Teenva AI & Digital Ventures builds AI credit risk models for NBFCs, banks, BNPL providers, and embedded lending platforms from Bangalore, India. At loan application, Teenva scores probability of default (PD), recommends approve / decline / refer, and returns explainable reason codes—using bureau features, application data, bank statement signals (scoped), and policy rules you control in your loan origination system.
Spreadsheet scorecards and opaque vendor black boxes make portfolio tuning hard. Teenva delivers trainable models, champion/challenger testing, and monitoring dashboards—integrated after KYC verification and alongside fraud detection for application fraud.
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AI credit risk analysis—PD models, cutoffs, and reason codes for your lending products.
Hub: AI solutions · Domain: Fintech
Decisions the model supports
| Decision point | Output |
|---|---|
| Application score | PD or rank score 0–1000 (your scale) |
| Recommendation | Approve, decline, refer to credit officer |
| Limit / pricing (scoped) | Suggested line amount or interest band within policy |
| Fraud overlap flag | Route to fraud detection if synthetic identity signals |
| Portfolio watch (scoped) | Early warning on existing book deterioration |
| Stress testing (scoped) | Scenario shocks on portfolio segments |
Credit models use structured ML—gradient boosting, logistic regression, survival models—not LLM chat for approve/decline.
Features we use in models
| Feature category | Examples |
|---|---|
| Bureau (scoped) | Score, tradelines, enquiries, delinquency history via licensed pull |
| Application | Income declared, employment type, loan purpose, tenure |
| Behavioral (scoped) | App engagement, repeat borrower history on your book |
| Bank statement (scoped) | Cash-flow stability, salary credits, EMI debits—parser + aggregates |
| Alternative (scoped) | Utility, GST, ecommerce seller metrics for MSME |
| KYC linkage | Identity consistency from KYC verification |
| Policy overlays | Regulatory caps, geography blocklist, product min/max ticket |
Features and bureau usage follow your license and RBI/NBFC policy (scoped in discovery).
Features we implement
Scoring API
Real-time score at application submit (< 1–3 s with bureau, scoped)Reason codes
Top drivers for decline (explainability for audit and customer comms where allowed)Policy engine
Hard rules before/after model scoreCredit analyst UI
Refer queue, override with mandatory notesModel registry
Versioned models, training data snapshot, approval workflowChampion/challenger
A/B new model on shadow or % trafficMonitoring
PSI, default rate vs prediction, segment driftBacktesting
Historical vintages before go-liveDocumentation pack
For your model validation team (scoped—not Teenva as auditor)
Architecture
- 1.Loan application (LOS / mobile app)
- 2.Feature assembly (app + bureau + bank parser)
- 3.Policy pre-checks (eligible product, geography)
- 4.Credit model → PD score + reason codes
- 5.Policy post-checks (cutoff, exposure limits)
- 6.Decision API → approve / decline / refer
- 7.Disbursement + collections feedback → retrain labels
- Deployment — Your VPC; bureau data never sent to public LLM APIs
- Labels — DPD30/60/90 or charge-off definitions agreed in discovery
Use cases by lender type
| Lender | Model focus |
|---|---|
| Personal loan NBFC | Thin-file and bureau-heavy scorecards |
| BNPL / pay-later | Short-tenor, high-volume, low ticket PD |
| MSME lending (scoped) | Cash-flow and GST-led features |
| Embedded lending (scoped) | Merchant or checkout context features |
| Two-wheeler / consumer durables (scoped) | Asset-backed product rules |
| Credit line / card (scoped) | Utilization and revolver behavior over time |
Integrations
| System | Role |
|---|---|
| Custom LOS / LMS | Application payload in, decision out via custom software development |
| SaaS lending platform | Multi-tenant scoring API via SaaS product development (scoped) |
| Bureau providers (scoped) | CIBIL/Experian/etc. via API integration |
| KYC stack | Identity features from AI KYC verification |
| Fraud detection | Application fraud flags via AI fraud detection |
| Zoho CRM | Referral queue via Zoho integration (scoped) |
Approval rate is not the KPI—risk-adjusted return is.
We tune cutoffs with your economics and prove lift with backtest and champion/challenger, not demo accuracy on toy data.
Credit risk vs other fintech AI
| Product | Focus |
|---|---|
| AI credit risk analysis | Will borrower repay?—underwriting PD models |
| AI fraud detection | Is this identity or transaction fraudulent? |
| AI KYC verification | Is this person who they claim?—onboarding IDV |
| Financial advisory chatbot | Customer-facing product FAQ |
| Transaction categorization AI | PFM spend labels—not credit decision |
Typical stack: KYC → credit score at apply → fraud on disbursement account → collections feedback retrains model.
Why Teenva AI
Lending domain + ML
Not repurposed chatbot engineersExplainability default
Reason codes for internal audit and fair-lending review (scoped)India book experience
Bureau, NBFC co-lending, BNPL patterns (scoped)Own your model
Weights and features in your environment—not locked SaaS onlyOps
Managed IT support for scoring API uptime
Delivery process
Discovery
products, ticket size, default definition, bureau access, regulatory constraints
Data extract
historical applications + performance labels (anonymized)
EDA
feature feasibility, leakage checks, segment stability
Baseline scorecard
simple logistic or rules benchmark
Model train
GBM/logistic with cross-validation; explainability review
Policy design
cutoffs, refer rules, override governance
API + analyst UI
integrate with LOS in shadow mode
Backtest & validation
your risk team sign-off (scoped)
Production
champion live; monitor drift monthly
Frequently asked questions
~2,000+ performance observations help; smaller books start with bureau-heavy scorecard + rules.
Usually combine bureau with proprietary features; full replace rare for regulated lenders.
Reason codes can feed permitted adverse action messaging—your legal defines wording.
Scoped—PDF/AA parser → cash-flow features; consent required.
We support segment analysis and documentation; formal fair-lending audit is your compliance scope.
No—production decisions use traditional credit ML, not LLM on applicant text alone.
Separate models or multi-task setup (scoped) per product economics.
Teenva delivers technical docs; your board/risk committee approves deployment.
Yes—lightweight feature set for <500 ms score (scoped) without full bureau on every cart.
12–16 weeks first production model with clean historical extract and LOS integration.
Explore
Build your credit risk models
Build your AI solution with us—PD models, explainable reason codes, and portfolio monitoring for NBFC, BNPL, and embedded lending.
Build your AI solution with us
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