In today's fast-paced financial landscape, the efficiency and accuracy of reconciliation processes are paramount. Organizations across various industries rely on reconciliations to ensure the integrity of their financial data, detect discrepancies, and maintain compliance. However, traditional reconciliation methods, often reliant on rigid rules-based systems, can struggle to keep pace with the complexities of modern data.
This white paper aims to provide a comprehensive guide for reconciliation teams, both business and technical, who are seeking to enhance their automated transaction match rates using machine learning (ML) technologies. We will delve into the challenges of manual matching, explore the benefits of ML-driven solutions, and provide practical guidance on evaluating and implementing these technologies.
Who is this Guide for?
This guide is specifically tailored for reconciliation teams who are experiencing limitations in their match rates and are keen to explore the potential of machine learning to improve efficiency and accuracy. Whether you are a business analyst seeking to understand the ROI of ML or a technical expert responsible for implementation, this document will provide valuable insights into leveraging ML for reconciliation optimization.
Post-rules Manual Matching: a Hidden Problem?
Reconciliations are fundamental to financial operations, serving as a critical control mechanism for verifying the accuracy and completeness of data across disparate systems. The traditional approach to transaction matching relies heavily on predefined rules that specify criteria for identifying matching pairs. While effective in many scenarios, rules-based systems often struggle with the intricacies of real-world data, particularly when dealing with incomplete or inconsistent information.
For reconciliations involving numerous matching fields, readily available unique identifiers, and high data quality, rules-based engines can deliver exceptional match rates. However, in reconciliations such as cash, general ledger (GL), and settlement reconciliations, the absence of unique references, limited data fields, and imperfect data quality can create significant challenges. In such cases, rules-based systems often reach their limitations, leaving reconciliation teams to manually match thousands of transactions daily.
The reliance on manual matching, even after the application of automated rules, represents a hidden problem that can significantly impact operational efficiency. While overall match rates across all reconciliations may appear satisfactory, the persistent low match rates in specific reconciliations can consume a disproportionate amount of team resources and create process bottlenecks. This issue is often obscured by the fact that manual matching tasks are frequently delegated to offshore teams, whose daily activities may not be fully visible to senior management.
The prevalence of manual matching is supported by surveys conducted across numerous industry professionals, which reveal that manual matching consumes an average of 30% of reconciliation teams' time. For a team of ten, this translates to three full-time equivalents (FTEs) dedicated to manual matching. This not only burdens the team with time-consuming tasks but also contributes to burnout and delays in reconciliation completion, ultimately hindering timely financial reporting.
To accurately assess the extent of manual matching within your organization, it is essential to quantify the time spent on these activities for problematic reconciliations. A simple survey can be used to identify the major activities performed by the team and estimate the time spent on each. This data will provide a clear picture of the manual matching burden and its impact on overall efficiency.
How Does Machine Learning for Reconciliations Help?
Machine learning offers a powerful solution to the limitations of rules-based matching, particularly in reconciliations that require significant manual intervention. By leveraging historical match data, ML algorithms can learn complex patterns and relationships between transactions, enabling them to identify matches that would be difficult or impossible for rules-based systems to detect.
The core challenge of transaction matching can be framed as a classification problem, where the goal is to predict the matching transaction(s) for a given input transaction. In supervised learning scenarios, historical matches serve as labeled training data, allowing ML algorithms to learn the underlying patterns that characterize matching pairs. Once trained, the ML model can then be used to predict matches for new, unseen transactions.
ML-based matching engines can be deployed in two primary ways: as a standalone solution or as a supplement to existing rules-based systems. In the latter approach, ML can be used to address the gaps left by rules-based matching, further improving overall match rates.
The benefits of incorporating ML into reconciliation processes are numerous:
Increased Productivity: By automating a significant portion of manual matching, ML frees up valuable time for reconciliation teams to focus on higher-value activities, such as financial analysis and strategic planning.
Reduced Process Time: Automating manual matching accelerates the reconciliation process, enabling faster completion and more timely financial reporting.
Reduced Mismatch Risk: Manual matching is prone to errors, particularly when performed under time pressure. ML-driven automation reduces the risk of human error, ensuring greater accuracy and consistency.
Auto-configuration: ML systems can automatically learn matching logic from historical data, eliminating the need for manual rule creation and maintenance.
Auto-maintenance: Effective ML systems include automated performance monitoring and retraining mechanisms to maintain optimal match rates and accuracy.
