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Automated IT Service Recovery in Banking | Case Study

automated IT service recovery

Introduction

Banking institutions operate in high-availability environments where system downtime and delayed incident resolution directly impact customer experience and business continuity. High incident volumes during peak business hours, duplicate tickets, and manual intervention reduce operational efficiency and increase risk. This case study highlights how a banking institution improved resilience through automated healing, intelligent ticket analysis, and service recovery mechanisms. By enabling event correlation, automation, and proactive monitoring, the organization significantly enhanced system stability and operational efficiency.

Customer

A large-scale banking institution managing high-volume IT incidents across application and infrastructure environments with 24×7 support requirements.

Business Objective

  • Improve IT resilience through automated healing and recovery
  • Reduce high incident volumes during peak business hours
  • Minimize SLA violations and improve response times
  • Eliminate duplicate and redundant tickets
  • Shift from reactive to proactive IT operations

Scope of Services

  • Heat map–based incident analysis across time and business hours
  • Identification of peak-hour incident patterns and workload spikes
  • Ticket classification and automation probability analysis
  • Detection of duplicate and parent-child ticket patterns
  • Design and implementation of automated healing workflows
  • Enablement of event correlation and alert suppression
  • Establishment of 24×7 Integrated Command Centre

Key Insights from Analysis

  • 17,600+ incidents analyzed
  • 75% incidents occur during business hours (9 AM–6 PM)
  • High-volume incident drivers:
    • Password issues (22%)
    • Account issues (19%)
    • Connectivity issues (17%)
    • Configuration issues (16%)
  • Significant duplication and parent-child ticket patterns observed

Detailed Findings

  • High dependency on manual ticket logging and resolution
  • Lack of event correlation leading to duplicate tickets (~400–500 cases)
  • Inefficient prioritization affecting response times
  • Repetitive issues (password, access, configuration) ideal for automation
  • High operational load during peak hours impacting service quality

Benefits

  • Reduced duplicate and redundant ticket volumes
  • Faster incident detection and response
  • Improved SLA adherence and service reliability
  • Better prioritization of critical incidents (P1/P2)
  • Enhanced operational efficiency and workload management

Impact

  • 30.7% automated resolution achieved
  • Up to 75% automation potential for password-related issues
  • Significant reduction in manual intervention
  • Improved service recovery and incident handling speed
  • Strong foundation for resilient, scalable IT operations
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