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Automating Bank Reconciliation with Machine Learning: Enhancing Transaction Matching Using BankSim…

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Key models: Logistic Regression, Random Forest, Gradient Boosting, SVM

TL;DR

Bank reconciliation is a critical process in financial management, ensuring that bank statements align with internal records. This article explores how Machine Learning automates bank reconciliation by accurately matching transactions using the BankSim dataset. It provides an in-depth analysis of key ML models such as Random Forest and Gradient Boosting, addresses challenges with imbalanced data, and evaluates the effectiveness of ML-based reconciliation methods.

Introduction: The Challenge of Bank Reconciliation

Manual reconciliation — matching bank statements with internal records — is slow, error-prone, and inefficient for large-scale financial operations. Machine Learning (ML) automates this process, improving accuracy and reducing manual intervention. This article analyzes the Bank Reconciliation ML Project, leveraging the BankSim dataset to train ML models for transaction reconciliation.

What This Article Covers:

  • How ML automates bank reconciliation for transaction matching
  • Key models: Logistic Regression, Random Forest, Gradient Boosting, SVM
  • Challenges with imbalanced data and why 100% accuracy is questionable
  • Implementation guide with dataset preprocessing and model training

Understanding the Problem: Why Bank Reconciliation is Difficult

Bank reconciliation ensures that every transaction in a bank statement matches internal records. However, challenges include:

  • Discrepancies in Transactions — Timing differences, missing entries, or incorrect categorizations create mismatches.
  • Data Imbalance — Some transaction types occur more frequently, making ML classification challenging.
  • High Transaction Volumes — Manual reconciliation is infeasible for large-scale financial institutions.

Existing rule-based reconciliation methods struggle with handling inconsistencies. ML models, however, learn patterns from past reconciliations and continuously improve transaction matching.

The Machine Learning Approach

Dataset: BankSim — A Synthetic Banking Transaction Dataset

The project uses the BankSim dataset, which contains 1,000,000 transactions, designed to simulate real-world banking transactions. Features include:

  • Transaction Details — Amount, merchant, category
  • User Data — Age, gender, transaction history
  • Matching Labels — 1 (matched) / 0 (unmatched)

Dataset Source: BankSim on Kaggle

Machine Learning Models Used

While the accuracy results are high, real-world reconciliation rarely achieves 100% accuracy due to complexities in transaction timing, formatting variations, and missing data.

Implementation Guide

GitHub Repository: ml-from-scratch — Bank Reconciliation

Folder Structure

ml-from-scratch/2025-03-04-bank-reconciliation/
├── data/
│   ├── banksim.csv  # Raw dataset
│   ├── cleaned_banksim.csv  # Processed dataset
│   ├── bank_records.csv  # Internal transaction logs
│   ├── reconciled_pairs.csv  # Matched transactions for ML
│   ├── model_performance.csv  # Model evaluation results
├── notebooks/
│   ├── EDA_Bank_Reconciliation.ipynb  # Exploratory data analysis
│   ├── Model_Training.ipynb  # ML training & evaluation
├── src/
│   ├── data_preprocessing.py  # Data cleaning & processing
│   ├── feature_engineering.py  # Extracts ML features
│   ├── trainmodels.py  # Trains ML models
│   ├── save_model.py  # Saves the best model
├── models/
│   ├── bank_reconciliation_model.pkl  # Saved model
├── requirements.txt  # Project dependencies
├── README.md  # Documentation

Step-by-Step Implementation

Set Up the Environment

pip install -r requirements.txt

Preprocess the Data

python src/data_preprocessing.py

Feature Engineering

python src/feature_engineering.py

Train Machine Learning Models

python src/trainmodels.py

Save the Best Model

python src/save_model.py

Challenges & Learnings

1. Handling Imbalanced Data

  • SMOTE (Synthetic Minority Oversampling Technique)
  • Class-weight adjustments in models
  • Undersampling the majority class

2. The 100% Accuracy Question

  • The synthetic dataset may oversimplify transaction reconciliation patterns, making matching easier.
  • Real-world reconciliation involves variations in formats, delays, and manual interventions.
  • Validation on real banking data is crucial to confirm performance.

3. Interpretability & Compliance

  • Regulatory requirements demand explainability in automated reconciliation systems.
  • Tree-based models (Random Forest, Gradient Boosting) provide better interpretability than deep learning models.

Results & Future Improvements

The project successfully demonstrates how ML can automate bank reconciliation, ensuring better accuracy in transaction matching. Key benefits include:

  • Automated reconciliation, reducing manual workload.
  • Scalability, handling high transaction volumes efficiently.
  • Improved accuracy, reducing errors in financial reporting.

Future Enhancements

  • Deploy the model as a REST API using Flask or FastAPI.
  • Implement real-time reconciliation using Apache Kafka or Spark.
  • Explore deep learning techniques for handling unstructured transaction data.

Machine Learning is transforming financial reconciliation processes. While 100% accuracy is unrealistic in real-world banking due to variations in transaction processing, ML models significantly outperform traditional rule-based reconciliation methods. Future work should focus on real-world deployment and validation to ensure practical applicability.

References

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