Optimising Loan Underwriting and Servicing with Intelligent Automation
A US-based insurance carrier wanted to modernise its underwriting and servicing workflows by automating risk evaluation, improving decision speed, and ensuring consistency across loan portfolios - from origination to repayment.
Outcomes
50%
Manual Workload Reduction
60%
Faster Risk Assessment
40%
Operational Cost Reduction
30%
Risk Accuracy Increase
Technology Vendors Suited to Evaluation
Business Problem
The carriers’s underwriting and servicing operations were fragmented across legacy systems and inconsistent data sources. Manual risk evaluation created long turnaround times and increased the potential for human error.
Servicing processes lacked automation, resulting in inefficiencies and inconsistent borrower experiences. To stay competitive and compliant, the carrier needed an integrated, data-driven approach to underwriting and lifecycle management.
Challenges
- Fragmented Data Sources: Risk assessment was hindered by siloed data and inconsistent formats
- Manual Risk Evaluation: Underwriting processes were labor-intensive, increasing decision turnaround times.
- Inconsistent Servicing Practices: Loan servicing lacked automation, leading to errors and inefficiencies.
From Idea to Evidence with NayaOne
The carrier used NayaOne’s platform to design, test, and validate an automated underwriting and servicing model in a secure, isolated sandbox.
- Sandboxed Experimentation: Deployed controlled environments to model and validate risk scoring workflows.
- Continuous Monitoring: Connected servicing tools to track borrower performance and repayment activity.
- Data Integration: Unified multiple data sources to ensure consistency, completeness, and compliance.
- Cross-Team Collaboration: Enabled credit, risk, and operations teams to evaluate models in parallel without disrupting production systems.
Impact Metrics
PoC Timeline Reduction
4 weeks with NayaOne vs 12 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
Evidence-backed approvals and policy tuning based on model performance and data consistency.
KPIs
- Underwriting Turnaround Time (hours): Speed from data ingestion to decision.
- Automation Coverage (%): Proportion of underwriting tasks handled automatically.
- Risk Model Accuracy (%): Precision of predictive scoring models.
- Data Consistency Rate (%): Alignment across integrated data sources.
- Servicing Error Rate (%): Reduction in manual or process-related servicing errors.
Validate Loan Underwriting Systems Faster
Test automated underwriting and servicing solutions side by side in a secure sandbox to accelerate decision-making and improve risk accuracy.
