Our patent-pending technology dramatically reduces the time and resources required for enterprise companies to reconcile accurately, so you can get back to doing.Product OverviewSchedule a Demo
Whether thousands or millions of transactions, our proprietary patent-pending AI-powered matching engine maximizes your match performance and accuracy. Say goodbye to manual, rules-based matching.
Our cloud-based solution means you can get up and running in less than a day with no implementation costs. This means you can start reconciling and provides you with a positive ROI from day one while dramatically lowering your implementation risks.
Our privacy-centric design will keep your business safe and your auditor happy. We’ve designed Sigma IQ to include COSO 13 controls from the ground up. We also provide built-in tracking/logging at the transaction level to improve audit visibility.
We specialize in high volume, difficult, and complex mission-critical reconciliations requiring precision accuracy and speed. Sigma IQ's proprietary AI-power is perfect when close enough is not good enough.
Outstanding matching performance. Zero administration overhead. Lowest Total Cost of Ownership. Staff friendly intuitive interface. Let us show you why you will love Sigma IQ.
Want to see how Sigma IQ can dramatically improve your matching reconciliations? Since Sigma IQ requires no implementation, we can demo our patent-pending solution using your actual data.Schedule a Demo
According to the APQC General Accounting Open Standards Benchmarking survey (2,300 companies participated) - Cycle Time for Monthly close ranges from 4.8 days or less for the top 25% of companies to 10 days or more for the bottom 25% of performers.
According to a study by Robert Half & the Financial Executives Research Foundation (FERF), only 13% of F&A teams have utilized advancements in technology solutions, with the majority of CFO’s admitting they still struggle with painful aspects of account reconciliation.
It's time for finance and accounting operations to move beyond spreadsheets and fragile rules based systems. Next generation technology such as Machine Learning provide specific solutions to hard problems.
Software selection for the enterprise is a complicated process that needs a structure in order to be successful. Most standard frameworks deal with the features, pricing and fit but fail to include different risks of implementation and success. This post describes how to add risk to the framework so that your decision is de-risked.
Artificial Intelligence (AI) and Machine Learning (ML) are terms we hear a lot in tech and industry press and can be thrown around without context or background. The PR hype accompanying AI/ML should appropriately trigger skepticism among CFOs in terms of business value the technology can generate. Where is the line between hype and reality? To start asking the right questions, it is helpful to understand some key concepts. * This white paper has been updated with new examples and clarity on how machine learning models learn and get updated.
Credit/Debit cards now account for over 75% of consumer transactions but traditional methods of reconciling the steps in the process haven’t evolved nearly as fast - until now. Traditional tools like Excel driven matching or rules based reconciliation can help in lower volume, simple matching but credit card reconciliation - with it’s raw volume and many different systems in the processing flow - creates unique reconciliation problems that need a new approach. This article describes the process and opportunities for efficiency improvement through intelligent automation.