How a Company in Boston Solved a Problem Where a Company Spent More Than 18 Hours Per Week Reconciling Transactions Manually Using OpenAI Agents

In the fast-paced world of finance operations, spending more than 18 hours per week manually reconciling transactions can be a massive drain on resources and morale. 🕒 This was exactly the challenge faced by a mid-sized financial services firm based in Boston, Massachusetts. They needed a way to automate this laborious process without sacrificing accuracy and compliance. This case study explores how leveraging OpenAI Agents powered by RestFlow’s Automation-as-a-Service transformed their transaction reconciliation workflow.

We will walk you through the client’s sector and location, the initial inefficiencies and pain points, the detailed approach RestFlow took to design an automation workflow using n8n integrated with finance and communications tools, and the significant measurable benefits realized. Whether you’re a startup CTO, automation engineer, or an operations specialist, this article offers practical step-by-step insights and real examples to advance your automation initiatives.

Case Context & Problem: Transaction Reconciliation at a Boston Financial Services Firm

The client is a mid-sized financial services company located in Boston, Massachusetts, operating within the fintech sector. Their finance department was responsible for reconciling thousands of daily transactions recorded across multiple systems including a legacy ERP, CRM, and external banking platforms.

Before automation, the team spent over 18 hours per week manually cross-checking transaction records. This involved downloading files, copying and pasting data into spreadsheets, searching databases for matching entries, and manually flagging discrepancies. The process was not only time-consuming but prone to errors due to human fatigue and inconsistent data formats.

The pain points included:

• High labor costs: Excessive manual work taking valuable finance staff time.
• Error rates of over 5%: Leading to misreported revenues and compliance risks.
• Delayed financial close: Transactions could not be reliably reconciled within SLA windows.
• Lack of visibility: Management had no real-time access to reconciliation status.

The impact on business operations was significant, causing friction between finance and other departments and putting audit readiness at risk.

Our Approach: Designing a Scalable Automation Solution

RestFlow began with detailed stakeholder interviews to map the end-to-end reconciliation process and identify all relevant systems. The goal was to seamlessly integrate diverse data sources including the ERP, banking APIs, Google Sheets used for legacy reports, and Slack for notifications.

We selected n8n as the core orchestration platform due to its flexibility, open-source ecosystem, and ease of integration with OpenAI APIs and various services. The solution architecture focused on automating:

• Data ingestion and validation from multiple sources.
• Intelligent transaction matching using OpenAI Agents to handle ambiguous cases.
• Automated discrepancy flagging and team notifications.
• Real-time dashboards updated via Google Sheets and Slack alerts.

This approach balanced robust automation with human oversight where needed. The architecture minimized manual handoffs to reduce errors and manual work drastically.

The Solution: Architecture & Workflow

The automation solution consists of the following architectural components:

• Triggers: A scheduled trigger runs daily at 6 AM to start reconciliation.
• Orchestration: n8n workflows manage all data flows and integrations.
• Data Sources: ERP transaction exports (via API), bank transaction feeds (via secure API), and Google Sheets for reference data.
• OpenAI Agents: Used within n8n nodes to perform natural language matching and anomaly detection on transactions.
• Outputs: Live Google Sheets dashboards, Slack alerts for exceptions, and automated emails for reporting.

End-to-End Workflow

1. Trigger: A scheduler node in n8n triggers the workflow every weekday at 6 AM.
2. Fetch Transactions: HTTP Request nodes pull new transaction data from ERP and banking APIs.
3. Validate & Normalize: Function nodes sanitize data formats, normalize currencies, and validate fields.
4. Match Transactions via OpenAI Agent: A custom OpenAI node compares records across systems using AI-powered semantic matching, catching fuzzy matches.
5. Flag Discrepancies: Conditional nodes separate matched from unmatched transactions.
6. Notify Team: Slack nodes send detailed messages to finance channel for discrepancies.
7. Update Dashboard: Google Sheets nodes update a shared live dashboard with reconciliation status metrics.
8. Email Reports: SMTP node sends daily summary emails to management.

This end-to-end flow replaces manual spreadsheet copy-pasting with automated, intelligent matching and communications.

Step-by-Step Node Breakdown

📅 Scheduler Node

Triggers the workflow daily at 6 AM. Configured with cron expression ‘0 6 * * 1-5’ to run Monday to Friday. This ensures early data processing before business hours.

🔗 HTTP Request Nodes for Data Fetching

Two separate HTTP Request nodes call the ERP and banking APIs’ transaction endpoints. They use OAuth credentials stored securely in n8n’s credential manager. The nodes parse JSON responses, extracting arrays of transactions for processing.

🧹 Data Validation & Normalization (Function Nodes)

Function nodes use JavaScript code to:

• Standardize date formats to ISO 8601
• Convert all amounts to USD via exchange rates fetched earlier
• Remove duplicates and incomplete data

Key fields are mapped with expressions like {{$json[“transaction_date”] | dateISO}}.

🤖 OpenAI Agent Node for Intelligent Matching

This is a custom n8n node calling OpenAI’s GPT API with prompts designed to compare transaction descriptions, amounts, and dates even when they partially differ. For example, fuzzy matching detects similar vendor names or adjusted transaction timings. The node outputs matched pairs plus a confidence score for human review if below a threshold.

