The Business Challenge

The client’s traditional loan underwriting process was manual, time-consuming, and heavily dependent on individual judgment. Risk evaluation primarily relied on static credit scores, outdated financial ratios, and subjective interpretation, resulting in inefficiencies and inconsistent decisions.

Key pain points included:

• Delayed Loan Approvals: Processing times of 5–7 days slowed customer onboarding.

• Missed Risk Patterns: Critical income and behavioral indicators were often overlooked.

• High Rejection Rates: Viable borrowers were denied loans due to rigid criteria.

• Inconsistent Underwriting Decisions: Different underwriters applied varying standards, creating uneven risk assessment.

• Rising NPAs: Overlooked risk signals contributed to an increase in Non-Performing Assets.

With growing credit volumes, the client required a scalable, data-driven underwriting framework capable of faster approvals, improved risk accuracy, and consistent decision-making across all loan applications.

Amantra Solution

Amantra implemented an AI-driven risk assessment engine seamlessly integrated into the client’s credit underwriting workflow, enabling faster, more accurate, and consistent lending decisions.

Key Components:

• Behavioral & Transactional Data Analysis: AI models evaluated bank statements, spending patterns, EMI behavior, and cash flows to assess borrower intent and repayment capacity.

• Multi-Source Credit Scoring: Combined traditional bureau scores with alternative data such as telecom and utility payments, as well as social and digital footprints, to build a 360° risk profile.

• Dynamic Risk Models: Machine learning models continuously updated based on market trends and historical lending outcomes, improving predictive accuracy over time.

• Explainable AI: Transparent scoring provided clear reasons for approvals or rejections, ensuring auditability and building trust in AI-driven decisions.

• Automated Decisioning: The system applied custom thresholds to recommend approvals, rejections, or escalations for manual review, dramatically reducing turnaround times.

This approach transformed underwriting from a slow, judgment-driven process into a data-driven, scalable, and intelligent framework capable of balancing growth with risk management.

Results & Business Impact

• • 60% Reduction in Processing Time: Faster credit decisions—within hours instead of days
  • 30% Lower Default Rates: Better identification of high-risk borrowers
  • 40% Approval Rate Increase: More viable customers approved without increasing risk
  • Consistency Across Teams: AI delivered uniform decisions, reducing subjectivity
  • Audit-Ready Risk Reports: Every decision supported by a documented rationale

Client Testimonial

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