Transforming Underwriting
Unlocking Efficiency: From Fragmented Processes to Integrated Solutions
A study of how Cogneesol transformed a client’s transaction management through the power of automation. By leveraging AI / ML-powered RPA, we optimized data handling, streamlined GL and sub-GL classifications, and significantly improved financial reporting accuracy. This transformation eliminated delays, enhanced processing efficiency, and ensured timely month-end closes—resulting in measurable improvements in accuracy, TAT, and cost savings.

AUTOMATED transaction processing, GL & Sub-GL classification, and month-end closures

ENHANCED operational accuracy, reduced delays, and boosted financial reporting efficiency

EMPOWERED the client with scalability, agility, and significant cost savings

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Client

A North American healthcare company

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Scope of Work

Maintenance of general ledger and subsidiary ledgers, intercompany reconciliations, bank and credit-card reconciliations, payroll journal maintenance

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Geography Serviced

North America

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Industry Serviced

Healthcare

Client’s Challenges


High transaction volumes across multiple bank accounts and credit card statements, coupled with non-standardized sorting and manual recording, caused delays in transaction processing. This manual intervention often also resulted in misclassification of General Ledger (GL) and sub-GL entries, leading to inaccuracies in financial reporting.

Impact of manual errors:

  • Approximately 7% of transactions were misclassified due to manual intervention, causing discrepancies in GL and sub-GL reporting
  • Delays in financial reporting:
    • 30% of month-end closes were delayed by 2–3 business days
    • 15% of financial reports contained errors or discrepancies, requiring rework
  • Transaction recording lagged by 2–4 days, leading to inaccurate interim financial views

Impact Generated


Through continuous refinement and optimization of automated processes using machine learning capabilities, we delivered measurable results:

Improved Transaction Management

  • We processed over 100,000 financial transactions annually across 4–5 bank accounts and multiple credit card statements
  • We manage payments, reimbursements, and intercompany reconciliations efficiently

Significant Cost Savings

  • Achieved savings equivalent to 1.5 FTEs, translating to $36,000/year (based on $24,000/FTE annually)
  • Improved accuracy saved $5,000–$10,000/year by minimizing rework and avoiding penalties
  • Faster reporting saved $10,000–$20,000/year in productivity and better decision-making
  • Avoided costs of hiring additional staff by handling increased transaction volumes efficiently, saving $24,000/year for one additional FTE

Scalable and Future-ready Operations

  • Cogneesol’s automation-enabled systems ensured scalability without additional resource investments, optimizing cost and operational efficiency

These business outcomes underline Cogneesol’s commitment to delivering measurable value and operational excellence.

Cogneesol’s Solution


Faced with the client's transaction volume and accuracy challenges, we recognized the need for a structured and automated approach to improve timeliness and eliminate errors.

Our team introduced targeted changes to streamline operations and enhance financial reporting accuracy.

Phase I: Introduction of a structured foundation

  • Requirement standardization: Documented and standardized transaction processing requirements to eliminate inconsistencies
  • Data organization: Cleaned and organized transaction data to ensure clarity and accessibility
  • Master file creation: Developed a comprehensive transaction-wise GL and sub-GL master file using historical data, setting the stage for automation

Phase II: Automation rollout

  • RPA deployment: Leveraged AI / ML algorithms to implement an intelligent BOT that:
    • Automatically downloads bank and credit card statements
    • Accurately categorizes and records transactions based on master data
  • Continuous improvement: Established a feedback loop to update and refine the master data for evolving transaction patterns
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