AI & Automation

Direct Bill Reconciliation Bot

Reconciliation reduced from hours to minutes, saving about 20 hours per week

The Problem

Direct bill reconciliation is not conceptually complex, but it is operationally inconsistent. Different carriers, different file structures, and slight mismatches across amounts, dates, and references forced hours of manual validation and rework.

Our Solution

We built a reconciliation system that combines deterministic matching logic with AI-assisted ambiguity handling. The platform ingests carrier files across formats, normalizes records into a unified schema, applies exact and partial rule checks, and invokes AI for edge-case semantic resolution where strict rules are insufficient.

How It Works

1

Carrier files (e.g., Tenfour, GEICO) are ingested across CSV, Excel, and related formats

2

Policy IDs, amounts, dates, and references are parsed and normalized

3

Atomic entries are grouped into meaningful financial units in a unified schema

4

Deterministic engine performs exact and partial reconciliation checks

5

AI resolves naming inconsistencies, slight mismatches, and semantic edge cases

6

Transactions are classified as Matched, Unmatched, or Ghost

Technologies Used

PythonRule-based Matching EngineAI Semantic MatchingData Normalization PipelinesPostgreSQL

Results & Impact

Reconciliation that took hours now completes in minutes

20 hrs/week saved

Weekly Time Saved

~20 hours

Processing Time

Hours to minutes

Audit Quality

Cleaner trails

Carrier Scalability

Format-agnostic

Key Takeaway

Rules handle certainty

AI handles ambiguity

Production reconciliation needs both

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Let's discuss how we can help you achieve similar results