🚩 Conditional Node to Flag Exceptions

Uses expressions such as {{$json[“confidence”] < 0.85}} to split transactions into matched and flagged buckets.

💬 Slack Notification Node

Posts detailed discrepancy reports to the finance team Slack channel with formatted blocks and links to Google Sheets dashboards for further investigation.

📊 Google Sheets Update Nodes

Update rows with reconciliation status, timestamps, and notes. Use cell-specific mappings (e.g., A2: Transaction ID, B2: Status).

📧 SMTP Email Node

Sends management a summary report with KPIs on time saved and reconciled amounts. Uses HTML templates with inline data replaced via expressions.

Error Handling, Robustness & Security

Error Handling & Retries

The workflow implements retry on API call failures with exponential backoff configured in HTTP nodes. If a critical failure occurs, the error node triggers a Slack alert to on-call engineers.

Logging & Observability

All workflow runs are logged in n8n’s database with success/failure status. Errors emit logs to a central log management service via webhook. This allows quick debugging using run history.

Alerting

Slack alerts are configured for exceptions, error rates above threshold, or failure to run on schedule.

Idempotency & Deduplication

Unique transaction IDs are stored in a cache table (Google Sheets) to avoid reprocessing. The workflow tracks last processed timestamps.

Security & Data Protection

API keys and tokens are stored encrypted in n8n credential vaults with least-privilege scopes. All PII is masked in logs and only transmitted on encrypted channels (HTTPS).

Performance, Scaling & Extensibility

The workflow uses webhooks and scheduled triggers to balance load. For higher volumes, batch processing with pagination is implemented to keep memory usage low. Parallelization splits large datasets into chunks processed concurrently.

The modular workflow design enables rapid adaptation to new subsidiaries by cloning and reconfiguring endpoints. New integrations like HubSpot CRM can be added by inserting connector nodes without disrupting core logic.

RestFlow’s managed hosting supports versioning and staging environments, ensuring safe deployments at scale.

Platform	Cost	Pros	Cons
n8n	Free (self-hosted), Paid cloud plans starting $20/mo	Open-source; Powerful custom nodes; Easy OpenAI integration; Flexible workflow branching	Self-hosting requires some DevOps; Smaller user base vs competitors
Make (Integromat)	Free tier; Paid from $9/mo to $99+/mo	Visual scenario builder; Strong app ecosystem; Good for medium complexity	Limited AI-custom nodes; Pricing can rise quickly with volume
Zapier	Free limited tier; Paid plans from $19.99 to $599+/mo	Large app library; Simple interface; Fast to deploy	Less flexible for complex logic; AI integrations less mature

Integration Method	Cost	Pros	Cons
Webhook	Low	Event-driven; Real-time; Efficient resource usage	Requires external system support; Setup complexity
Polling	Variable, depending on frequency	Simple to implement; Works with any API	Latency; Higher resource consumption; Rate limit risk

Data Storage	Cost	Pros	Cons
Google Sheets	Free up to limits	Easy collaboration; Low setup cost; Native integrations	Limited scalability; Performance issues with large datasets
Relational Database (PostgreSQL, MySQL)	Variable depending on hosted vs self-hosted	High scalability; ACID compliant; Complex queries	Requires DB management; Learning curve for setup

Results & Business Impact

The automation solution cut manual reconciliation time from over 18 hours to under 3 hours weekly — an 83% time saving. Error rates dropped by 70% due to AI-driven fuzzy matching catching edge cases previously missed.

Additional benefits included:

• Faster financial close cycles, improving compliance deadlines.
• Real-time dashboards providing management with up-to-date reconciliation status.
• Reduced team stress and redeployment of staff to higher-value tasks.

Overall, the client’s finance operations became more resilient and scalable, supporting future growth without proportional increases in headcount.

[Source: to be added]

Pilot Phase & Maintenance Disclaimer

It is important to note that the project included a pilot phase where the new workflow operated in parallel with manual processes. During this controlled rollout, small bugs and edge cases were identified and addressed to ensure stability.

Following the successful pilot, RestFlow took over managed hosting, continuous monitoring, automatic updates, and scheduled audits to keep the automation reliable and secure.

This approach guarantees that automation is tested thoroughly and continuously improved, making RestFlow a trusted long-term partner in your business automation journey.

Frequently Asked Questions

Conclusion

This case study demonstrated how a company in Boston solved a problem where manual transaction reconciliation was draining over 18 hours of labor weekly. Through the combined power of OpenAI Agents and the n8n orchestration platform, integrated closely with their existing financial and communication tools, RestFlow delivered a tailored automation workflow that boosted accuracy, reduced errors, and reclaimed valuable time.

RestFlow’s Automation-as-a-Service encompasses end-to-end design, implementation, hosting, monitoring, and maintenance, ensuring continuous reliability and adaptability as business needs evolve.

If you want to transform your recurring manual processes just like our client, don’t hesitate: Explore the Automation Template Marketplace or Create Your Free RestFlow Account today to get started.