10 Alternate Data Sources Indian NBFCs Use for Credit Scoring
India's lending revolution has a data problem. Over 50 crore working-age Indians lack a traditional credit score — not because they are risky borrowers, but because they have never participated in the formal credit system. For NBFCs chasing growth in new-to-credit (NTC) segments, the credit bureau score is a dead end. It simply does not exist for the borrowers they want to reach.
The answer? Alternate data.
Indian NBFCs are now tapping into non-traditional data sources — from UPI transaction histories to telecom behaviour patterns — to build predictive credit models that can assess risk for borrowers invisible to CIBIL. This shift is not theoretical. NBFCs using alternate data scoring have expanded their addressable market by 3-5x while maintaining or even improving portfolio quality.
This article breaks down the 10 most powerful alternate data sources Indian NBFCs are actively using, how each source works, and the predictive value it delivers. Whether you are a credit head evaluating alternate scoring models or a product leader building NTC lending products, this guide will show you exactly where the data opportunity lies.
Why Traditional Credit Data Falls Short for Indian NBFCs
Before diving into alternate sources, it is important to understand why the traditional approach fails at scale in India.
The Bureau Coverage Gap
Metric | Reality |
|---|---|
Working-age Indians (18-65) | ~95 crore |
Indians with bureau credit history | ~35 crore |
Credit invisible population | ~50-60 crore |
NBFCs relying solely on bureau scores | Missing 60%+ of addressable market |
Average bureau hit rate for NTC products | 15-25% |
What This Means for Growth
When an NBFC launches a product targeting first-time borrowers — say a two-wheeler loan for young professionals or a working capital line for micro-merchants — the traditional approach rejects 70-85% of applicants at the bureau check stage itself. These are not risky applicants being filtered out. These are applicants about whom the system has zero information.
Alternate data fills this gap by providing signals of financial behaviour, responsibility, and capacity from sources the borrower already engages with daily.
The 10 Alternate Data Sources Powering Indian NBFC Credit Scoring
1. Account Aggregator (AA) Data
What It Is: The Account Aggregator framework, launched under RBI guidelines, allows consented sharing of financial data between financial information providers (FIPs) and financial information users (FIUs). It covers bank account statements, deposit accounts, insurance policies, mutual fund holdings, pension data, and tax information.
How NBFCs Use It for Scoring:
- Cash flow analysis: Monthly income patterns, expense regularity, surplus consistency
- Balance maintenance: Average balances, minimum balance violations, month-end behaviour
- Transaction categorisation: Automated classification of income vs expenses vs transfers
- Salary crediting patterns: Employer stability, salary growth trajectory
- EMI discipline: Existing loan repayments visible in bank transactions
Predictive Power: Very High (Gini coefficient improvement of 15-25% over bureau-only models)
Why It Works: Bank account data reveals actual financial behaviour — not just borrowing behaviour. A person who consistently maintains positive balances, receives regular income, and manages expenses responsibly is demonstrating credit-relevant discipline regardless of whether they have a bureau score.
Indian NBFC Implementation: Leading NBFCs now integrate AA data as a primary underwriting input for NTC segments. The consent-based, digital-native flow allows real-time data pull during the application journey, making it operationally seamless.
2. Telecom Behaviour Data
What It Is: Mobile usage data from telecom operators including recharge patterns, data consumption, call behaviour, roaming patterns, and account tenure. This is accessed through partnerships with telcos or third-party aggregators with appropriate consent.
How NBFCs Use It for Scoring:
- Recharge consistency: Regular prepaid recharges indicate financial discipline
- Spend level: Higher ARPU correlates with income capacity
- Network stability: Long tenure with one operator suggests residential stability
- Data usage patterns: Consistent data consumption indicates digital activity and employment
- Outgoing call diversity: Wide contact networks correlate with social capital
Predictive Power: Moderate to High (Gini improvement of 8-15%)
Why It Works: A person who has maintained the same phone number for 5+ years, recharges consistently every month, and uses data regularly is exhibiting patterns of stability and financial planning. These micro-behaviours aggregate into meaningful credit signals.
