Expense Category Agent
Classify healthcare invoices into standard expense categories
Description
Challenge:
Healthcare invoices contain inconsistent and unstructured line-item descriptions that vary across providers, regions, and languages. This leads to manual classification, errors in ipd_code assignment, delays in claims adjudication, and inefficiencies in billing reconciliation and financial reporting. Ensuring uniform expense categorization is difficult, time-consuming, and prone to human error, making accurate, auditable, and scalable processing a significant challenge.
How It Works:
The Expense Category Agent ingests invoice line items in JSON along with a “Relevant Context” CSV mapping table containing standardized ITEMS, SUBITEMS, and IPD default codes. It preprocesses text, expands abbreviations, detects language, and applies NLP-based fuzzy and semantic matching to identify the closest SUBITEM. Each candidate is scored and ranked using combined exact, fuzzy, semantic, and details-based metrics. High-confidence matches are auto-assigned, medium-confidence matches flagged for review, and low-confidence results return a fallback or empty code. The agent outputs enriched invoice items with ipd_code and maintains a detailed audit log for transparency, retraining, and compliance.
Benefits:
≥90% precision and ≥88% recall in line-item classification
60–75% reduction in manual coding time
Consistent expense coding across multiple languages and geographies
Faster claims adjudication, billing reconciliation, and analytics
Transparent, auditable mapping trail for compliance and review
Resources
Features
Expense Category Agent standardizes invoice line-item classification using NLP and semantic embeddings to ensure consistent ipd_code assignment across heterogeneous invoices.
Features & Capabilities:
Input Handling: Accepts JSON invoice lines (item, price, details) and CSV mapping table (ITEMS, SUBITEMS, IPD default, Input Method)
Preprocessing: Text normalization, abbreviation expansion, language detection, translation if needed
Candidate Retrieval: Substring/exact match, fuzzy token overlap, semantic similarity search
Scoring & Ranking: Weighted score combining exact (0.4), fuzzy (0.2), semantic (0.3), and details (0.1)
Decision Thresholds: High-confidence ≥0.80 → auto-assign; medium 0.60–0.79 → flag for review; low <0.60 → empty/fallback code
Multi-Language Support: Detects input language and maps using multilingual embeddings
Audit Logging: Stores candidate scores, decision paths, and review flags for traceability
Feedback Loop: Updates synonym lists and retrains embeddings based on corrections
Operating Blueprint
Knowledge Sources:
Provided Relevant Context CSV (ITEMS, SUBITEMS, IPD default, Input Method)
Historical invoice–IPD mappings for supervised training
Medical terminology lexicons and multilingual embeddings
Business Rules:
Mandatory fields: item, price, details in JSON input
Assign IPD code only to best matching SUBITEM
Multi-language handling: detect and translate if necessary
Maintain original item and price values in output
Record all scoring and candidate paths for audit
Tool Workflow:
Receive JSON invoice and context CSV
Normalize and translate text; expand abbreviations
Index SUBITEMS in vector DB and lexical index
Retrieve candidate matches using substring → fuzzy → semantic search
Calculate combined scores; rank and assign ipd_code
Append ipd_code to invoice lines; flag medium-confidence matches
Store audit logs for transparency and retraining
Badges
Classification
