Case Study 1: AI-Powered Bank Reconciliation in NetSuite: Automation Case Study

Month-end close was taking too long.
Errors were slipping through.
And leadership didn’t have clear cash numbers when decisions had to be made.

A mid-sized business with multiple locations and bank accounts relied on a fully manual bank reconciliation process in NetSuite. Finance teams downloaded bank statements, exported NetSuite data, and matched transactions line by line in spreadsheets.

Each bank account required 10-15 hours every month. Manual work led to 5–10% matching errors, and many issues were only discovered during audits. When transaction volume increased during peak periods, delays and risk increased even further.

Why This Was Really a Data Problem

At its core, this wasn’t an accounting issue — it was a data problem.

The company already had all the information it needed. Thousands of transactions. Dates. Amounts. Descriptions. Vendors. History. But that data lived in separate systems and was being reviewed by humans one row at a time.

As volumes grew, spreadsheets simply couldn’t keep up. What was needed wasn’t more effort — it was a smarter way to analyze large sets of transaction data, spot patterns, and surface only what truly required attention.

Quantitative Impact

The manual process consumed appx 200 hours per account each year. This reduced visibility into real cash positions and pulled experienced finance staff away from planning, forecasting, and operational support.

The cost wasn’t just time. It was slower decisions and unnecessary risk.

Operational Challenges

The work was repetitive and exhausting.
Errors were hard to trace.
Resolving differences took longer than it should have.

As transaction counts increased, audit pressure grew. Limited tracking made it difficult to explain changes quickly, especially during close and audit cycles.

Solution Overview

We designed and implemented an automated bank reconciliation and cash verification platform that connects directly to NetSuite and live bank data.

Instead of people reviewing transactions one by one, the system analyzes large volumes of transaction data automatically. Using AI trained on historical patterns, it compares amounts, timing, descriptions, and vendor behavior to identify matches at scale.

Over 95% of transactions were matched automatically.
Only unclear items were flagged, ranked by confidence, and presented with suggested matches — allowing reviewers to focus on exceptions instead of raw data.

Key Capabilities

• Side-by-side bank and NetSuite views
• Clear exception prioritization
• One-click approvals
• Adjustable matching tolerances
• Full audit history with comments and change tracking
• Automated reconciliation and certification reports

The experience was designed to be fast, visual, and easy to understand — even with large data volumes.

Implementation Approach

• Weeks 1–2: Secure integrations, data mapping, and validation
• Weeks 3–4: AI matching logic and review dashboard
• Weeks 5–6: Multi-account scaling and performance tuning
• Weeks 7–8: Audit workflows, training, parallel testing, and go-live

Results

The impact was immediate and measurable:

• Reconciliation time reduced from 10–15 hours to under 1 hour per account
• 120+ hours saved per account annually
• 95%+ automatic match rate
• ~90% reduction in errors
• $12,000+ in annual labor savings per account

Most importantly, finance teams moved from manually reviewing data to using insights from it — gaining faster cash visibility, stronger controls, and more time for planning and growth.