Agentic AI for financial operations is experiencing rapid transformation from simple macros to Robotic Process Automation, Document Intelligence, Generative AI and now Agentic AI, particularly in underwriting, fraud detection, customer service and various other factors. An era is emerging that promises to revolutionize lending far more profoundly, Agentic AI.
Unlike Traditional AI, which operates within predefined boundaries and relies heavily on analyses data, provides insights and supports decision-making. Agentic AI introduces a level of autonomy that allows systems to learn, adapt, and act in real time. It has the potential to reshape lending operations from credit scoring to loan origination and risk management, autonomously. Agentic automation in finance creates personalized solutions in real time and marks as one of the most significant advancements in the evolution of intelligent financial systems.
In this blog, Megha Kochhar, Industry Principal – Digital Transformation (Financial Services) at Infosys BPM highlights the transition from traditional AI to Agentic AI in financial operations and how it is creating a greater impact than before.
What makes agentic AI different from traditional AI?
Traditional AI interprets data, identifies patterns, and provides insights that support human decision‑making. Agentic AI goes beyond analysis. It acts.
An Agentic AI system can continuously learn from new data, adapt without manual retraining, make autonomous decisions aligned with defined goals, and respond instantly to market and borrower behaviour changes.
For lenders, this means shifting from static decision models to systems capable of dynamic, real‑time optimization; reducing risk, accelerating processes, and improving customer outcomes.
Dynamic credit scoring for a dynamic world
Traditional credit scoring relies on the past data like credit score, liabilities, and income to assess a borrower’s creditworthiness. While effective, it often fails to capture the nuances of evolving borrower behaviour. Real‑time credit scoring using Agentic AI fundamentally redefines creditworthiness evaluation. Assessments are continuously updated using models that learn and adapt in real time.
Real-world use cases
- Real-time behavioural insights
- Proactive risk detection
If a borrower demonstrates improved financial discipline, consistent savings, reduced credit utilisation, or more stable income flows, Agentic AI can autonomously adjust credit scores or loan terms. This enables lenders to serve previously underserved or borderline applicants while maintaining robust risk controls.
Agentic AI can identify early signs of defaults based on emerging patterns across regions, industries, or borrower segments especially during an economic downturn. Agentic AI can reduce exposure in high-risk zones, flag vulnerable accounts and recommend or initiate restructuring.
This ability to act ahead of risk events significantly improves portfolio resilience. Several companies have explored applying Agentic AI in the financial operations.
JP Morgan has integrated Agentic AI and machine learning into credit scoring, fraud detection and customer service.
Danske Bank deployed on AI driven fraud detection system, improving false positives by 60% and fraud detection capabilities by more than 50%, according to AI business.
These implementations show that banks are rapidly adopting Agentic AI. What else can Agentic AI help transform?
Transforming loan origination with autonomous intelligence
Loan origination often involves extensive manual intervention, document verification, and multiple communication loops, all contributing to long approval cycles.
Agentic AI significantly accelerates this process by learning from the previous applications to continuously improve its accuracy and speed.
Agentic AI can,
- Instant pre‑approval through real-time eligibility checks
- Automated document verification using advanced NLP and computer vision
- Direct borrower interactions to clarify missing or inconsistent information
- Continuous learning to reduce exceptions over time
The result is dramatically faster funding cycles and a more seamless borrower experience.
Keeping humans in the loop
Despite its autonomy, Agentic AI serves as a support system for human agents, helping simplify and streamline their work. It enhances human capabilities, allowing them to focus on more complex, judgment-driven tasks.
Human oversight remains essential for:
- Handling nuanced or exceptional borrower scenarios
- Ensuring regulatory and ethical compliance
- Managing high-value or complex loan portfolios
- Conducting periodic audits for neutrality and transparency
Agentic AI aims to improve straight-through-processing (STP) and reduce manual intervention, without removing the critical human element in the lending process. For example, consider a lender using Agentic AI to handle standard applications while human agents handle edge cases such as mixed- income households, striking a balance between efficiency and empathy.
Hyper-personalized lending at scale
Personalization is already a priority for modern lenders, but Agentic AI brings proactive personalization by adapting terms, repayment schedules, or recommendations based on nuanced borrower signals.
Example in action
For borrowers with seasonal income patterns, Agentic AI can:
- Adjust repayment schedules based on seasonal trends
- Detect early signs of financial stress
- Propose or implement modified repayment terms proactively
This helps any type of gig-economy workers with irregular income, as digital lender uses agentic models to tweak EMI schedules automatically, reducing defaults while enhancing customer satisfaction.
Building non-bias, transparent, and compliant AI systems
As autonomy increases, concerns around bias become more significant. Ensuring responsible AI behaviour is imperative. Below listed are some of the safeguard actions to keep it transparent.
Key safeguards
- Bias‑free training datasets: Models must be trained on balanced, representative data.
Tools like Zest AI identify biases, for instance, whether women or minority applicants face disproportionate rejection. - Continuous monitoring: Autonomous decisions should be evaluated regularly for ethical and regulatory standards.
- Policy‑driven decision engines: Embedding regulations into AI workflows ensures compliant decisions such as declines or interest rate changes.
- Scenario-based audits: Stress‑testing edge cases ensure fairness across diverse borrower profiles.
For example, automated bias scans revealed that women‑owned small businesses were being disproportionately denied lines of credit, prompting updates to the training datasets and resulting in fairer lending decisions.
Early adoption is already underway
Global banks are increasingly incorporating advanced AI and machine learning into credit scoring, fraud detection, and customer engagement. Early outcomes show:
- Deeper insights into borrower behaviour
- Higher fraud detection accuracy
- Lower false positives
- Improved approval rates without increasing risk exposure
These trends indicate that Agentic AI is becoming foundational across lending ecosystems.
Agentic AI represents a shift from reactive decision-making to active, self-improving financial intelligence.
For borrowers, faster approvals, personalized terms, fairer and more transparent decisions. For lenders, optimized risk management, greater operational efficiency, stronger portfolio performance and enhanced customer relationships.
How can Infosys BPM help?
BPM in Financial Services brings deep domain expertise and advanced agentic AI-led solutions to accelerate this transformation. Infosys BPM enables lenders to deploy Agentic AI safely, ethically, and at scale.
By combining industry-leading AI platforms with human expertise, Infosys BPM empowers lenders to unlock faster growth, stronger customer relationships, and resilient loan portfolios.