Indian Context: With 110+ crore mobile connections in India, telecom data covers nearly the entire adult population — including those completely invisible to credit bureaus. For rural and semi-urban lending, telecom behaviour is often the single richest alternate data source available.
3. UPI Transaction History
What It Is: Unified Payments Interface (UPI) transaction records showing payment patterns, merchant categories, transaction frequency, amounts, and peer-to-peer transfer behaviour. Accessed through Account Aggregator framework or direct bank data with consent.
How NBFCs Use It for Scoring:
- Transaction volume: Frequency and consistency of digital payments
- Merchant diversity: Variety of payment categories (groceries, utilities, subscriptions)
- Income indicators: Regular incoming P2P transfers suggesting salary or business income
- Spending discipline: Ratio of discretionary to essential spending
- Payment regularity: Consistent bill payments, subscription maintenance
Predictive Power: High (Gini improvement of 12-20%)
Why It Works: UPI adoption in India crossed 14 billion monthly transactions in 2025. For the digitally active population, UPI data provides a real-time, granular view of financial life. Regular payments to utility billers, consistent merchant transactions, and stable income flows are powerful indicators of repayment capacity and discipline.
Indian Context: India's UPI ecosystem is unique globally. Even small-town merchants, street vendors, and daily-wage workers transact on UPI. This makes it one of the most democratic alternate data sources — covering segments that no other data source reaches.
4. Utility Payment History
What It Is: Payment records for electricity bills, water bills, gas connections, broadband/internet, and municipal taxes. Accessed through utility company APIs, BBPS data, or Account Aggregator framework.
How NBFCs Use It for Scoring:
- Payment punctuality: On-time vs late payment patterns
- Consumption stability: Consistent usage patterns indicating residential stability
- Bill amount trends: Rising utility consumption can indicate income growth
- Connection tenure: Long-standing utility connections indicate address stability
- Multiple connections: Managing multiple utility accounts shows capacity
Predictive Power: Moderate (Gini improvement of 5-10%)
Why It Works: Utility payments are recurring obligations similar to EMIs. A person who has paid electricity bills on time for 3+ years is demonstrating exactly the kind of regular payment behaviour that predicts loan repayment. The beauty is that nearly every Indian household has at least one utility connection.
Data Access in India: Bharat Bill Payment System (BBPS) is becoming a centralised source for utility payment data. Several NBFCs now pull BBPS payment history as part of their alternate scoring stack.
5. E-Commerce Activity Data
What It Is: Online shopping behaviour including purchase frequency, average order values, product categories, return rates, and digital payment preferences. Accessed through consent-based data sharing with e-commerce platforms or inferred from bank/UPI transaction data.
How NBFCs Use It for Scoring:
- Purchase frequency: Regular online shopping indicates digital comfort and spending capacity
- Average order value: Correlates with disposable income
- Product categories: Buying investment goods (electronics, appliances) vs consumables
- Payment method preference: Prepaid vs COD preference indicates risk appetite
- Return behaviour: Low return rates correlate with deliberate decision-making
Predictive Power: Moderate (Gini improvement of 5-12%)
Why It Works: E-commerce activity reveals spending capacity and decision-making patterns. A person who regularly purchases on Amazon or Flipkart, pays digitally, and has low return rates is demonstrating income stability, digital literacy, and deliberate behaviour — all positive credit signals.
Indian Context: With 25+ crore online shoppers in India, e-commerce data provides coverage for the growing middle class and aspirational segments. It is particularly useful for consumer durable lending and personal loan products.
6. Device and Digital Footprint Data
What It Is: Smartphone characteristics including device model, operating system version, storage capacity, app portfolio, and general digital engagement patterns. Collected through mobile SDK integration with borrower consent.
