Transforming GL Accuracy with Automation at Scale

Accounting TO TAXES (ATT) partnered with a North American healthcare company to re-engineer its transaction management and financial reporting workflows. Faced with mounting volumes and recurring inaccuracies in General Ledger (GL) and sub-GL classification, ATT implemented an AI/ML-powered RPA solution that streamlined data capture, reduced reporting delays, and eliminated costly errors. The result: faster month-end closes, scalable operations, and measurable cost savings.

What We Delivered

AUTOMATED transaction categorization and GL/Sub-GL mapping to eliminate manual errors

ENHANCED reporting timelines and accuracy through real-time reconciliation

EMPOWERED the finance team with scalable, future-ready processes and lower operational costs

Client Snapshot

Client

A U.S.-based healthcare organization with a diversified multi-site presence

Scope of Work

General ledger and sub-ledger maintenance, intercompany and bank reconciliations, credit card matching, and payroll journal upkeep

Region

North America

Industry

Healthcare

The Challenge: High Volume, High Risk

The client struggled with daily transaction volumes across multiple accounts—each requiring precise classification. Manual processes, inconsistent formats, and lack of a unified reconciliation framework led to delays and discrepancies in financial reporting.

Key Issues:

7% of transactions misclassified due to manual entry across GL and sub-GL
30% of month-end closes delayed by 2–3 business days
15% of financial reports required rework, impacting reporting integrity
2–4 days lag in transaction posting, leading to unreliable interim statements

These bottlenecks directly affected the client’s ability to maintain timely financial oversight and scale efficiently.

ATT’s Approach: Standardization First, Automation Next

Our solution followed a phased transformation strategy—laying a data foundation before introducing automation.

Phase I: Process Foundation

Requirement Standardization: Defined consistent processing logic across all transaction types
Data Structuring: Cleaned and categorized past transactions to build a reference framework
GL/Sub-GL Master Creation: Built a transaction-linked master file to guide classification logic

Phase II: Intelligent Automation

RPA Deployment: Introduced AI/ML-driven bots to automate statement downloads and transaction classification
Dynamic Learning: Enabled continuous rule refinement through feedback loops, improving accuracy with each cycle

Results Delivered

Operational Efficiency

Over 100,000 transactions processed annually across multiple financial sources
Full automation of GL and sub-GL classification based on real-time bank and card statement data
Automated reconciliations across intercompany, bank, and card transactions

Financial Impact

$36,000 annual savings through elimination of 1.5 FTEs
$5,000–$10,000 saved annually by avoiding errors, penalties, and rework
$10,000–$20,000 in productivity gains from faster reporting and decision-making
$24,000 saved in staffing costs by scaling volume without hiring additional resources

Scalability

Enabled seamless scale-up of transaction processing without increasing headcount
Reduced reporting risk and ensured faster, audit-ready month-end closes

Risk-Free Evaluation

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