Using Device and Telco Data for Credit Risk in India
Device and telco data help Indian lenders assess borrowers who have no bureau history. Signals like handset value, SIM tenure, recharge patterns, app usage, and repayment behaviour become features in an alternate-data credit model — with borrower consent — letting NBFCs approve thin-file customers that a bureau-only policy would decline.
India has more than 117 crore wireless connections — 1,171.91 million as of July 2025, per the Telecom Regulatory Authority of India (TRAI), via IBEF. Yet over 160 million Indians remain credit-underserved (TransUnion CIBIL). The mobile phone is the one asset almost every borrower carries — which is why device and telco signals have become a practical foundation for lending to people the credit bureau cannot see. Platforms like YuALT, which has supported 10 million+ credit journeys, let credit teams build these models without writing code.
Why Do Lenders Need Device and Telco Data at All?
Traditional underwriting depends on a bureau score. That works for salaried, urban, formally-banked borrowers — but a large share of Indian applicants are new-to-credit (NTC) or thin-file: gig workers, kirana owners, first-time borrowers, and rural households. The new-to-credit share of loan originations has stayed subdued as lenders turn cautious (Business Standard), leaving demand unmet.
Device and telco data fill the gap. A borrower with no loan history still generates a rich behavioural trail: how long they have held a SIM, how regularly they recharge, whether their handset is entry-level or premium, and how they use financial apps. These signals correlate with stability and repayment intent — the two things a lender most wants to predict.
What Device and Telco Signals Actually Predict Credit Risk?
Not every data point is useful. The signals lenders rely on cluster into a few families:
Signal category | Example features | What it proxies |
|---|---|---|
Device attributes | Handset price band, device age, storage tier | Income stability, asset ownership |
SIM & telecom tenure | Months on current number, recharge regularity | Address/identity stability |
Recharge & spend | Recharge amount, prepaid vs. postpaid, top-up frequency | Cash-flow rhythm, liquidity |
App footprint | Presence of banking, UPI, utility apps | Financial engagement, formality |
Behavioural metadata | Charging patterns, screen-time regularity | Routine, employment consistency |
A stable number beats a new one. A SIM held for three years with steady monthly recharges signals a settled borrower; a two-week-old prepaid connection does not. Postpaid is a soft positive. Qualifying for a postpaid plan is itself a telecom-underwriting decision the lender can piggyback on.
How Does AI Turn These Signals Into a Credit Decision?
An AI credit model ingests hundreds of consented device and telco features, weighs them against historical repayment outcomes, and outputs a risk score. With a no-code platform like YuALT, a credit analyst — not a data-science team — selects the data sources, trains a scorecard, checks fairness and stability, and pushes it into the loan-origination flow. The model keeps learning as new repayment data arrives, so the scorecard sharpens over each lending cycle. This is the same alternate-data approach explained in our guide to alternate-data credit scoring in India and how lenders score thin-file borrowers with no credit history. (This is an educational explainer, not legal advice.)
What Are the Consent and Compliance Rules?
Device and telco data are personal data. Under the Digital Personal Data Protection (DPDP) Act, 2023, a lender must obtain free, specific, informed, and unambiguous consent before collecting or processing this data, state the purpose clearly, and let the borrower withdraw consent (Ministry of Electronics and IT). The RBI Digital Lending Directions, 2025 reinforce this: data collection must be need-based, disclosed, and auditable, with borrowers able to see and delete their data (Reserve Bank of India). Practical rule: collect only what the model uses, log every consent, and never scrape data the borrower did not agree to share.
How AI Helps
AI makes device and telco data usable at scale. Manually reviewing recharge logs or handset attributes is impossible across lakhs of applications; a model does it in milliseconds. YuALT lets credit teams combine device, telco, banking, and other alternate-data sources for NBFC credit scoring into a single scorecard — built and adjusted without code. The result: thin-file borrowers get a fair decision, approval rates rise without loosening risk appetite, and every feature stays consent-backed and auditable, so the model holds up to both credit-committee and regulatory scrutiny across the full portfolio.
FAQ
Q1. Is device and telco data legal to use for credit scoring in India? Yes, provided the lender obtains valid consent under the DPDP Act, 2023, and follows the RBI Digital Lending Directions on data collection and disclosure. The data must be need-based and the borrower must be able to withdraw consent.
Q2. Can telco data replace a credit bureau score? It complements rather than replaces the bureau. For thin-file and new-to-credit borrowers with no bureau record, device and telco signals may be the primary basis for a decision; for others they add an extra risk layer.
Q3. What is the single most predictive telco signal? SIM tenure and recharge regularity tend to be strong stability proxies. A long-held number with consistent top-ups signals a settled borrower, which correlates with lower default risk.
Q4. Do borrowers have to share their device data? No. Sharing is voluntary and consent-based. Borrowers who decline can still be assessed through other data — but many opt in when it improves their chance of approval.
Q5. Does a lender need data scientists to build these models? Not with a no-code platform. YuALT lets credit-risk analysts build, test, and deploy device- and telco-based scorecards without programming, keeping model logic transparent to the credit team.
Q6. How is bias avoided in alternate-data models? Good platforms run fairness and stability checks during model building, monitor outcomes across borrower segments, and let teams exclude features that proxy for protected attributes.
Conclusion
With over 117 crore mobile connections and 16 crore-plus credit-underserved Indians, device and telco data are among the most practical routes to responsible financial inclusion. The winning approach pairs the right signals with consent-first compliance and a no-code model that credit teams can own end to end.
Turn alternate data into confident credit decisions. Talk to the YuVerse team to see YuALT in action.
References
- Telecom Regulatory Authority of India (TRAI), via IBEF — https://www.ibef.org/news/india-s-wireless-subscriber-base-rises-above-1-171-million-telecom-regulatory-authority-of-india-trai
- TransUnion CIBIL, Credit-Underserved Indians — https://newsroom.transunioncibil.com/more-than-160-million-indians-are-credit-underserved/
- Business Standard, New-to-Credit Loan Share — https://www.business-standard.com/amp/finance/news/new-to-credit-loan-share-dips-to-16-in-q1-as-lenders-stay-cautious-report-125092401219_1.html
- Ministry of Electronics and IT, DPDP Act 2023 — https://www.meity.gov.in/content/digital-personal-data-protection-act-2023
- Reserve Bank of India, Digital Lending Directions 2025 — https://rbi.org.in/Scripts/NotificationUser.aspx?Id=12514&Mode=0
