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The new competitive edge for lending and fraud prevention, ETCISO
The conversation around risk assessment and fraud prevention has shifted fundamentally. It is no longer defined by how many loans can be disbursed or how quickly customers can be onboarded. Today, lenders must consider risk intelligence above all else.
India’s digital financial ecosystem has grown in scale and scope as adoption, products, and participants have increased. Digital payment transactions accounted for 99.8% of the total transaction volume in India in the first half of 2025.Meanwhile, bank fraud losses surged to ₹36,014 crore in FY25, a 194% increase over the previous year.Given these numbers, the traditional rule-based safety net isn’t just fraying, it’s rapidly becoming obsolete.
The Death of the Rulebook: Moving to Predictive Intelligence
For years, lending in India operated on a rigid “if–then” framework: if a borrower’s CIBIL score crossed 750 and income met a defined threshold, approval followed. While straightforward, this model excluded millions of creditworthy New-to-Credit (NTC) borrowers and proved inadequate in detecting increasingly sophisticated fraud.
The competitive edge now lies in Predictive and Behavioural Modelling. The industry has moved beyond static data to analyse alternative data streams. In India, this means leveraging UPI transaction flows, utility bill payment consistency, and even digital footprints.
By applying machine learning (ML), lenders can assess risk with greater precision and consistency. Recent analyses indicate that AI-driven credit models can increase automated approval rates by 30%–50%, while reducing loss rates by 10%–25%.3 With such advances in predictive intelligence, the emphasis is shifting from relying solely on a static, months-old credit report to evaluating a borrower’s current financial behaviour and future potential.
Unmasking the Invisible: Graph Intelligence & Fraud
Fraud in India has evolved from simple phishing to industrial-scale operations involving synthetic identities and mule accounts. Investigations in 2025 revealed over 8.5 lakh mule accounts linked to cyber-fraud syndicates.4 Traditional systems, being limited to known patterns, are reactive and struggle to keep up with the rapidly evolving cyberthreat environment.
This is where Graph Intelligence comes in. It treats data points as “nodes” and their relationships as “edges.” By visualising these networks, AI can detect “clusters” of suspicious activity that look like normal behaviour in isolation.
Graph Neural Networks (GNNs) detect fraud by shifting from a traditional siloed view of transactions to a connected, relational view of data. Instead of evaluating single events in isolation (such as checking if one payment is too large), graph technology maps the relationships between users, accounts, devices and IP addresses to uncover complex, coordinated fraud rings.
Hyper-Personalization: Turning Risk into Relationship
Although hyper-personalisation is usually seen as a marketing tool, in 2026, it is a risk management powerhouse. By understanding a borrower’s specific behaviour, lenders can intervene before a default happens.
AI leverages vast customer behavioural data to predict loan defaults more accurately. AI algorithms also provide more granular insights into customer behaviour, enabling financial institutions to offer more personalised solutions. In fact, using AI for customer segmentation can lead to a 10% increase in recoveries, while reducing operational expenses by 40% and increasing customer satisfaction scores by 30%.5
The Ethical Guardrails: The FREE-AI Framework
The release of the RBI’s FREE-AI committee report in August 2025 has set the gold standard for how these technologies should be deployed. The “Seven Sutras” or foundational principles, including fairness, accountability and explainability, must become core to product design for the BFSI sector.
However, the challenge is twofold:
- Bias Mitigation: Fintechs must ensure that the algorithms don’t inadvertently discriminate against specific demographics or regions due to biased training data.
- Explainability (XAI): When a loan is rejected by an AI algorithm, the reason for the rejection should be explainable in human-logic terms.
The New Competition Edge
The shift to AI-powered risk intelligence isn’t just about security; it’s also about the bottom line. This is partly due to the massive efficiency gains that AI-powered fraud detection systems offer. Research shows that such systems can help achieve detection rates of 87%-94% with 40%-60% reduction in false positives compared to traditional rule-based approaches.
In the 2026 lending landscape, the winners won’t be those with the most capital, but those with the best risk intelligence. By moving from reactive rules to proactive AI, Indian fintechs can expand financial inclusion, protect their margins and build a more resilient financial future. The goal is clear: build systems that are “Understandable by Design” and “Safe by Default.” For the BFSI sector, AI is no longer a luxury—it is the very engine of survival.
The author is Prasanna Kumar, Chief Credit Officer, Axio.
Disclaimer: The views expressed are solely of the author and ETCISO does not necessarily subscribe to it. ETCISO shall not be responsible for any damage caused to any person/organization directly or indirectly.
