinsey & IACPM survey of 44 financial institutions, despite substantial investments, on average, ~93% of financial institutions fail to fully deploy Gen AI across various use cases.

In credit decisioning specifically, approximately 27% of institutions remain in exploratory or pilot phases, with full-scale adoption still rare. A major reason these projects have been put on a back burner has been the lack of a clear and immediate ROI.

Pilot deployments have surfaced critical concerns around data privacy, model reliability, and governance. In the absence of robust controls, generative AI systems have introduced risks related to hallucination, lack of auditability, and regulatory non-compliance, prompting many institutions to delay broader implementation.

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De-Risking Agentic AI in Credit Operations With Galytix

At Galytix, we’ve spent over five years building and refining CreditX, a specialised AI agent for credit and risk professionals.

We have built it in partnership with FIs, who have helped shape our AI agent through their hands-on experience. Solving for the core issues that banks face has allowed credit and risk professionals across 52 countries to trust our product.

  • Traceability: CreditX addresses governance concerns by ensuring full auditability and traceability of data across all automated processes. Each output is linked to its originating source, enabling transparent review and compliance with internal and external regulatory standards. CreditX gives control back to credit officers through complete transparency, traceability, and the ability to personalise/change logic.

  • Constant tech upgrades: CreditX is designed as a continuously evolving platform, with regular integration of emerging technologies and best practices. In this continuously evolving tech space, it ensures the latest tech is plugged in with minimal effort from FIs. For example, with the integration of advanced AI, we improved the turnaround time of OCR-dependent processes by 30-50%.

  • Human Supervised Algorithmic Data Processing: Combining algorithmic processing with human supervision guarantees precise and contextually relevant data.

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Conclusion: The Momentum Is Real, and the Opportunity Is Now

Specialised AI Agents are the promise of a capability that delivers clear and measurable productivity gains, while overcoming the biggest challenges faced by previous technologies, like untrustworthy automations and a lack of intelligent insights.

At Galytix, we are not experimenting. We have built and deployed a specialised agent for 30+ world-renowned banks. With a 20-30% increase in decisioning speed and up to 80-90% automation in performing credit analysis while maintaining full compliance, CreditX delivers on the true potential of AI Agents. For institutions seeking clarity, control and ROI, the path forward is no longer theoretical; it’s available, proven and ready to scale.

Try CreditX now.

Contact us here to know more.

*Source: Accenture. (2024). Commercial banking top trends for 2024. https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Commercial-Banking-Trends-2024.pdf