Imagine a delivery partner for Swiggy, who we shall call Anita, who earns ₹38,000 monthly via food-delivery assignments and banks every payout. On paper, she ticks the “stable income” box. But in the past 90 days Anita’s account shows multiple small credits, near-zero balance cycles within days of each payout, and several loan applications submitted within the same week from her smartphone. Neither her bureau score nor traditional underwriting would raise a red flag.
Yet these behavioural and device-driven signals are the kind of “alternate data” that risk and underwriting teams now rely on to uncover hidden credit risk and fraud intent not because Anita is uncreditworthy, but because the legacy model simply cannot see the invisible.
Swiggy facilitated over ₹102 crore in loans to delivery partners in the past 12 months. As this thriving gig workforce becomes a major borrower segment, the question for lenders is no longer ‘can we lend?’ but ‘how do we underwrite safely when the borrower has a thin file and digital footprint becomes the only window?’. This is going to be the new paradigm of India’s gig-credit moment where alternate data and digital footprints are rewriting risk models for first-time borrowers.
India’s Gig Credit Moment: Large Workforce, Thin Files
India’s gig economy is rapidly becoming the new mainstream. According to NITI Aayog, the country’s platform and non-platform gig workforce was approximately 7.7 million in 2020-21 and is projected to grow to 23.5 million by 2029-30. For lenders, this means a swiftly expanding pool of working‐age adults who earn a living, but often depart from salaried employment and established credit footprints.
With such scale comes the “thin-file borrower” challenge: gig workers, micro-entrepreneurs and delivery partners typically lack documented credit history, salaried pay slips or a stable employer ledger. In traditional bureau-based underwriting models, this absence is treated as a warning flag albeit a false one in many cases which results in millions remaining “credit invisible” despite demonstrable earning capacity.
As cash-flow based underwriting and transaction analytics come into the picture, lenders now analyse bank transaction patterns, inflow regularity, spending discipline and buffer balances instead of relying solely on bureau scores. These are key indicators of repayment capacity. At the same time, they leverage digital footprints like device behaviour, login patterns, SIM tenure etc. to infer intent and identity integrity. This merged approach bridges the visibility gap for thin-file borrowers.
Put simply, the borrower no longer needs a previous loan history to qualify. By analysing how she earns, spends and interacts digitally, lenders can score first-time borrowers with a lot more precision.
Digital Footprints 101 : From Capacity to Intent
1. Capacity: Reading the story in the bank statement
Take Rajesh, a Zomato delivery partner in Jaipur. Every week, his account shows predictable UPI credits from the platform and small merchant payments from customers. Between payouts, he pays rent, groceries, and recharges his data pack but crucially, he maintains a modest buffer of ₹1,500–₹2,000 even at month-end. This money management is a signal of repayment capacity. Transaction analytics show lenders that Rajesh lives within his means, maintains liquidity, and pays bills on time which is a stronger credit indicator than any bureau score could offer for a first-time borrower.
Now contrast that with another applicant whose salary account sees multiple inflows from lending wallets, instant withdrawals after each credit, and recurring transfers to new beneficiary accounts. These are subtle but important red flags. They hint at cash-flow stress or loan stacking where a borrower takes multiple short-term loans across platforms to mask financial strain. By analysing such transaction-level alternate data, lenders gain a real-time view of financial discipline by spotting repayment stress weeks before it surfaces as delinquency.
2. Intent: Knowing who’s really behind the screen
Not all risk hides in numbers; some hides in identities. Consider an NBFC that notices a curious pattern of multiple loan applications from different names, all routed through the same smartphone. On paper, each applicant looks unique, but device fingerprinting tells another story as the device ID, IP address, and SIM information all match. This is a textbook case of synthetic identity fraud, where fraudsters reuse devices to apply for loans under different identities.
Device and network intelligence help lenders catch these behaviours early. For instance, a delivery partner’s phone that logs in consistently from the same location and SIM suggests stable identity usage. But if that same device suddenly begins logging in from multiple cities via a VPN, the pattern indicates intent risk and possibly account misuse or credential sharing.
In a lending world where fraud networks are getting more organised, digital footprints provide a behavioural map of authenticity.
3. When capacity meets intent: A complete view of risk
The most effective credit risk models blend both layers of capacity from financial transactions and intent from device behaviour. Think of two Swiggy partners applying for small-ticket loans:
- Asha earns irregularly, but her average monthly inflows are consistent. She pays bills early and maintains balance stability. Her device has a single SIM, steady geolocation, and regular app activity.
- Rohit, on the other hand, shows similar inflows but frequent SIM swaps and a device previously used to apply for loans under two other names.
Asha represents a good borrower in an unconventional income model. Rohit represents predictability with deception i.e., the kind of risk that only alternate data can expose.
