Unnamed US-Based Bank

The bank faced significant inefficiencies from manual operational processes involving complex data extraction across ERP, CRM, web applications, and internal portals. Scaling operations was difficult without compromising performance, and maintaining data accuracy under strict compliance requirements added further strain on staff.

Qentelli deployed a tailored RPA strategy using UiPath, Python, and MS SQL, with custom parsing rules for multi-source data extraction. Intelligent automation workflows replaced manual steps, integrated smoothly with existing systems, and included rigorous validation and error-checking protocols to meet banking compliance standards.

The implementation achieved a 35% reduction in processing time and a 25% reduction in operational costs. Data accuracy reached 80% and the system delivered a 50% increase in scalability to handle growing operational demands.

• Custom parsing rules tailored to specific data structures are critical for reliable multi-source extraction in banking environments.
• Phased RPA rollout with iterative testing and staff training reduces adoption friction and improves long-term ROI.
• Seamless integration with legacy ERP/CRM systems is a prerequisite for automation delivering measurable efficiency gains.