Alternative data lets lenders evaluate thin file and new to credit customers with more nuance, speed, and fairness. Instead of relying only on bureau scores, modern models use verified digital traces from everyday life to enrich risk signals and widen access while controlling loss. In this guide to the Top 10 Credit Scoring Methods Using Alternative Data, you will learn what each method captures, how it predicts repayment, and the common pitfalls to manage. We focus on explainable techniques, measurable governance, and consent based collection so risk teams can deploy with confidence and meet regulatory expectations without surprises.
#1 Telecom and mobile usage signals
Prepaid and postpaid telecom data provides early signals for borrowers without deep credit histories. Stable phone numbers, on time bill payments, and sustained top up amounts show continuity and financial discipline. Incoming and outgoing call patterns, handset age, and SIM tenure add context about stability and fraud risk when captured with consent. Models typically transform these attributes into features like average recharge size, month to month variance, and tenure buckets, then combine them with demographics. Risk teams must avoid sensitive content, apply strict privacy controls, and monitor drift to ensure fairness, transparency, and performance over time.
#2 Utilities and rent payment histories
Utility and rent payment histories mirror the regular obligations that borrowers manage every month. Verified records from electricity, water, gas, broadband, and rental platforms can confirm punctuality and affordability without relying on loans. Features such as on time pay ratio, number of late days, average monthly spend, and seasonal variance help predict probability of default. Linking this data through consumer permission reduces friction and prevents document fraud. Risk teams should calibrate thresholds for different city tiers and household sizes, normalize by local tariffs, and test stability across seasons to avoid bias in the underwriting decision.
#3 Bank transaction cash flow analytics
Bank transaction data transformed through cash flow analytics reveals income consistency, expense hygiene, and residual capacity to repay. Signals include median salary inflow, income volatility, bounced debits, gambling flags, NSF fees, and recurring commitments such as EMI and rent. After categorization, models compute free cash flow, debt service coverage, and shock buffers, then apply scorecards or machine learning for risk ranking. Explainability is strengthened with reason codes mapped to categories and metrics. Operational safeguards must include data minimization, consumer consent, and robust encryption, with regular back testing to keep thresholds aligned to macro shifts and regulatory guidance.
#4 E commerce and platform behavior
E commerce and platform data offers a granular view of spending, fulfillment reliability, and marketplace reputation. Order frequency, average basket size, return rates, delivery address stability, and verified reviews indicate both ability and intent to repay. For sellers or gig workers, metrics such as monthly gross merchandise value, cancellation ratio, and on time completion rate reflect income strength. Feature engineering should remove outliers from holiday spikes and promotions, and segment buyers, sellers, and riders with distinct scorecards. Compliance teams must document provenance, ensure consumer consent, and provide clear notices so applicants understand what data is used and how it influences outcomes.
#5 Digital payroll and employment verification
Digital payroll and employment records validate income sources and tenure with high precision. Employer verified pay slips, bank credited salaries, and provident fund contributions help confirm identity, stability, and net take home pay after deductions. Models typically derive features such as consistent pay dates, average increments, overtime variability, and gap analysis between expected and observed salary credits. Cross checking employer reputation and industry cyclicality further refines risk. Implement strong consent flows, avoid storing raw documents longer than necessary, and align with local employment laws while providing customers with simple mechanisms to contest or correct inaccurate data.
#6 Education and professional credentials
Verified education and professional credentials add context about skills and earning potential when combined with other factors. Digital diplomas, licensing registries, and certification platforms can evidence formal training, while alumni and association membership confirms continuity. Risk models should treat these as supportive variables rather than sole determinants, deriving features such as highest qualification level, recency of certification, and field employability. To reduce bias, calibrate by cohort outcomes rather than prestige, and regularly test for disparate impact. Provide applicants with clear explanations and alternative paths to demonstrate capability if credentials are unavailable or do not reflect current employment reality.
#7 Device and behavioral telemetry
Device and behavioral telemetry can detect fraud and assess application quality when used with consent and strict privacy controls. Signals include typing cadence, form correction rate, time to complete steps, device matching to prior sessions, and geolocation consistency across visits. These variables help separate genuine applicants from bots or synthetic identities and can improve early delinquency prediction. Engineering teams should hash identifiers, limit collection to purpose bound metrics, and avoid sensitive content. Risk governance must document tests for stability, fairness, and reject inference, and provide reason codes that reference behavior categories rather than opaque technical measurements.
#8 Psychometric micro assessments
Short, validated psychometric assessments can estimate conscientiousness, planning, and risk aversion, which correlate with repayment behavior. Well designed tasks measure consistency, response time, and attention rather than trivia. Scores are best used as a complementary signal for thin file applicants, blended with cash flow and obligation data. Lenders should use instruments validated in the local context, publish clear consent language, and provide accessible alternatives for those with disabilities or limited digital literacy. Continuous monitoring of predictive power and differential item functioning helps maintain fairness, while human review handles edge cases where the assessment may be unreliable.
#9 Geospatial and stability features
Geospatial and stability features summarize the consistency of an applicant life patterns without inferring sensitive attributes. Examples include address tenure from verified sources, distance between residence and workplace, commute regularity, and consistency of application geolocation with declared addresses. When combined with time at job and phone tenure, these variables help estimate the likelihood of stable income and reduce first pay default risk. Use conservative thresholds, add manual review for mismatches, and never infer protected characteristics. Document how features are derived, retain only aggregated values, and allow customers to update addresses easily to keep models accurate and defensible.
#10 MSME POS and invoicing for business credit
For micro and small enterprises, merchant POS, invoicing, and inventory records provide a robust basis for cash flow based scoring. Daily sales, refund ratios, card versus cash mix, days inventory outstanding, and invoice payment delays map to revenue durability and liquidity risk. Models can compute seasonality indexes, working capital gaps, and survival probabilities using anonymized peer benchmarks by category and region. To prevent gaming, compare declared turnover with GST or tax filings where legally permissible, and reconcile bank deposits with POS totals. Provide clear reason codes and coaching tips tied to operational levers so owners can improve eligibility over time.