Calculating Benefits and ROI of Improving Match Automation Levels
Quantifying the benefits of ML-driven reconciliation requires a thorough analysis of the time, cost, and risk associated with manual matching. By accurately measuring these factors, organizations can demonstrate the ROI of investing in ML technologies.
The daily time taken for a given reconciliation each day:
This can be calculated using two methods:
Multiply the number of daily manual matches by the average time required to create each match.
Multiply the percentage of time spent on manual matching by the team size and daily work hours.
The annual cost of manual matching for a reconciliation:
This can be calculated by multiplying the number of annual operational workdays by the daily time spent on manual matching and the fully loaded cost of an FTE per hour. Calculate your return on investment here www.operartis.com/roi-calculator
Manual matching impact to the overall reconciliation process time:
If the percentage of time spent on manual matching is known, the impact on the daily reconciliation process time can be estimated by multiplying this percentage by the length of the workday.
Mismatch correction efforts:
In addition to manual matching, reconciliation teams may also spend time correcting mismatches generated by rules-based systems. These efforts should also be factored into the ROI calculation, using the same methods described above.
What Capabilities to Look for in a Machine Learning-Based Matching Solution
When evaluating ML-based matching solutions, it is essential to consider several key
capabilities:
Deployment:
Integration: Can the solution be seamlessly integrated with existing reconciliation systems and workflows, or does it require a complete replacement?
APIs: Does the solution provide RESTful APIs for easy integration with other systems?
Deployment Options: Does the solution support cloud, on-premises, or hybrid deployments?
Hardware Requirements: If on-premises, what are the hardware requirements in terms of processing power, memory, and storage?
Installation: How easy is it to install and configure the solution?
Scalability: Can the solution handle the required transaction volumes and data loads?
Configuration and Training:
ETL Tools: Does the solution provide ETL tools for data mapping and transformation?
Training Data: How does the solution handle training data, and what are the requirements for data quality and volume?
Model Training: How does the solution train ML models, and what algorithms are used?
Configuration: How much configuration is required to set up and customize the solution?
Matching:
Matching Algorithms: What ML algorithms are used for matching, and how effective are they?
Match Accuracy: How accurate are the matches generated by the solution?
Match Speed: How quickly can the solution generate matches?
Explanation: Can the solution provide explanations for its matching decisions?
Evaluation:
Performance Metrics: What performance metrics are used to evaluate the solution's effectiveness?
Reporting: Does the solution provide comprehensive reporting and analytics capabilities?
Testing: How easy is it to test and validate the solution's performance?
Ongoing monitoring and Model Risk Management:
Monitoring: Does the solution provide real-time monitoring of performance and data quality?
Retraining: How does the solution handle model retraining and updates?
Model Drift: Does the solution have mechanisms to detect and address model drift?
Audit Trails: Does the solution provide audit trails for tracking changes and ensuring compliance?
Explainability: Can the model explain its decisions, thus enabling better risk management.
Evaluating Machine Learning Matching Solutions: Metrics Over Marketing
In the rapidly evolving landscape of AI and ML, it is crucial to adopt a data-driven approach to evaluating matching solutions. While marketing materials may highlight impressive claims, it is essential to focus on tangible metrics and verifiable results.
Not All AI/ML Is Created Equal
The term "AI/ML" encompasses a wide range of techniques and technologies, and not all solutions are created equal. Some solutions may rely on simplistic algorithms or outdated methods, while others may leverage cutting-edge deep learning techniques. It is essential to understand the underlying algorithms and methods used by a solution to assess its suitability for your specific needs.
Metrics Over Marketing: Trust but Verify
Instead of relying solely on marketing claims, focus on evaluating solutions based on objective metrics. These metrics should include match rates, accuracy, speed, and scalability. Request detailed performance reports and benchmark data from vendors to validate their claims.
The Power and Transparency of Public Benchmark Data
Public benchmark datasets can provide valuable insights into the performance of different ML solutions. When available, leverage these datasets to compare the performance of different vendors and assess their suitability for your specific reconciliation challenges.
Leverage Operartis' public benchmark dataset here www.operartis.com/benchrec, a valuable resource for AI reconciliation evaluation.
What's Next?
Don't settle for the limitations of manual reconciliations. Operartis offers a glimpse into the future of financial services with machine learning - a future powered by data-driven insights and AI.
Want to find our more? Read A Buyers Guide to Machine Learning-Based Transaction Matching and transform your reconciliation with AI.
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