Introduction
Reconciliation – the process of verifying the accuracy of financial records – is a crucial function for any organization. However, traditional methods often rely heavily on manual effort, leading to inefficiencies and errors. This guide explores how bank reconciliation AI and machine learning (ML) can revolutionize transaction matching, improving automation rates and reducing the burden on reconciliation teams.
Who Should Read This Guide?
This guide is designed for reconciliation teams (both business and technical) who struggle with low match rates in their reconciliations. It aims to educate you on how ML can improve your matching process and provides key considerations when evaluating and selecting an ML solution.
Do You Need Bank Reconciliation AI and Machine Learning Matching?
Traditionally, transaction matching relies on pre-defined rules. These rules work well for reconciliations with high data quality and numerous matching fields. However, for reconciliations like bank statements, the limited number of fields and potential inconsistencies can result in low automated match rates. This translates to a significant amount of manual effort required from reconciliation teams.
Machine Learning to the Rescue
ML shines in these scenarios. By analyzing historical matches, ML systems learn data patterns and create matches based on these patterns, bypassing the limitations of strict matching rules. This significantly reduces the number of transactions requiring manual intervention.
How Does Machine Learning Work?
Imagine transaction matching as a classification problem. Each transaction is a "sample," and the objective is to predict its corresponding match (another transaction) from the opposite source.
ML algorithms can be supervised or unsupervised. Supervised learning leverages existing correct matches from historical reconciliations to understand data patterns. Unsupervised learning, while less common, can be used in situations where historical matches are unavailable.
These learned patterns allow the ML system to predict matches for new transactions. ML models can function independently or supplement rule-based matching, ultimately boosting overall match rates.
Choosing the Right ML Solution
Whether you develop an in-house solution or utilize a vendor's product, various ML algorithms exist, each with its own strengths and weaknesses. The effectiveness of these solutions also depends on specialized algorithms designed for transaction matching.
Evaluating Solutions: The Importance of KPIs
It's crucial to assess any solution's efficacy on representative data using key performance indicators (KPIs). The most accurate measurement comes from testing the solution on your own production data. However, this process can be complex and time-consuming due to security and vendor approval requirements.
A Simpler Approach: Public Benchmark Data
A faster alternative involves using published vendor performance metrics measured on publicly available datasets. Operartis established the first public dataset and metrics for ML-based transaction matching in 2023. This allows for a rapid comparison of different ML models without needing your own data.
Key Performance Indicators (KPIs) for Transaction Matching
Transaction matching with ML aims to reduce manual effort by automatically matching more transactions. However, complete automation isn't necessary. Users can still review and correct any mismatches created by the ML model.
Therefore, the optimal ML model should maximize the number of automatically matched transactions while maintaining high precision. High precision ensures the number of mismatches requiring user correction remains minimal.
The time saved by the ML model can be calculated as the difference between:
Reduced manual effort due to increased automatic matches
User time spent identifying and correcting mismatches
Finding the Right Balance
The effectiveness of an ML model hinges on balancing the number of automated matches with the number of mismatches. The ratio between the average time to correct a mismatch and the average time to manually match a transaction determines this tradeoff.
Beyond KPIs: Additional Considerations
While KPIs are essential, other factors play a role in selecting the right ML solution. Here are some key considerations:
Model Governance: Does the solution have established processes for managing and approving models?
Training Requirements: Does the system require extensive data pre-processing or can it handle data "as-is"?
Model Risk Management: Does the solution offer tools for monitoring model performance and identifying potential issues?
Setup and Training: Does the vendor offer support for setting up and training the model?
Engineering Considerations: Are there any special hardware requirements?
Explainability: Does the model provide confidence values and explain its decision-making process?
Evaluating Solutions Using Public Datasets
Public datasets like the one established by Operartis allow you to evaluate solutions without compromising your confidential data. By comparing results on a common ground, you can make informed decisions about which ML model best suits your needs.
Conclusion
This guide has equipped you with the knowledge to understand how machine learning can transform your transaction matching process. By carefully considering the factors outlined above, you can leverage the power of ML to improve efficiency, reduce workload, and achieve higher accuracy in your reconciliations.
Next Steps
Ready to explore how machine learning can benefit your business? Get a quote personalized to your use case with our Proof of Value (PoV) assessment. We'll measure the match rate improvement on your reconciliation data and provide an automation report detailing your ROI before you commit to purchase.
Let's talk it over – schedule your demo to increase efficiency, improve exception management, reduce costs, and enhance visibility into your financial data.