Transaction Risk Ratings through AI Technology

The adage ‘the devil is in the details’ holds particularly true when it comes to your financial close and the risk associated with your transactional data. Embracing AI-driven solutions to aid transaction matching not only saves resources but elevates your role to a strategic partner in accounting and finance, thanks to efficient risk identification and process improvement.

The Risk: Transaction Matching Process

With supply chain pressures resulting in more intercompany relationships, new business channels with third-party agents creating more data trails, and more complex consumer behaviors and purchase interfaces, many organizations are struggling to manage large volumes of transaction data, keep up with changing payment trends, and provide business-critical insights as the economy shifts.

Leading organizations are leveraging transaction matching automation to do the heavy lifting, ensuring their time and effort are focused on high-value activities. Whilst automating the matching process may reduce manual efforts and improve efficiency, there is a potential of limited ability to detect and correct errors or anomalies by automated systems alone due to lack of oversight. Cadency Match facilitates an extra detective step using AI to identify risk at the transactional level to indicate where human intervention may be necessary.

The Opportunity: Artificial Intelligence (AI) within Transaction Matching

The emergence of AI has the potential to be a major disruptor to business success in the years to come. According to PwC’s 22nd Annual Global CEO Survey, nearly 3 out of 5 global CEOs believe AI will have a larger impact on the world than the internet revolution, with the potential to add $15.7 trillion to global GDP by 2030. That’s more than the current output of China and India combined.

Due to the massive opportunity that AI presents, it’s no surprise that the corporate accounting industry is facing a demand for its application in the financial close process. While providers like Trintech have continually promoted automation as a solution to market problems for many years, finance leaders are beginning to understand how AI and automation technologies can properly address the challenges they face.

Reconciling transactions is usually the first step in the financial close process and is one of the most time-consuming and manual parts of the financial close. With Cadency Match, you can quickly perform transaction matching and rapidly enhance the accuracy and regulatory compliance of all resulting financial statements. Matching transactions with bank statements, credit card statements, point of sale, merchant, third-party delivery services, and other external sources happen at a fraction of the time that manual processes take. This frees up time to spend on unmatched transaction exceptions – improving the accuracy and reliability of your close. With Cadency Match, not only can the heavy lifting of data collection, transformation, manual matching, and resolution process be automated, the identification of risk through AI ensures quality, control, and ongoing improvement.  

Solution: AI-Driven Risk Identification with Cadency Match

At the heart of Cadency Match is a Transaction Risk Rating engine that employs machine learning (ML) to apply risk ratings to transaction matches, ensuring more and more accurate review of suspect or out-of-policy items. The Risk Rating Engine is built to analyze large amounts of financial data and allocate risk ratings, based on historical data specific to each customer. This not only facilitates a thorough review of transactions but also aids in identifying potential fraud and policy deviations, with risk ratings from 0-10 and categorized into Low, Medium, or High.

The Risk Rating Engine conducts comprehensive analyses, including monetary value assessments, and trend analyses to detect anomalies and aid in identifying potential fraud. Entries rounded off or altered to be rounded off are flagged for review, suggesting potential risk. It also evaluates the matching methods, where deviations from standard procedures could indicate process weaknesses or fraudulent activities.

Conclusion

Cadency Match Automation with AI’s role in risk identification cannot be overstated. It not only simplifies the accountant’s workload by handling repetitive tasks but also empowers them to concentrate on strategic, value-adding activities. This technological adoption, though seemingly incremental, progressively refines and automates matching processes, significantly mitigating risk and positioning Finance and Accounting teams as pivotal, strategic partners in driving organizational change.

By Kierian Davis, Product Marketing Manager