How NBFCs Use It for Scoring:
- Device value: Smartphone model and price point as income proxy
- Device age: How frequently devices are upgraded
- App portfolio: Presence of financial apps, productivity tools, educational apps
- Storage management: Organised vs chaotic device usage
- OS and security updates: Keeping software current indicates tech awareness
Predictive Power: Low to Moderate (Gini improvement of 3-8%)
Why It Works: Device data serves as a proxy for economic status and behavioural traits. A person using a mid-range smartphone (₹15,000-25,000), with financial planning apps installed, and current OS updates is signalling income level and organisational behaviour. While not a standalone predictor, device data adds meaningful lift when combined with other alternate sources.
Privacy Considerations: This is one of the most privacy-sensitive alternate data sources. Leading NBFCs use only aggregated, anonymised device characteristics rather than specific app lists or usage patterns. RBI and DPDP Act compliance is critical here.
7. Psychometric Assessment Data
What It Is: Structured questionnaire-based assessments that evaluate financial attitudes, risk tolerance, decision-making patterns, and behavioural traits. Administered as part of the loan application journey through short digital assessments (typically 10-15 minutes).
How NBFCs Use It for Scoring:
- Financial literacy indicators: Understanding of interest, repayment, budgeting
- Risk attitude: Conservative vs aggressive financial decision patterns
- Planning orientation: Short-term vs long-term financial thinking
- Integrity signals: Consistency checks and social desirability detection
- Stress response: How applicants describe handling financial pressure
Predictive Power: Moderate to High (Gini improvement of 10-18%)
Why It Works: Psychometric scoring measures intent and attitude — complementing other data sources that measure capacity and behaviour. A borrower who demonstrates strong financial literacy, conservative risk attitudes, and long-term planning orientation is statistically more likely to prioritise loan repayment.
Indian Context: Psychometric assessments are particularly powerful for microfinance and small-ticket lending where other data sources may be sparse. Several Indian MFIs have demonstrated 20-30% reduction in defaults by incorporating psychometric scores into their decision models.
8. GST Filing Data
What It Is: Goods and Services Tax (GST) filing records for businesses, including revenue declared, tax paid on time, filing frequency, and business category information. Accessed through GST portal integration with GSTIN and borrower consent.
How NBFCs Use It for Scoring:
- Revenue verification: Monthly/quarterly revenue from GST returns
- Filing discipline: Regular vs delayed vs non-filing patterns
- Business growth: Revenue trajectory over 12-24 months
- Tax compliance: Timely payment of tax obligations
- Business category: Industry classification and risk mapping
Predictive Power: High for business lending (Gini improvement of 15-22%)
Why It Works: For MSME and business lending, GST data is perhaps the single most reliable alternate data source. Regular GST filings with consistent or growing revenue demonstrate business viability and compliance orientation. A business that files GST on time is managing its affairs responsibly — a strong credit signal.
Indian Context: With GST covering 1.4 crore+ registered businesses, this data source is critical for MSME lending. NBFCs targeting the micro and small enterprise segment increasingly mandate GSTIN sharing as part of their digital lending stack.
9. Social Signals and Professional Data
What It Is: Professional network data (LinkedIn profile completeness, employment history, endorsements), public professional records, employer verification, and social stability indicators. Accessed through consent-based API integrations and public data aggregation.
How NBFCs Use It for Scoring:
- Employment verification: Current employer, designation, tenure
- Career trajectory: Job stability and progression patterns
- Professional network: Connection quality and professional engagement
- Skill signals: Certifications, education verification
- Digital identity consistency: Matching information across platforms
Predictive Power: Low to Moderate (Gini improvement of 3-7%)
Why It Works: Professional stability is a strong predictor of repayment capacity. A person with verified employment, 3+ years at their current company, and an active professional network is demonstrating the stability that correlates with consistent loan repayment. Social signals provide context that other data sources miss.
