Discover how AI-powered reconciliation automation transformed financial operations by improving speed, accuracy, and scalability while strengthening financial controls.
Problem Statement:
Kristal.ai’s reconciliation process was largely manual, taking 7–9 minutes per transaction, leading to delays, inefficiencies, and higher risk of errors at scale. Recognizing the structural limitation of manual processes, the management pushed for a technology-led solution to enable scalability and improve turnaround times.
Solution:
In collaboration with a technology partner:
- Build an AI-powered reconciliation engine for automated matching
- Developed logic to map and classify transactions across bank and internal data
- Enabled high-speed auto-matching with minimal manual effort
- Built exception workflows for unmatched transactions
Result/ Impact:
- Processing time reduced from 7–9 minutes to ~0.1 seconds per transaction
- Achieved ~75% straight-through reconciliation automation
- Reduced blended reconciliation time to ~1.5–2 minutes per transaction
- Improved scalability, accuracy, and reporting timelines
- Established a technology-driven reconciliation framework and stronger financial controls