Success Stories / Generative AI and AI/ML for Payments

Generative AI and AI/ML for Payments

A financial services organization providing digital wallet solutions was facing operational inefficiencies due to manual receipt processing across diverse document formats.
AWSGenerative AIMachine Learning
8 min read

Executive Summary

A financial services organization providing digital wallet solutions for managing public funds was hampered by manual receipt processing – a process that slowed reconciliation, introduced errors, and inflated operating costs. Partnering with Matoffo, the company adopted an AWS-based Generative AI platform that automatically ingests, extracts, and validates receipt data using Amazon SageMaker, Amazon Textract, and Amazon Bedrock. The rollout cut processing time from approximately 3 minutes per receipt to under 20 seconds, reduced error rates from 5-7% to approximately 2%, and boosted monthly processing capacity from 8,000 to over 25,000 receipts. As a result, the client established a new operational baseline that scales effortlessly with growth while delivering faster, more reliable financial reconciliation for public-sector clients.

Client Background

Our client is a financial services organization providing digital wallet solutions for managing public funds. The platform enables government entities and public-sector organizations to securely distribute, track, and reconcile funds across educational institutions and public programs. Operating in the fintech sector and serving enterprise clients across the United States, the company supports large transaction volumes with strict compliance and audit requirements. Their vision revolves around combining cutting-edge technology with efficient financial processes, setting new standards for public fund management while ensuring timely, accurate reconciliation when it matters most.

Client's Feedback

5.0
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"Matoffo currently makes up around 25%-30% of the company's workforce. The team manages the project professionally and the internal stakeholders are particularly impressed with their dedication to the success of the partnership and operational integrity. They've been able to identify and deliver scarce resources in a time where IT technical staff is difficult to find."

Chief Technology Officer

Customer Challenge

As the provider’s transaction volume grew, so did the operational stress on its document-heavy reconciliation processes. Manual extraction, validation, and re-keying of receipt data created a domino effect of delays, costs, and compliance concerns.

Key Business Challenges:

Prolonged Processing Cycles:

Receipt processing required approximately 3 minutes per document with manual data entry, delaying financial reconciliation and creating bottlenecks during high-volume periods.

Escalating Operating Costs:

Manual data entry and validation consumed 3-4 FTEs dedicated to receipt processing, driving up operational costs and limiting scalability without proportional headcount increases.

Error-Driven Compliance Risk:

Mis-keyed figures and extraction errors (5-7% error rate) led to reconciliation discrepancies, threatening audit compliance and requiring costly manual corrections.

These business pressures threatened the company’s ability to deliver fast, accurate reconciliation while protecting operational efficiency in an increasingly demanding public-sector market.

Goals and Requirements

In response to slow processing cycles, costly manual effort, and growing receipt volume, the client set clear, results-oriented targets. The aim was to create a faster, more accurate, and highly scalable operation – while maintaining strict compliance and auditability.

Performance Targets

  • Cut Processing Time:

    Reduce receipt processing from ~3 minutes to under 30 seconds through AI-driven document ingestion and extraction.

  • Boost Data Accuracy:

    Achieve >90% field-level extraction accuracy across all supported document types, minimizing rework and reconciliation errors.

Financial Targets

  • Lower Operating Cost:

    Reduce manual data-entry effort by reallocating FTEs from repetitive processing to exception handling, targeting six-figure annual savings.

  • Reduce Error Remediation:

    Minimize costly manual corrections by automating validation and anomaly detection.

Scalability & Reliability

  • Handle Volume Growth:

    Design a pipeline that can process 25,000+ receipts per month without performance degradation or new headcount.

  • Ensure Continuous Compliance:

    Embed regulatory alignment (SOC 2, state/federal procurement) and audit trails directly in the workflow.

The Solution

To resolve the client’s document-heavy reconciliation challenges, Matoffo implemented a structured, multi-phase approach – building an AI-powered automation platform on AWS that ensures scalability, operational resilience, and near real-time processing. The solution was designed to integrate with existing systems, streamline workflows, and significantly reduce manual overhead.