Important Caveats: Social media scoring is controversial and faces regulatory scrutiny. Leading NBFCs focus only on professional data (employment verification, career history) rather than personal social media activity. The Digital Personal Data Protection Act restricts how social data can be used, and responsible lenders stay well within these boundaries.
10. Rental Payment History
What It Is: Records of monthly rent payments made by tenants, including payment amount, regularity, landlord verification, and tenancy duration. Accessed through rent payment platforms, bank statement analysis, or direct verification.
How NBFCs Use It for Scoring:
- Payment consistency: Monthly rent paid on time for extended periods
- Rent-to-income ratio: Rental commitment relative to income
- Tenancy duration: Long tenancies indicate stability
- Payment method: Digital rent payments provide verifiable trail
- Landlord relationships: Long-term landlord relationships signal reliability
Predictive Power: Moderate to High (Gini improvement of 8-15%)
Why It Works: Rent is often the largest monthly obligation for urban Indians — frequently larger than any potential EMI. A person who has paid ₹15,000-25,000 rent consistently for 24+ months is demonstrating exactly the same discipline needed for EMI repayment. Rental data is arguably the most directly analogous alternate data to actual loan repayment behaviour.
Indian Context: India's rental market is largely informal, making data access challenging. However, the shift toward digital rent payments (through platforms like NoBroker, CRED, and bank transfers tracked via AA) is rapidly creating structured rental payment data. NBFCs that can access and use this data gain a significant edge in urban NTC lending.
Comparing Predictive Power Across Data Sources
Data Source | Gini Improvement | Coverage in India | Ease of Access | Best For |
|---|---|---|---|---|
Account Aggregator | 15-25% | 40-50 crore (bank a/c holders) | High (AA framework) | All NTC segments |
UPI Transaction History | 12-20% | 35-40 crore (active UPI users) | High (via AA) | Urban/semi-urban borrowers |
GST Filings | 15-22% | 1.4 crore businesses | Medium (consent + API) | MSME lending |
Psychometric Assessment | 10-18% | Universal (anyone can take test) | High (digital assessment) | Microfinance, small-ticket |
Telecom Behaviour | 8-15% | 80+ crore (active mobile users) | Medium (telco partnerships) | Rural, low-income segments |
Rental Payments | 8-15% | 10-12 crore (urban renters) | Low-Medium (fragmented) | Urban salaried NTC |
Utility Payments | 5-10% | 30+ crore (households) | Medium (BBPS) | Universal, supplementary |
E-Commerce Activity | 5-12% | 25+ crore (online shoppers) | Low (platform partnerships) | Consumer lending |
Device Data | 3-8% | 50+ crore (smartphone users) | High (SDK) | Supplementary signal |
Social/Professional | 3-7% | 10 crore (LinkedIn users) | Medium (APIs) | Salaried professionals |
How NBFCs Combine Multiple Alternate Data Sources
The real power of alternate data comes from combination — no single source is sufficient, but multiple sources together create a comprehensive credit picture.
The Stacking Approach
Leading NBFCs do not rely on any single alternate data source. Instead, they build ensemble models that combine 3-5 sources:
Tier 1 — Primary Sources (highest predictive power):
- Account Aggregator data (cash flow analysis)
- UPI transaction history
- GST filings (for business lending)
Tier 2 — Secondary Sources (strong supplementary value):
- Telecom behaviour
- Psychometric assessment
- Rental payment history
Tier 3 — Supporting Sources (incremental lift):
- Utility payments
- E-commerce activity
- Device data
- Professional data
Model Architecture
A typical alternate data credit model for an Indian NBFC might work as follows:
- Data ingestion: Pull 3-5 data sources based on borrower profile and consent
- Feature engineering: Extract 200-500 features from raw data (transaction frequency, payment patterns, stability indicators)
- Model training: Train gradient boosted or neural network models on historical portfolio data
- Score generation: Produce a unified alternate credit score (typically 300-900 range to match bureau convention)
- Decision integration: Combine alternate score with any available bureau data for final credit decision
Real Results from Indian NBFCs
Metric | Bureau-Only Model | Bureau + Alternate Data | Improvement |
|---|---|---|---|
Approval rate (NTC segment) | 12-18% | 35-50% | 3x increase |
Default rate (30+ DPD) | 4.5% | 3.8% | 15% reduction |
Portfolio growth | Baseline | +40-60% | Significant expansion |
Customer acquisition cost | ₹2,500-3,500 | ₹1,800-2,200 | 30% reduction |
Building an Alternate Data Strategy: A Framework for NBFCs
Step 1: Define Your Target Segment
Different borrower segments are best served by different data source combinations:
- Young salaried professionals: AA data + UPI history + rental payments
- Micro-merchants: GST filings + UPI history + telecom data
- Gig workers: UPI history + telecom data + device data
- Rural borrowers: Telecom data + psychometric + utility payments
- Women entrepreneurs: AA data + psychometric + social data
Step 2: Assess Data Accessibility
For each data source, evaluate:
- Consent mechanism: How will you obtain borrower consent?
