In today's business landscape, automation is a cornerstone to maximizing productivity and unlocking growth. AI-powered back-office solutions are becoming the norm and reconciliations are a vital back and middle office function which is ripe for benefitting from AI.
The Rise of Automation in Reconciliations
Reconciliations are an integral process in any organization. They ensure the accuracy and integrity of financial data by comparing and records from different sources and matching those which originate from a common business event.
For many types of reconciliation where many data fields are available for matching or unique references exist, traditional rules-based matching engines can deliver excellent match rates.
However, for other kinds of reconciliation (e.g. cash recs, GL, settlement recs), a lack of unique references and or limited number of data fields with imperfect data quality, rules-based matching can hit a limit, leaving recons teams having to manual match thousands of transactions daily
So, although your average match rate across all of your reconciliations may be high, those bottle-neck reconciliations which have a lower match rate may be absorbing a lot of your team's time (Pareto principle in action, 20% of causes determine 80% of problems!)
This is born out by industry surveys which show that manual matching takes an average of 30% of the recon team's day. This can lead to team burnout, increased operational costs and delayed financial reporting, causing a lack of real-time visibility into financial health.
The good news is that AI and machine learning are coming to the rescue. The most effective ML-powered reconciliation solutions automate a significant portion of these unmatched transactions, freeing up teams to focus on more strategic tasks. These solutions achieve this by learning from historical match data to identify statistical patterns, automatically establishing nuanced and calibrated matching logic which surpasses that of rules based matching and removes most of the manual matching workload.
Benefits of Machine Learning for Reconciliations
The benefits of incorporating machine learning into your organization's reconciliations are multifold:
Increased productivity:Â Reducing the man-hours required for manual matching. This allows your finance team to focus on higher-value activities like financial analysis and strategic planning.
Reduced process time: reducing the manual matching workload means that reconciliation teams can complete their reconciliations faster, ensuring more timely a financial reporting.
Reduced mismatch risk during the manual reconciliation process. Users can make match errors, especially if they have to rush to complete their manual matching activities. This can lead to potential financial or audit issues.
Auto-configuration :Â ML based systems build their matching logic automatically from historical match data, auto-calibrating to ensure correctness. These removes the need to setup large numbers of handcrafted rules and regularly maintain them.
Auto-maintenance: Effective ML based systems include automated performance monitoring and auto-retraining to ensure that matching continues to provide both optimal match rates and optimal accuracy.
Unlock the Power of Reconciliation Automation
Are you ready to transform your reconciliation processes? Matchimus offers a compelling solution that delivers unmatched efficiency, accuracy, and intelligence.
Get a Quote Personalized to Your Use Case with Our Proof of Value (PoV) Assessment.Â
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. Matchimus – the future of reconciliation is here.