Vanthrope
Back to Work/Financial Reconciliation Engine
Client workRunning in production[ev-fin-01]

Financial Reconciliation Engine

Context: Mid-market enterprise operating multi-entity bank disbursements, accounts payable, and high-frequency invoice matching.

The Operational Problem

“Month-end financial close required 3 full business days of manual ledger matching across thousands of bank transactions and supplier invoices.”

Timeline6 weeks discovery to pilot sign-off, live in production
ClassificationClient work
StatusRunning in production
Truth Gate Evidence[ev-fin-01]
Operational Constraints

Real-world boundaries

▸Must match bank MT940 statements and multi-currency invoices against ERP general ledger
▸Zero tolerance for unauthorized ledger adjustments
▸Strict accounting compliance and auditability
Engineered Solution

What was deployed

Engineered an automated double-entry reconciliation engine matching transaction feeds against ERP lines with deterministic mathematical checksums and automated exception queues.

How it is kept safe
98% automated deterministic match rate threshold before proposing batch entries
Dual-key human review required for any discrepancy or transaction exceeding $50
Full cryptographic audit trail recording matching confidence and original statement hashes
Measured Outcomes

Verified results

3 days → 45 minutes
Method: Month-end close compression measured across quarterly audits
Evaluation Period: Quarterly financial closes
98% match rate
Method: Automated deterministic match rate without manual intervention
Evaluation Period: Production live

“Our month-end close dropped from 3 business days to 45 minutes. The 98% match rate leaves our finance team only reviewing rare edge-case exceptions.”

— Chief Financial Officer, Mid-Market Distribution Client
What Went Wrong & What Changed

The friction encountered

Friction: Banking fee deductions occasionally caused fractional penny rounding mismatches between foreign currency accounts.

Remediation: Added automated bank fee splitting rules with dedicated ledger clearing accounts.

Post-Mortem Reflection

What I'd do differently

Built the fee split rule matrix into the initial intake sprint.

Engineering Stack: Python, FastAPI, Next.js, PostgreSQL, Zod / Pydantic
Next Steps

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Led by Arslan Rehmani, Founder & Principal Systems Architect. For personal engineering field notes, open proof ledger, and interactive diagnostic scans, visit arslanrehmani.com →