Automating Expense Quality Checks with AI Inconsistencies

Client Overview

The client is a global enterprise with a centralized finance function responsible for auditing employee expense claims to ensure accuracy, policy compliance, and audit readiness. With growing transaction volumes and strict compliance requirements, the existing expense quality check (QC) process had become resource-intensive and difficult to scale without increasing headcount.

Business Challenge

The pre-automation (“As-Is”) expense QC process was highly manual, fragmented, and error-prone. Each audit cycle consisted of 50+ manual control and validation steps, covering data extraction, cross-system reconciliation, policy validation, and audit documentation, involving:

Manual extraction of expense data from Maconomy and multiple Excel workbooks
Complex cross-referencing using XLOOKUP formulas, increasing the risk of human error
Visual verification of receipts and invoices across systems
Manual interpretation and application of detailed expense policy rules

The process consumed significant finance team capacity, delayed feedback to employees, and introduced inconsistency in policy enforcement.

Objectives

The primary objectives of the initiative were to:

Eliminate repetitive manual effort from expense quality checks
Apply expense policies consistently across 100% of transactions, with rules centrally managed
Improve audit accuracy, traceability, and regulatory compliance
Enable faster processing cycles without disrupting existing systems
Ensure policy documents are maintained in a shared, centralized repository, allowing SMEs to update and refine policies as business rules evolve, without dependency on IT or automation rework

Solution Overview

Auxiliobits partnered with the client’s Automation Centre of Excellence to design and implement an AI-powered, end-to-end automation for checking expense quality, branded as EQUA-BOT.

Instead of taking over the main financial systems, the solution automated the manual tasks around Maconomy, turning the quality check process into a complete digital workflow.

Digital Workforce Architecture

The solution operates as a coordinated digital assembly line comprising three specialized bots:

Together, these components deliver seamless automation from data extraction to final audit decision.

Detailed Solution Design

Dispatcher – Data Consolidation and Queuing

The Dispatcher bot automates all preparatory activities, including:

Logging into Maconomy to download Expense Sheet Reports and Approval Centre exports
Merging, cleaning, and standardizing data from multiple sources into a single consolidated report
Applying business filters (e.g., job numbers, work codes) to identify in-scope expenses
Loading each eligible expense line as a unique transaction into the Expense_QC_Audit_Queue in Orchestrator

This step replaces hours of manual data preparation with a repeatable, standardized process.

Performer—Evidence Retrieval and Case File Creation

For each queued transaction, the Performer bot:

Navigates to the relevant expense record in Maconomy
Accesses and downloads all supporting documents (receipts, invoices)
Organizes evidence into transaction-specific folders
Packages expense metadata and evidence paths into a structured digital case file

The complete case file is then passed to the Agent layer for intelligent review.

Agent Layer – AI-Driven Validation and Decisioning

At the core of the solution is an agent layer powered by Google’s Gemini 2.5 Flash model, enabling human-like judgment at machine speed.

The agent performs:

Document Intelligence – Extracts key data from receipts and invoices (vendor, date, amount, currency)
Contextual Analysis – Interprets business purpose descriptions and validates policy-specific conditions (tips, foreign expenses, etc.)
Business Rule Application – Cross-references extracted data against the official Expense Policy Rule Book
Decision Engine – Issues a reasoned outcome for each line item: Approved, Rejected, or Cannot Determine

Policy Validation Logic

Each expense is evaluated consistently against a comprehensive and evolving set of expense policy rules. The validations outlined below represent illustrative examples, and the policy framework is not limited to these checks alone. Additional rules and conditions are applied based on organizational, regulatory, and regional requirements. These validations include, but are not limited to:

Receipt validation – availability, completeness, and OCR confidence thresholds.
Amount validation – comparison of claimed versus receipted amounts, with defined tolerance handling
Vendor validation – fuzzy matching against approved vendors and enforcement of blocklists
Market validation – cross-checks against authoritative master data
Business purpose validation – assessment of description clarity and alignment with expense policy guidelines

Exception Handling and Resilience

The automation was designed for enterprise-grade resilience with clear exception categorization:

Business Exceptions

Examples include missing evidence, illegible receipts, or invalid vendor data.

System Exceptions

System exceptions refer to technical issues encountered during automated processing, such as application login failures, report download timeouts, or temporary unavailability of the transaction queue (the centralized system list where expense records awaiting quality checks are stored for processing). To handle such exceptions, the solution includes:

Results and Business Impact

Quantifiable Benefits

Significant reduction in manual effort, eliminating the 58-step manual audit process
24×7 automated execution, with scheduled daily processing at 7:00 PM EST, ensuring continuous availability and faster employee feedback
100% consistent policy enforcement across all expense transactions, removing human bias and interpretation variability
Improved audit readiness, with complete, traceable, and time-stamped logs generated for every decision

Strategic Value

Technology Stack

RPA orchestration and queue management
Maconomy (core financial system – unchanged)
Google Gemini 2.5 Flash (AI decision engine with data privacy controls; cases with low confidence are routed for human review)
Excel-based data transformation and reporting

Conclusion

The EQUA-BOT implementation demonstrates how intelligent automation and agentic AI can transform complex finance operations without replacing existing systems. By embedding intelligence directly into the expense quality assessment workflow, the client achieved faster processing, stronger compliance, and a future-ready audit capability.

After implementing EQUA-BOT, the entire quality check workflow became automated and exception-driven. The average processing time dropped to 2 minutes per transaction, resulting in an 88% reduction in effort. Over the measured period, the bot successfully processed 37,510+ transactions with a 96–99% success rate, far exceeding the earlier manual accuracy levels of around 80%.

In terms of effort savings, the automation eliminated 1,162+ hours of manual finance work annually. As a result, the team requirement reduced from 11 FTEs to just 4 FTEs, unlocking significant capacity across the finance function.

This shift allowed the remaining team to move away from repetitive quality checks and focus on higher-value activities such as exception handling, audit readiness, and compliance governance, while maintaining accuracy and control.

Overall, automation improved speed and accuracy and created a scalable, future-ready QC framework that can handle increasing transaction volumes without adding operational overhead.

This case study exemplifies how enterprises can move from manual toil to automated excellence through thoughtful orchestration of RPA and AI.