Teenva AI & Digital Ventures builds AI fraud detection systems for payment platforms, neobanks, lending apps, and merchants from Bangalore, India. Every transaction, login, or payout is scored in milliseconds—combining ML anomaly models, velocity rules, and device signals—so you block fraud without drowning legitimate users in false declines.
Fraud patterns shift fast: mule accounts, synthetic identity rings, UPI collect scams, and card testing. Teenva delivers a scoring API, rules engine, and analyst case queue integrated with your core ledger and payment webhooks—paired with identity verification at onboarding and credit risk models for lending decisions.
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AI fraud detection—real-time risk scores, rules, and case management for payments and accounts.
Hub: AI solutions · Domain: Fintech
Fraud types we detect
| Category | Signals & patterns |
|---|---|
| Payment fraud | Stolen cards, card testing, high-velocity small charges |
| UPI / wallet abuse (India) | Collect-request scams, mule VPA rotation, velocity spikes |
| Account takeover (ATO) | New device + password reset + immediate transfer |
| Synthetic identity | Linked with KYC signals at onboarding |
| First-party / friendly fraud | Dispute patterns, chargeback history (scoped) |
| Merchant fraud (scoped) | Fake listings, refund abuse on marketplaces |
| Insider / payout fraud (scoped) | Anomalous admin actions, beneficiary changes |
| Application fraud (scoped) | Overlaps with credit risk models |
Models score events—authorize, capture, login, beneficiary add, withdrawal—not chat messages.
Real-time vs batch
| Mode | Use case | Latency target |
|---|---|---|
| Real-time scoring | Authorize/decline/challenge at checkout or transfer | < 100–300 ms API (scoped) |
| Near-real-time | Post-auth review, hold settlement | Seconds |
| Batch / graph (scoped) | Ring detection, mule network analysis overnight | Hours |
Most deployments start real-time scoring plus an analyst queue for grey-zone scores.
Features we implement
Scoring API
Risk score + reason codes for your payment or banking flowRules engine
Velocity, amount caps, geo, blocklists—versioned and auditableML models
Gradient boosting, isolation forest, graph features (scoped) on your labeled dataDevice & session signals
Fingerprint hash, IP reputation, impossible travel (scoped)Step-up challenges
OTP, 3DS, manual review trigger—not binary block onlyCase management UI
Analyst queue, notes, approve/deny, feedback to retrainExplainability
Top features behind each score for compliance reviewFeedback loop
Chargeback and confirmed fraud labels improve modelsMonitoring
Fraud rate, false positive rate, model drift alertsAudit logs
Who changed rules, who overrode a decision
Architecture
- 1.Event (payment, login, payout)
- 2.Feature enrichment (user history, device, merchant)
- 3.Rules engine (hard blocks, allowlists)
- 4.ML scorer → risk score + reason codes
- 5.Decision: approve | challenge | decline | review queue
- 6.Webhook to core / Razorpay / Stripe / ledger
- 7.Labels (chargeback, fraud confirm) → model retrain pipeline
- Data minimization — Tokenized IDs; no raw PAN storage in scoring layer (PCI scope aware)
- Deployment — Your VPC/cloud; no sending full transaction history to public LLM APIs
- High availability — Fail-open vs fail-closed policy you define per product
Use cases by business type
| Business | Fraud focus |
|---|---|
| Payment aggregator / PG | Merchant onboarding fraud, transaction laundering patterns |
| Neobank / wallet | ATO, mule payouts, rapid small transfers |
| Lending app | Disbursement fraud, duplicate applications |
| Ecommerce merchant | Card testing on checkout; tie-in with Razorpay events |
| B2B payouts (scoped) | Vendor bank detail change + immediate large transfer |
| Marketplace (scoped) | Seller payout and buyer chargeback rings |
Integrations
| System | Role |
|---|---|
| Razorpay / Stripe | Payment webhooks → score before capture via Razorpay / Stripe (scoped) |
| Core ledger / banking API | Account events, balance, beneficiary list |
| API integration services | Connect via API integration services |
| Zoho CRM | Escalate cases via Zoho integration (scoped) |
| Data warehouse | Batch features and analyst reporting |
| Notification | SMS/email OTP for step-up challenges |
Integrated with your custom fintech stack.
Rules-only systems miss novel fraud; ML-only systems anger good customers.
We build hybrid scoring with tunable thresholds and analyst override—measured on fraud caught and false positive rate.
Fraud detection vs other fintech AI
| Product | Focus |
|---|---|
| AI fraud detection | Real-time transaction and account event scoring |
| AI KYC verification | Identity document and liveness at onboarding |
| AI credit risk analysis | Borrower default probability for lending |
| Transaction categorization AI | Merchant/category labels for PFM |
| Financial advisory chatbot | Customer-facing Q&A on products |
Together: KYC at signup → fraud score on every payment → credit model at loan application.
Why Teenva AI
Fintech engineering
Custom software + ML, not generic chatbot wrappersIndia payments context
UPI, IMPS, COD-adjacent merchant patterns (scoped)Analyst-first
Case UI and reason codes, not black-box decline onlyCompliance-aware design
Audit trails, data residency, least-privilege (scoped per regulator)Ops
Managed IT support and DevOps monitoring for scoring uptime
Delivery process
Discovery
Event types, fraud losses, current rules, label availability
Data assessment
Historical transactions, chargebacks, confirmed fraud cases
Baseline rules
Quick wins: velocity, blocklists, geo
Feature pipeline
User, device, merchant, amount features
Model v1
Train on labeled data; offline precision/recall eval
Scoring API
Integrate with payment or auth flow in shadow mode
Case UI
Analyst review for grey scores
Champion/challenger
A/B threshold tuning on live traffic (scoped)
Retrain cadence
Monthly or on drift; rule change governance
Frequently asked questions
We complement or replace depending on need—custom rules for India-specific patterns and owned data.
No—production scoring uses traditional ML and rules, not public LLM APIs on live PAN/PII.
Hundreds of confirmed fraud cases help; we bootstrap with rules + semi-supervised methods when labels are sparse.
Your policy—we implement both; most neobanks prefer fail-closed on payouts, fail-open on low-value auth (scoped).
Scoring layer uses tokens and hashed IDs; deployment in your region; full PCI scope defined in architecture review.
Threshold tuning, step-up challenges instead of hard decline, and analyst feedback loops—KPI tracked jointly.
Scoped batch jobs linking shared devices, VPAs, and beneficiaries.
- Yes—onboarding risk score from AI KYC verification feeds fraud features.
We deliver documentation for your compliance team; we are not a licensed auditor.
10–14 weeks MVP (rules + scoring API + basic case UI) with webhook access and historical data export.
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Build your AI fraud detection system
Build your AI solution with us—real-time risk scores, rules, and case management for payments and accounts.
Build your AI solution with us
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