88%
Processing Time Reduction
400/mo
Operational Hours Saved
10 x
Data Volume Capacity
45 min
Batch Time
Problem & Opportunity
Clients were using legacy reconciliation engine and were struggling with exponentially growing data volumes, taking 6+ hours nightly to process batch reconciliations. This created operational bottlenecks for financial institutions and delayed critical morning reports for enterprise clients.
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With transaction volumes growing 300% year-over-year, the existing single-threaded process was unsustainable and threatened SLA compliance for enterprise financial data clients.
Key Pain Points:
• 6+ hour nightly batch processing windows
• Single-threaded architecture limiting scalability
• 400+ manual ops hours monthly for monitoring and intervention
• Delayed morning reports impacting client SLAs
Strategy & Decision-Making
Features & Solutions
Feature Prioritization Framework
Used enterprise RICE scoring methodology to prioritize features based on Business Reach, Financial Impact, Implementation Confidence, and Development Effort. This helped me focus on high-ROI automation features for enterprise clients first.
12
Enterprise Features Delivered
98%
Enterprise Delivery Success Rate
3
Enterprise Release Phases
<5
Critical Business Issues
Process & Methods
User Research & Validation
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Conducted 25+ stakeholder interviews with enterprise operations teams
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Analyzed 6 months of enterprise transaction data for business pattern recognition
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A/B tested algorithm approaches with 85% enterprise compliance accuracy threshold
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Validated enterprise UI/UX through stakeholder prototype testing sessions
Cross-functional Collaboration
Enterprise Engineering Team
12 senior developers
Financial Data Science Team
4 ML engineers
Business Operations Team
8 business analysts
Financial Compliance Team
3 regulatory specialists
Results & Learnings
The enterprise reconciliation platform transformation delivered exceptional business results across all key financial metrics
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Key Learnings
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Phased rollout was critical for enterprise client adoption and system stability
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ML model accuracy improved 40% with financial domain-specific training data
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Real-time business monitoring prevented 95% of potential enterprise processing issues
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Cross-functional collaboration was essential for enterprise technical success
Quantitative Outcomes
85%
Reduction in enterprise processing time
$2.3M
Annual cost savings for enterprise clients
92%
Error rate reduction improving compliance