The Future of AI and Transaction Matching: Leadership Through Risk Identification

The adage ‘the devil is in the details’ holds particularly true when it comes to your financial close and the risk associated with our 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 initial phase of the financial closing process typically starts with your transactions. Leveraging AI can support streamlining this process by proactive identifying potential risks associated with these transactions. By doing so, you enhance accuracy and mitigate risks right from the source, optimizing your financial operations and leading the way for better outcomes.

The Challenge: High Volume Transaction Matching Risk

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 high volume transaction matching tools 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.

The Opportunity: Artificial Intelligence (AI) and Transaction Matching

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, so what does that look like for high volume transaction matching?

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 Trintech’s matching tools, you can quickly perform transaction matching and rapidly enhance the accuracy and regulatory compliance of all resulting financial statements. This frees up time to spend on unmatched transactions exceptions – improving the accuracy and reliability of your close. However, by leveraging AI, not only can the heavy lifting of data collection, transformation, manual matching, and resolution process be automated, but the early identification of risk can also ensure quality, control and finance and accounting teams can lead the charge of process improvement.

Solution: AI Driven Risk Identification

Trintech platforms are at the forefront of leveraging AI and transaction matching technology to redefine risk identification. With unparalleled accuracy and efficiency, it empowers organizations to navigate complex financial landscapes confidently, shaping the future of risk identification in the financial close.

At the heart of Trintech’s high volume matching capabilities is a Risk Rating engine that uses machine learning (ML) to apply risk ratings to transaction matches, ensuring a 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.

With the help of AI, organizations can enhance their capabilities to detect and prevent fraudulent transactions in real-time. The model can identify signs of fraudulent activity by analyzing transactional data and historical fraud patterns, significantly reducing financial losses. It also evaluates the matching methods, where deviations from standard procedures could indicate process weaknesses or fraudulent activities.

AI can assist by shining a light on that “devil in the detail” and increasing transparency and maintaining auditability which is crucial when adopting AI technologies.

Conclusion

Trintech’s High Volume Matching Automation with AI’s role in risk identification is the realistic future of transaction matching and AI. 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 positions Finance and Accounting teams as pivotal, strategic partners in driving organizational change.

By Kierian Davis, Product Marketing Manager