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Merger Balance Sheet Automation

AI-powered financial data reconciliation system for bank mergers with >90% accuracy using vector embeddings

Key Metrics

90%+
Data Fill Rate
Successful data matching and filling rate
40% → 90%
Performance Improvement
Improvement over traditional exact matching
24 hours
Setup Time
Time to deployment and value
95%
Matching Precision
Accuracy of semantic matching

Features

AI-Powered Semantic Matching

Vector-based similarity matching using transformer models to handle inconsistent naming conventions and identify data relationships.

Multi-Version Architecture

Three implementations: standard exact matching, enhanced with comprehensive logging, and vector-based for maximum accuracy.

Enterprise Logging & Audit

Comprehensive audit trails with SQLite and PostgreSQL support for compliance and troubleshooting in financial environments.

Automated CSV to Excel Integration

Processes multiple CSV files and updates balance sheets automatically with configurable matching thresholds.

Multi-Client Support

Handles data for different financial institutions with standardized folder structure and client-specific configurations.

Predictive Value Filling

AI-powered prediction of missing values based on learned patterns from historical financial data.

Technology Stack

Python
PyTorch
Sentence Transformers
FAISS
SQLite
PostgreSQL
OpenPyXL
Pandas
Node.js
Vector Embeddings