Client Overview

A leading product vendor providing end-to-end loan management solutions for several top banks in India.

The client manages large-scale lending workflows across origination, underwriting, verification, and approvals. As their LOS/LMS platforms evolved rapidly with new rules, pricing changes, bureau integrations, and regulatory updates, their QA and testing teams struggled to maintain high-quality, compliant, and comprehensive test datasets across environments.

The Challenge

The client's testing ecosystem was constrained by real-data limitations and manual processes, resulting in coverage gaps and QA inefficiencies. Key challenges included:

• Usage of real customer data was restricted due to PII sensitivity and compliance risks; data masking disrupted sequence logic, breaking real-life behaviour.
• Manual test data creation was slow and repetitive, limiting coverage across diverse borrower profiles and policy variations.
• Frequent business/policy updates (eligibility changes, pricing grids, bureau thresholds, GST rules) outpaced the ability to refresh test datasets.
• Edge cases, fraud patterns, and "near-miss" scenarios were rarely tested, leading to defects slipping into production.

These limitations resulted in poor test coverage, higher defect leakage, and significant delays in validating new loan flows or policy rollouts.

Our AI Solution

To modernize the client's testing capabilities, we deployed an AI-driven synthetic data generation platform that produces fully policy-aware, PII-safe test datasets replicating real lending behaviour end-to-end.

This comprehensive solution included:

1. Rule-Aware Synthetic Data Generation

AI generates datasets aligned with lending rules and credit policies, including:

• FOIR, LTV, score bands
• DSCR calculations
• Sectoral risk ratings
• Geo-based risk tiers

2. Coverage Packs for Comprehensive Testing

Pre-built and customizable packs cover all lending scenarios, such as:

• Happy paths and standard approvals
• Near-miss rejects
• "Approve-if" conditional scenarios
• Fraud/EWS patterns
• Co-lending cases
• Top-up, refinance, and restructuring journeys

3. Multi-Modal Synthetic Data Creation

Supports all formats required across LOS/LMS and partner systems:

• Tabular data (loan applications, repayment history, bureau summaries)
• Documents (synthetic payslips, bank statements, GST returns, KYC PDFs)
• Payloads (JSON requests/responses for APIs)
• Images (KYC, identity proofs)

4. Versioning & Replay Framework

Each dataset is versioned per policy or rule change, enabling:

• Consistent regression testing
• Replays after pricing or rule updates
• Full auditability of test history

5. Privacy and Compliance by Design

Synthetic data is generated with zero reversibility to real customers and includes:

• Privacy certification reports
• Drift detection
• Bias checks
• No real PII used in any environment

6. Third-Party API Simulation

AI produces mocks/stubs for integrated services such as:

• Bureau reports
• GST responses
• CKYC data
• eNACH/eSign flows

This ensures testing can proceed even when external systems are unavailable or rate-limited.

Impact & Key Results

The AI-powered synthetic data platform delivered significant improvements in test efficiency, quality, and compliance:

• Test Coverage Increased by 2-4× — Including rare and complex edge cases that were previously impossible to test.
• Test Data Preparation Time Reduced by 60-80% — Effort reduced from hours or days to minutes.
• Defect Leakage Reduced by 30-50% — Fewer production defects due to comprehensive scenario coverage and high-fidelity data.
• Fully Compliance-Safe Non-Prod Environments — No real PII used; complete audit trail and privacy checks ensured regulatory adherence.
• Agile-Ready Testing — Data auto-refreshes whenever rules, pricing grids, bureau thresholds, or eligibility logic changes.

Transform Your QA with Intelligent Synthetic Data

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