85–90% model accuracy
Complete automation of the credit risk process
Improved risk visibility and strategy formulation
Consistent model performance
200% Improvement in Query Performance
Optimized Data Ingestion and ETL Design
Improved SLA for Bulk Requests
Efficient Tool Selection and Architecture Design
95% Reduction in Manual Effort
90–95% Accuracy in Placeholder Text Replacement
Drastic Time Reduction
Significant Cost Savings
100–300% improvement in query performance
USD 15M+ ROI in Phase 1 of implementation
10+ AI/ML use cases delivered across key functions
99.6% SLA achieved with 24x7 infrastructure support
30–40% Reduction in IT Costs
Accelerated Analytics with 3X Faster Reporting
AI/ML-Ready Infrastructure
Manual Work Reduction
40% Reduction in IT Infrastructure Costs
60% Improvement in Data Processing Speeds
4X Scalability Boost
70% Reduction in Downtime
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