Global Financial Services Group

The Challenge

Large financial services enterprises commonly run their financial data across multiple systems that were never designed to talk to each other — SQL Server transactional databases, live external market data APIs, operational platforms — each updated on different schedules and with no unified layer for consolidated financial reporting. Monthly P&L reconciliation at this scale required manually mapping transactions across these disparate systems, a time-intensive process that consumed significant analyst capacity and introduced error risk every cycle. Without automated integration, the finance team was spending more time collecting and reconciling data than analysing it. Beyond the reconciliation problem, the financial reporting environment had no forward-looking capability: budget reporting depended on static spreadsheets that produced point-in-time snapshots rather than rolling projections, and there was no mechanism to proactively detect financial irregularities before they surfaced at period-close. Regulatory reporting submissions were hand-assembled from the same legacy templates each cycle, adding further manual overhead without improving accuracy or auditability.

Our Solution

As Principal Consultant, GYSP engineered a unified ODI 12c extraction and transformation pipeline integrating all 4 disparate corporate data layers — live external APIs and SQL Server databases — into a centralised, secure data hub. Automated cloud data integration loops replaced the manual P&L transaction mapping that had been consuming analyst bandwidth, reducing monthly P&L reconciliation work volume by approximately 60%. Oracle Analytics Cloud (OAC) ML algorithms were then programmed and deployed directly on the core P&L pipelines, creating a live anomaly detection engine that proactively flagged financial irregularities and anomalous transactions before they reached period-close — shifting the detection point from reactive reconciliation to continuous, automated monitoring. Predictive forecasting models built inside OAC delivered continuous, rolling 3-month visibility into key commercial performance indicators, giving commercial teams a forward-looking view rather than retrospective snapshots. Finally, legacy budget spreadsheets used for regulatory reporting were replaced with dynamic BI Publisher outputs — structured for compliant regulatory submissions and generated automatically from the integrated data layer rather than assembled manually each cycle.

Key Deliverables

• ODI 12c pipeline built to integrate 4 disparate corporate data layers — live external APIs and SQL Server databases — into a centralised, secure extraction and transformation hub
• Monthly P&L reconciliation work volume reduced by approximately 60% by replacing manual mapping tasks with automated cloud data integration loops
• OAC ML anomaly detection algorithms deployed on core P&L pipelines to proactively flag financial irregularities before period-close
• Rolling 3-month predictive forecasting models built in OAC, delivering continuous forward-looking visibility into key commercial performance indicators
• Legacy budget spreadsheets replaced with dynamic BI Publisher outputs tailored for compliant regulatory submissions
• Finance analyst capacity freed from data collection and reconciliation — redirected to analysis and insight generation

Services Delivered

• Heterogeneous ETL Integration
• ML Anomaly Detection
• Predictive Forecasting
• Regulatory Reporting Automation

Tech Stack

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