NudgeGuard is a prototype behavioral security layer designed to identify potentially manipulated or anomalous digital payment contexts and apply adaptive interventions before a high-risk transaction is completed.
Traditional payment security mechanisms such as PINs, OTPs, biometrics, and MFA primarily verify whether the user is authorized.
They do not necessarily determine whether an authorized user is being manipulated into making a fraudulent payment.
NudgeGuard addresses this human-layer security gap.
NudgeGuard analyzes transaction context and behavioral risk indicators, assigns a risk score, and selects an appropriate intervention.
Low-risk transactions remain frictionless while higher-risk transactions receive contextual warnings, verification prompts, or temporary cooldowns.
Transaction Context ↓ Behavioral Heuristics ↓ Risk Scoring ↓ Policy Mapping ↓ Adaptive Intervention ↓ User Decision
Collects transaction-related contextual signals such as:
- Transaction amount
- Recipient familiarity
- New recipient status
- Transaction context
- User interaction patterns
Evaluates indicators associated with potentially manipulated payments.
Converts detected indicators into a normalized risk score.
Maps risk levels to appropriate interventions.
Provides context-aware friction such as:
- Warning messages
- Verification questions
- Cooldown periods
- Transaction review prompts
| Risk Level | Score | Response |
|---|---|---|
| Low | 0–30 | Normal transaction flow |
| Medium | 31–70 | Contextual verification |
| High | 71–100 | Strong intervention |
A transaction involving:
- A newly added recipient
- An unusually high amount
- Suspicious payment context
can trigger a higher risk score.
Instead of displaying a generic warning, NudgeGuard provides a targeted intervention designed to encourage the user to pause and verify the transaction.
- HTML
- CSS
- JavaScript
NudgeGuard/ ├── index.html ├── styles.css └── js/ ├── dashboard.js ├── interventions.js ├── nudgeguard-core.js └── simulator.js
This project is currently a prototype and is not connected to production UPI infrastructure or real payment systems.
The risk engine uses simulated/contextual inputs rather than production financial data.
- Machine-learning-based behavioral risk modelling
- Continuous model calibration using fraud feedback
- Privacy-preserving behavioral signals
- Integration through a payment-security SDK
- Real-time fraud intelligence
- Explainable risk scoring
- Adaptive intervention optimization
NudgeGuard is a research/prototype project intended to explore human-layer security in digital payments.