Introduction

In the financial industry, reconciliation is essential for maintaining data integrity and regulatory compliance. However,the industry demands continual improvement, with reduced turnaround times and stricter SLAs, including the T+1 settlement change. This situation creates opportunities and requirements to automate many manual steps. The reconciliation process involves identifying discrepancies, reviewing and remediating breaks, and rerunning reconciliations to ensure all issues are resolved, typically done on T+1. Despite the current manual nature of many steps, there is significant potential for automation to enhance accuracy and reduce turnaround times, making Level 1 Recon steps obsolete.

The Client

A leading financial institution needed to overhaul its reconciliation process to cope with increasing demands for speed and accuracy. The client faced numerous challenges due to outdated reconciliation systems and manual processes, which hindered their ability to meet tight SLAs and maintain data accuracy.

The Need

The client's legacy reconciliation systems and processes had several challenges:

• Manual Reviews: Users manually reviewed breaks to determine necessary fixes.
• Manual Remediation: Users input transactions into upstream systems manually.
• Processing Large Volumes: Handling large numbers of breaks with high complexity required significant time.
• Time Sensitivity: Breaks had to be resolved promptly to meet reporting SLAs.
• Accuracy: Manual interactions increased the potential for errors.

Selecting EZOPS ARO^®

To address these challenges, the client implemented EZOPS’s IPA (Intelligent Process Automation) solution, which offers:

• Automated Break Categorization: Using IPA business rules, users can configure rules to categorize expected breaks (e.g., bank fees). EZOPS’s Machine Learning algorithms learn from past break reasons to categorize them accurately.
• Automated Remediation: Given the break categorization, the IPA bot framework resolves issues in the upstream system.
• Automatic Recon Rerun: Once issues are resolved, a new recon is triggered on the latest data, showing resolved breaks without waiting until T+1.

The Outcome

The implementation of IPA yielded significant results:

• Faster Turnaround Times: Automating what can be automated and presenting a clean recon for users to review allows them to focus on real issues.
• Continuous Process Improvement: As new issues are found, users can configure business rules to categorize them, continuously improving the process
• Improved Trust: Providing recon clients with a view of a clean recon post-remediation builds trust in the data, eliminating the need to wait until T+1.
• Enhanced Accuracy: Reducing manual touch points increases accuracy.
• Scalability: The process runs efficiently on both high and low-volume days, with auto-scaling available for high-volume automated processing.
• Reduced Risk: Fewer manual interactions mean risk officers have less to review.

Key Metrics

• Reconciliation % Match Rate: Measures transactions accurately reconciled, both pre and post-automated remediation.
• Break Resolution Time: Average time taken to identify, remediate, and close breaks.
• Manual Touch Points: Number of issues requiring manual resolution.
• SLA's KPIs: Number of SLAs breached due to recon delays.
• Error Rates: Number of errors identified and resolved through the system's automated processes and audit trails

Conclusion

By adopting EZOPS’s IPA solution, firms can enhance operational efficiency, improve data accuracy, and ensure timely resolution of discrepancies, supporting their goals of accuracy and compliance.We invite you to explore how EZOPS can help you meet or exceed your goals. Please contact us today to start the conversation.