- API availability: Is the data accessible programmatically?
- Cost per pull: What does each data source cost per application?
- Latency: How quickly can you get data for real-time decisions?
- Coverage: What percentage of your target segment will this cover?
Step 3: Build or Buy Models
This is where many NBFCs face a critical decision. Building custom alternate data models requires:
- Data science talent (scarce and expensive in India)
- 12-18 months of data collection before meaningful models
- Ongoing model monitoring and recalibration
- Regulatory compliance expertise
Platforms like YuALT offer a no-code alternative — pre-built alternate data models trained on Indian lending data that non-technical credit teams can deploy, monitor, and optimise without building a data science function from scratch. This approach powers 10 million+ credit journeys across Indian lenders, proving that alternate data scoring at scale is achievable without massive upfront investment.
Step 4: Validate and Monitor
Alternate data models require continuous validation:
- Back-testing: Test model predictions against actual portfolio performance
- Champion-challenger: Run alternate data models alongside existing processes before full deployment
- Segment monitoring: Track model performance across borrower sub-segments
- Drift detection: Monitor for changes in data source reliability over time
- Regulatory reporting: Document model logic and fairness metrics for RBI oversight
Regulatory Landscape for Alternate Data Scoring in India
RBI Guidelines
The Reserve Bank of India has been progressively supportive of alternate data use:
- Account Aggregator framework: Formal infrastructure for consented data sharing
- Digital Lending Guidelines (2022): Mandate transparency in credit decision logic
- DPDP Act (2023): Establishes consent and data minimisation requirements
- Model risk management: Expected guidelines on AI/ML model governance
Compliance Requirements
NBFCs using alternate data must ensure:
- Explicit consent: Borrowers must consent specifically to each data source being used
- Purpose limitation: Data used only for stated credit assessment purpose
- Explainability: Ability to explain why an applicant was approved or rejected
- Fairness testing: Models must not discriminate based on protected characteristics
- Data retention limits: Clear policies on how long alternate data is retained
- Right to correction: Borrowers can challenge and correct data used in decisions
Challenges and Limitations
Data Quality Issues
- Inconsistent formats: Different data providers use different schemas
- Missing data: Not all borrowers have all data sources available
- Temporal gaps: Some data sources have limited historical depth
- Fraud risk: Manufactured or manipulated digital trails
Operational Challenges
- Integration complexity: Connecting to multiple data sources requires robust infrastructure
- Latency management: Real-time decisioning with multiple API calls
- Cost management: Per-pull costs across multiple sources add up
- Consent management: Tracking and honouring consent across sources
Model Challenges
- Cold start problem: Need performance data before models can be validated
- Population stability: Alternate data patterns shift as digital adoption evolves
- Overfitting risk: Complex models with limited training data can overfit
- Interpretability: Regulatory requirement for explainable credit decisions
The Future of Alternate Data in Indian Lending
Emerging Trends
- Open Credit Enablement Network (OCEN): Will further democratise credit data access
- Unified Lending Interface (ULI): RBI initiative for standardised lending data pipes
- Real-time scoring: Moving from batch processing to instant alternate scoring
- Behavioural biometrics: How a person types, scrolls, and interacts as credit signal
- Satellite and geospatial data: For agricultural and rural lending at scale
Market Projections
- Alternate data-enabled lending in India expected to reach ₹8-12 lakh crore by 2028
- 80%+ of new NBFC credit products will incorporate at least one alternate data source
- Account Aggregator ecosystem expected to reach 50 crore linked accounts by 2027
- NTC lending market growing at 35-40% annually driven by alternate scoring
Frequently Asked Questions
What is the most reliable alternate data source for credit scoring in India?
