Small business owners can expect finance workflows like these to become standard: “At checkout step on a supplier portal, a LoanBot offers a trade credit line with fixed-day terms and dynamic discounting for early payments—embedded directly in the payment flow.” Moreover, Fintech agents would compare terms across loan providers, check the credit history of the business owner, process the application, including KYC and credit funds into the supplier’s account.
AI agents can execute these multi-step complex decision-making tasks autonomously in minutes. This is a complete rethink of user behaviour, customer experience, embedded finance workflows and compliance checks. Moreover, realizing this promise requires more than technological innovation. It depends on enterprise-grade digital engineering, regulatory collaboration and cross-functional integration between financial and technical expertise.
Competing in an Agentic Finance WorldWhen agents become intermediaries, financial institutions that rely on brand loyalty or customer inertia will be most impacted. Why? Because Agentic AI shifts decision-making from emotional or habitual choices to algorithmic optimization. Historically, banks and lenders have benefited from “stickiness”—customers staying with familiar brands even when better options exist. AI agents will continuously scan the market for the best rates, lowest fees, and most favourable terms and execute switches instantly.
Institutions must rethink their value proposition. Competing on convenience or brand alone won’t suffice. They need to design agent-friendly products with transparent metadata, dynamic pricing and embedded incentives that appeal to algorithmic decision-making. In short, the battleground shifts from marketing to machine-readable value.
Barriers to Agentic Finance Growth
According to a recent BCG report, only 27% of banks are truly future-ready. Most institutions still need to resolve technological challenges: fragmented data systems, legacy architectures and a lack of high-quality, domain-specific datasets.
Driving Tangible ROI Through AI-Led Performance Gains
With digital transformation budgets facing tighter scrutiny, financial institutions are under increasing pressure to prove that their AI initiatives deliver measurable impact, such as:
- Productivity gains across the lifecycle: AI-driven tools are improving efficiency across software development and data modernization, as measured by person-month savings and faster release cycles.
- Operational cost reduction: Based on our experience, we have noticed that AI-led automation and generative models are reducing operational costs by 30–50%, with the savings reinvested in modernization programs.
- Cycle time and quality improvements: Organizations report faster prototyping, backlog conversion and testing, along with better deployment frequency, code quality and compliance scores.
Trust: The Foundation for AI Adoption in Finance
Most AI solutions are still in early stages, making them prone to errors, which in autonomous finance can be catastrophic. A miscalculation in loan approval or payment routing could lead to financial loss, regulatory breaches, or reputational damage. The risk is amplified in closed systems where decision criteria are opaque to users. Lack of transparency introduces potential bias, risk-taking behaviours that do not mimic user intention, and unfair outcomes.
Security concerns compound the challenge. Autonomous agents require access to sensitive financial data and transaction capabilities, making them prime targets for cyberattacks. Without zero-trust architectures, strong encryption, and tokenized credentials, these systems could expose institutions to fraud and data breaches.
AI regulations are still maturing. Regulators demand clear documentation of how decisions are made, especially in credit, risk, and compliance workflows. AI systems must provide traceable logs and interpretable outputs to satisfy governance requirements and maintain consumer trust.
Finally, human-in-the-loop models are essential. While autonomy is the goal, oversight ensures ethical decision-making and mitigates systemic risk. Institutions are already implementing hybrid workflows where AI handles routine tasks but escalates exceptions to human reviewers.
Agentic AI is transforming fintech by embedding autonomous decision-making into everyday workflows, delivering speed, personalization, and compliance at scale. Success hinges on robust digital engineering, governance, and trust frameworks to turn prototypes into production-ready ecosystems.
The author is Barath Narayanan, Global BFSI and Europe Geo Head, Persistent Systems.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
