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Finance Automation

Bank Reconciliation Automation Case Study

How we built an intelligent reconciliation engine that automates transaction matching, ledger sync, and exception handling for a multi-entity finance team.

Bank Reconciliation Automation Case Study

About the client

The client is a mid-market financial services firm operating across multiple subsidiaries and bank accounts. Their finance team was responsible for reconciling thousands of daily transactions across several banks, accounting systems, and internal ledgers. Manual reconciliation was slowing down month-end close, increasing error rates, and pulling senior accountants into low-value matching work instead of analysis and reporting.

95%

Transactions Matched

80%

Faster Closing

10x

Team Efficiency

Business Name

Mid-Market Finance Group

Location

United Kingdom

Industry

Finance

Problem Statement

As transaction volume grew across entities and bank accounts, the finance team's manual reconciliation process became a serious bottleneck. Spreadsheets were error-prone, exceptions piled up unresolved, and audit trails were inconsistent, putting both reporting accuracy and compliance at risk.

    Key Challenges

  • Spreadsheet-Driven Process

    Reconciliations relied on fragile Excel files copied across teams, with no shared source of truth.

  • Slow Month-End Close

    Reconciling thousands of entries by hand pushed close cycles to two weeks or more every month.

  • High Error Rate

    Manual matching and copy-paste mistakes led to misposted entries and frequent restatements.

  • Weak Audit Trail

    Reviewers struggled to evidence who matched what, when - creating friction during audits.

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Problem Statement Bank Reconciliation Automation
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Solution for Bank Reconciliation Automation

The Solution

Ouranos Technologies built a cloud-based reconciliation platform that ingests bank feeds and ledger data, automatically matches transactions using configurable rules and ML-assisted suggestions, and surfaces only true exceptions to the finance team. Every action is logged with a full audit trail, and dashboards give controllers real-time visibility into close progress.

Key Results

Faster Close

  • Reduced month-end close from 12 days to under 3 days.
  • Eliminated weekend overtime during reporting cycles.
  • Freed senior accountants to focus on analysis and forecasting.

Higher Accuracy

  • Auto-matched 95% of bank transactions on day one of go-live.
  • Cut posting errors by over 90% versus the spreadsheet baseline.
  • Standardized matching rules across every entity and bank account.

Audit & Control

  • Every match, override, and adjustment is timestamped and attributed.
  • Built-in maker-checker workflow for high-value exceptions.
  • Auditors get read-only access to a complete, exportable history.

Our Approach

01. Discovery & Process Mapping

  • Audited existing reconciliation workflows across entities and banks
  • Cataloged transaction types, common exceptions, and rule patterns
  • Identified compliance and audit-trail requirements

02. Matching Engine Design

  • Designed deterministic rules for high-confidence one-to-one matches
  • Layered ML-assisted suggestions for many-to-one and fuzzy matches
  • Defined exception categories and escalation routing

03. Integration & Rollout

  • Integrated bank feeds (Plaid + direct SFTP) and ERP ledger APIs
  • Rolled out per entity with a parallel-run period for confidence
  • Trained finance team on exception handling and reporting dashboards
Let’s Make This Your Story
Our Approach for Bank Reconciliation Automation

Key Modules

Reconciliation Engine

Reconciliation Engine

  • Rule-based and ML-assisted matching
  • Multi-currency and multi-entity support
  • Configurable match tolerance and date windows
  • Bulk import for historical backfills
Exception Workflow

Exception Workflow

  • Auto-routing of unmatched items to owners
  • Maker-checker approval for adjustments
  • Inline notes and attachment support
  • SLA tracking and aging reports
Controller Dashboard

Controller Dashboard

  • Real-time close progress by entity
  • Match-rate and exception KPIs
  • Aging analysis and trend charts
  • Exportable audit-ready reports
Technologies Used for Bank Reconciliation Automation

Technologies Used

  • Frontend: React.js + TypeScript
  • Backend: Node.js + PostgreSQL
  • Integrations: Plaid, SFTP bank feeds, NetSuite & QuickBooks APIs
  • Infra: AWS (ECS, RDS, S3) with full audit logging

Mid-Market Finance Group

Client Thoughts

Reconciliation used to define our month-end. Now it runs in the background and our team only touches the items that actually need judgment. We close faster, our auditors are happier, and our controllers finally have time to think about the numbers instead of just chasing them.

Mid-Market Finance Group Team

Testimonial from Mid-Market Finance Group

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