Restoring Accuracy in a $1B Financial System

Developed and deployed an automated financial error detection and correction system, ensuring data integrity, regulatory compliance, and leadership confidence in a $1 billion contract management system.

Challenge

Opening the system for direct subcontractor data entry led to a rapid decline in accuracy, reducing forecasting precision from 99% to 60%, impacting budgeting, payments, and regulatory compliance.

Solution

Built an automated anomaly detection and correction system using machine learning (Scikit-learn) and SQL-based statistical anomaly detection, ensuring forecasting accuracy, audit compliance, and seamless integration with the PHP-based financial management system.

Impact

Increased forecasting accuracy from 60% to 99%, prevented errors that could misallocate millions, reduced financial close times, and ensured compliance with FAR, CAS, SOX, PPA, and DCAA audit requirements.

Scikit-learn SQL Server Python PHP ETL

Project Overview

This system tracked $1 billion in financial transactions on a rolling four-year cycle, ensuring accurate budgeting, risk assessment, and cash flow forecasting. A shift from monthly to weekly and daily subcontractor data entry introduced errors and misclassifications, reducing forecasting accuracy from 99% to 60%.

I led the development of an automated anomaly detection and correction system using machine learning and rule-based validation to detect inconsistencies, alert subcontractors, and provide correction workflows. This restored forecasting accuracy to 99%, prevented multi-million-dollar misallocations, and ensured compliance with FAR, CAS, SOX, PPA, and DCAA audit requirements.

Due to its success, the system was integrated into additional financial reporting tools, scaling seamlessly and requiring no further optimizations.

Key Achievements

  • Increased forecasting accuracy from 60% to 99%, ensuring financial decisions were based on accurate, real-time data.
  • Prevented potential multi-million-dollar misallocations by detecting and correcting financial errors before they distorted reporting.
  • Reduced financial close times, improving efficiency in budgeting, contract administration, and payment processing.
  • Ensured compliance with FAR, CAS, SOX, PPA, and DCAA audit requirements, supporting faster and more accurate financial audits.
  • Sustained financial accuracy for $1 billion in transactions over rolling four-year contract cycles.
  • Developed an anomaly detection system, providing real-time validation and correction of financial entries.
  • Integrated seamlessly into additional financial reporting tools, ensuring data integrity across multiple systems without further rework.

Technical Execution

  • Designed an anomaly detection system combining machine learning (Scikit-learn) and SQL-based validation, ensuring accuracy while maintaining finance team trust.
  • Implemented real-time error alerts for subcontractors, detecting financial discrepancies based on historical patterns and statistical thresholds.
  • Developed a correction queue with suggested fixes, allowing finance teams to review and validate flagged anomalies.
  • Integrated real-time dashboards to track financial accuracy, error trends, and compliance risks, enabling leadership to make data-driven decisions.
  • Established automated reconciliation workflows, eliminating manual error resolution and accelerating financial close processes.
  • Provided full audit trails for all corrections, ensuring transparency and regulatory compliance.

Leadership & Strategy

  • Partnered with CFOs, finance leadership, and contract managers to align the system with budgeting and risk assessment needs.
  • Worked closely with subcontractors and data entry teams, ensuring smooth adoption of real-time correction workflows.
  • Standardized financial accuracy metrics, maintaining forecasting integrity across multiple reporting systems.