Account Aggregator data (bank account cash flow analysis) is widely regarded as the most reliable alternate data source for Indian credit scoring. It provides the highest predictive power (15-25% Gini improvement), covers the broadest population (anyone with a bank account), and has the strongest regulatory backing through the RBI's AA framework. However, best results come from combining multiple sources rather than relying on any single one.
Is alternate data credit scoring legal in India?
Yes, alternate data credit scoring is legal in India when implemented with proper borrower consent. The Account Aggregator framework provides a regulated mechanism for data sharing. The Digital Personal Data Protection Act (2023) establishes consent requirements, purpose limitations, and data minimisation principles that NBFCs must follow. The key requirement is explicit, informed consent from borrowers for each data source used.
How accurate are alternate data credit models compared to traditional bureau scores?
Well-built alternate data models can match or exceed traditional bureau scores in predictive accuracy for their target populations. For NTC borrowers (where bureau scores do not exist), alternate data models typically achieve 65-75% accuracy in predicting default — comparable to bureau scores for the banked population. When alternate data is combined with available bureau data, the ensemble model typically outperforms either source alone by 15-25%.
How long does it take to build an alternate data scoring model?
Building a custom alternate data scoring model from scratch typically takes 12-18 months — including data collection (3-4 months), feature engineering (2-3 months), model development (3-4 months), validation (2-3 months), and deployment (2-3 months). No-code ML platforms like YuALT can reduce this timeline to 4-8 weeks by providing pre-built frameworks, validated features, and deployment infrastructure, making alternate data scoring accessible to NBFCs without dedicated data science teams.
What consent is required from borrowers for alternate data scoring?
Under India's data protection framework, NBFCs must obtain explicit, specific consent from borrowers for each alternate data source used. This means telling borrowers exactly what data will be accessed (e.g., "We will access your bank account transaction history via Account Aggregator"), how it will be used (credit assessment), and how long it will be retained. Blanket consent for "all available data" is not sufficient — granular, informed consent is required.
Can alternate data scoring lead to discrimination or bias?
Yes, if not carefully designed, alternate data models can perpetuate or amplify existing biases. For example, device data might discriminate against lower-income borrowers, and social data might reflect network inequalities. Responsible NBFCs implement fairness testing across protected characteristics (gender, caste, religion, geography) and regularly audit models for disparate impact. RBI guidelines increasingly require lenders to demonstrate model fairness.
Moving Forward with Alternate Data Scoring
The opportunity is clear: 50+ crore Indians need credit. Traditional scoring cannot reach them. Alternate data can.
For NBFCs ready to act, the path forward involves selecting the right data sources for your target segment, building or adopting models that combine multiple sources for maximum predictive power, and implementing robust governance to satisfy regulatory requirements.
The technology exists. The data exists. The regulatory framework supports it. The NBFCs that move fastest will capture the massive market of creditworthy thin-file Indians that traditional scoring systems have left behind.
Ready to deploy alternate data credit scoring without building a data science team? YuALT's no-code ML platform enables Indian NBFCs to build, deploy, and monitor alternate data models in weeks — not months. Power 10 million+ credit journeys with ML models your existing credit team can manage.