  1. 1

    Document Ingestion and Centralization

    The first step involved establishing secure and automated channels for ingesting receipts from the digital wallet platform. Incoming documents - scanned PDFs, photographed images, and digital files - are received via Amazon API Gateway and stored in Amazon S3 with AES-256 encryption (SSE-KMS). S3 event notifications trigger the processing pipeline automatically upon document arrival, ensuring centralized, high-throughput intake.
  2. 2

    Intelligent Document Processing

    Next, the system applied advanced document parsing using Amazon Textract to extract key fields including merchant name, date, amount, line items, and tax information. The OCR service handles diverse receipt formats, orientations, and image qualities - from clean digital PDFs to low-quality photographed paper receipts.
  3. 3

    ML Classification and Custom Model Training

    Amazon SageMaker hosts custom-trained models for receipt classification, merchant name normalization, and expense categorization. The models were trained on historical receipt data to recognize domain-specific patterns and improve extraction accuracy for edge cases not handled by standard OCR.
  4. 4

    GenAI Normalization and Validation

    Amazon Bedrock with Generative AI models normalizes unstructured receipt data, validates extracted fields against expected patterns, and flags anomalies for review. The LLM interprets ambiguous text, standardizes merchant names, and categorizes expenses according to the customer's classification taxonomy - handling the semantic understanding that rule-based systems cannot provide.
  5. 5

    Workflow Orchestration and Integration

    AWS Lambda and AWS Step Functions coordinate the end-to-end workflow, including exception handling and human-in-the-loop review routing. Low-confidence extractions (below 85% threshold) are automatically flagged for manual validation. Structured receipt data is stored in Amazon RDS (encrypted) for integration with the customer's existing financial systems, with confidence scores and audit metadata preserved for compliance reporting.

Results and Impact

The implementation of the AI-powered receipt processing platform delivered immediate, measurable improvements to the client’s operational efficiency and financial reconciliation. By automating document intake, data extraction, and validation processes, the organization achieved major gains in speed, accuracy, and scalability – while reducing dependency on manual workflows.

Quantitative Outcomes

  • ~85% reduction in processing time, from approximately 3 minutes to under 20 seconds per receipt.

  • ~93% extraction accuracy achieved across diverse receipt formats and image qualities.

  • Error rate reduced from 5–7% to ~2%, representing approximately 65% fewer reconciliation discrepancies.

  • Monthly processing capacity increased from ~8,000 to 25,000+ receipts without additional staffing.

Qualitative Outcomes

  • Enhanced compliance readiness, with complete audit trails and confidence scoring for every processed receipt.

  • Scalable operations, allowing the platform to handle transaction volume growth without proportional headcount increases.

Key Learnings

The success of this project stemmed from deliberate alignment between business needs and technical execution. Every architectural and process decision was rooted in addressing specific inefficiencies within the client’s receipt processing workflows. The following elements were critical to the platform’s success:

  • Layered AI Architecture:

    Combining Amazon Textract for OCR, Amazon SageMaker for custom classification, and Amazon Bedrock for semantic understanding created a robust pipeline that handles edge cases traditional single-service approaches cannot address.

  • Confidence-Based Routing:

    Implementing confidence scoring with human-in-the-loop review for low-confidence extractions maintained data quality while maximizing automation rates. This approach ensured accuracy without blocking throughput.

Next Steps

Following the successful deployment of the AI-powered receipt processing platform, the client is well-positioned to expand its capabilities, drive additional value, and unlock new operational efficiencies. The next phase of this transformation focuses on extending functionality and broadening impact.

  1. 1

    Expand to Additional Document Types

    The current platform is optimized for receipt processing. The client plans to extend AI workflows to additional document types—such as invoices, purchase orders, and contracts—where extraction complexity and processing volumes create similar operational challenges.
  2. 2

    Implement Predictive Analytics

    To maintain high accuracy and drive proactive insights, the team will integrate predictive analytics for spend pattern detection and anomaly identification, enabling finance teams to identify unusual expenditure patterns before they become compliance issues.

Conclusion

The successful deployment of a Generative AI–powered receipt processing platform marked a pivotal moment in the client’s operational transformation journey. What began as a response to manual processing bottlenecks evolved into a scalable, cloud-native solution that delivers faster, more accurate reconciliation – with fewer manual touchpoints and greater transparency.

By eliminating document chaos, embedding business-aligned automation, and enabling near real-time processing, the platform has redefined how receipts are validated and reconciled. Beyond efficiency gains, the solution has elevated compliance readiness, improved staff productivity, and created a replicable framework for expanding intelligent document processing across other business areas.

This transformation not only positions the client as a forward-thinking leader in public fund management but also lays the foundation for expanded services and market growth – proving that when AI meets execution, operational excellence becomes a competitive advantage.

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