4. From static scoring to continuous vigilance
Traditional credit scoring is like taking a photograph; it freezes a moment in time. Meanwhile, digital-footprint-based lending is like watching a live video; it shows movement, change, and emerging signals. Lenders today can monitor patterns like sudden drops in balance, spikes in UPI activity, or new device fingerprints appearing post-disbursement continuously. Each deviation tells a story, allowing real-time credit monitoring and intervention before losses occur. This evolution from static bureau checks to continuous, signal-based risk management marks the next chapter in India’s lending transformation.
Frequently Asked Questions
1. How is alternate data reshaping credit access for gig workers in India?
Alternate data enables lenders to assess borrowers who lack traditional credit histories, such as delivery partners, freelancers, and micro-entrepreneurs. Instead of relying solely on bureau scores, lenders analyse transaction behaviour, earnings consistency, and digital footprints including UPI patterns, payout cycles, and device stability. This provides a more accurate picture of both repayment capacity and intent. As a result, millions of previously unscorable gig workers are gaining visibility within the formal credit ecosystem, often receiving fairer loan terms and faster approvals.
2. What types of alternate data do lenders use to assess credit risk?
The most common alternate data sources include bank transaction analytics, mobile payment history, telecom usage, platform earnings data, and device intelligence. For instance, consistent inflows from gig platforms, regular utility bill payments, and a stable device ID history can all indicate financial discipline and authenticity.
These signals are used to supplement or replace missing bureau information, allowing for a multi-dimensional risk assessment. In digital lending, the combination of financial data which indicates capacity and behavioural data which indicates intent provides the most complete risk visibility.
3. How do embedded credit models work on platforms like Swiggy or Zomato?
Embedded credit allows borrowers to access financing directly within the digital platforms they already use without filling out external loan forms or visiting banks. For example, Swiggy’s restaurant partners can access working capital loans through their merchant dashboard, while delivery partners receive in-app credit offers linked to their payout cycles.
Lenders use real-time platform data to determine eligibility and risk, while repayments are automatically deducted from platform settlements.
This approach reduces friction, speeds up approvals, and aligns lending closely with the borrower’s actual earning behaviour and has become a key innovation in platform-based embedded finance.
4. How does device intelligence help in detecting fraud and synthetic identities?
Device and network analytics play a critical role in verifying digital identities and preventing fraud. By tracking parameters like device ID, IP address, SIM tenure, and geolocation consistency, lenders can identify anomalies that signal synthetic identities, loan stacking, or credential sharing.
For example, if multiple loan applications originate from the same device but under different names, the system flags potential fraud. This device-level visibility helps lenders block high-risk profiles before disbursement by moving fraud control from post-event investigation to real-time prevention.
5. How do lenders maintain fairness and transparency when using alternate data?
Fairness in credit decisioning hinges on explainable AI and consent-based data access. Lenders must ensure that algorithms distinguish between genuine income volatility (common among gig workers) and actual risk. Under India’s Account Aggregator (AA) framework, borrowers explicitly consent to share their financial data, ensuring transparency and control. Moreover, explainable model design allows lenders to justify credit decisions and prevent bias against informal earners. The goal is to use data not to exclude, but to empower inclusion through accountability.
6. What are the biggest risks in embedded lending models, and how are they mitigated?
The biggest challenges include data governance, overexposure from rapid disbursals, and cross-platform fraud rings. To mitigate these, lenders employ dynamic risk models that track borrower behaviour across payout cycles and device clusters.
Consent-based frameworks ensure ethical data use, while real-time monitoring and early warning systems (EWS) detect anomalies before defaults occur. Additionally, embedding regulatory disclosures and transparent repayment terms builds user trust which is a critical factor for sustained adoption of embedded credit models.
7. Can alternate data models replace traditional credit bureaus entirely?
Not yet, nor should they. Bureau data remains valuable for understanding long-term credit behaviour, but it’s often incomplete for new-to-credit or informal borrowers. The future lies in hybrid models, where bureau information is complemented by alternate signals such as transaction flow, digital activity, and device behaviour. This blended approach provides the accuracy and inclusivity that modern lending demands.
Rather than replacing bureaus, alternate data expands the lens through which lenders evaluate creditworthiness, making the invisible visible.
Conclusion: From Invisible Profiles to Intelligent Credit
When we first met Anita, our Swiggy delivery partner in the introductory example, whose account showed a stable income but volatile transaction behaviour. Her case represents more than one borrower’s story; it captures the defining challenge of India’s credit transition of visibility without context.
In a system built on legacy scores, Anita’s financial rhythm would have been invisible in a series of inflows and outflows dismissed as noise. But through alternate data and digital footprint analytics, that same noise becomes a narrative and her earning patterns reveal capacity, her transaction behaviour reveals discipline, and her device identity reveals authenticity. Together, these signals tell lenders something the bureau never could: not just what she earns, but how she manages it.
Platforms that once simply connected customers and workers are now gateways to financial inclusion. It offers lenders the ability to interpret alternate data with precision, balance automation with judgment, and uphold governance with speed. This transformation is the essence of India’s embedded credit revolution.