How AP Analytics and Data Accuracy Drive Better Decision-Making in Finance

Emily Perkins

Emily Perkins

Head of Content Strategy

Robust AP analytics provide visibility into areas of data errors and inaccuracies, and should be a critical component of every operational strategy.

June 6, 2024

6 min read

How AP analytics and data accuracy drive better decision-making in finance

Inaccurate data is one of the main roadblocks to seamless AP automation. Robust AP analytics provide visibility into these errors and inaccuracies and should be critical to every operational strategy.

Data accuracy is the number one issue AP teams face

In a recent social media poll, asked finance and accounting professionals to select their organization's most significant accounts payable (AP) automation challenge. Poll respondents listed issues with data accuracy as the leading obstacle to achieving reliable and trustworthy automation within their department.

Why data accuracy is so crucial in AP

Data accuracy is crucial for effective AP automation, yet it often poses a significant challenge. Errors in invoice data, vendor information, or payment records can lead to incorrect payments, delayed processing, and strained vendor relationships. Ensuring data accuracy requires robust validation processes and real-time insights, which can be challenging to maintain without comprehensive automation solutions that include advanced data verification and error-checking mechanisms.

Financial leaders need accurate accounting data to ensure the integrity and reliability of financial transactions and records. It is vital for maintaining precise payment schedules, avoiding costly errors, and ensuring compliance with regulatory requirements. Having correct AP information enhances operational efficiency by reducing the need for manual rework, which can be time-consuming and prone to further errors. According to Gartner, poor data quality costs organizations an average of $12.9 million every year. Data quality is essential for any business that relies on data for decision-making and data modeling. 

Data accuracy can also significantly impact cash flow forecasting, which allows organizations to predict their cash outflows more reliably and manage liquidity effectively. This enables better financial planning and helps avoid scenarios of cash shortages or overdrawn accounts. 

Additionally, having reliable information on hand supports data-driven decision-making by providing clear, trustworthy insights into invoice volume and amounts, spending patterns, vendor performance, and process efficiencies. Analytics capabilities are critical to data accuracy, providing holistic insight into operational performance metrics.

In an Ardent Partners AP Metrics That Matter report, 50 percent of respondents believe deeper and more agile analytics remain key to AP departments' achieving higher performance levels in the years to come. 

The importance of data-driven decision making

Data-driven decision-making is a critical component of running a successful business. In today's digital age, all businesses generate vast amounts of data across their tech stack and teams, but without practical analysis and interpretation, this data is nothing more than noise. Data-driven decision-making allows businesses to extract valuable insights from data and make informed decisions that can shape the future of their organizations.

However, actionable data is only possible when a system or solution is in place to organize, process, and surface key metrics. AP analytics dashboards and reports are necessary for businesses to better understand customer behavior, market trends, and operational efficiency. Every transaction, invoice, payment, and vendor profile generates valuable data points that can — and should — be analyzed. When surfaced in the right platform and view, this data can be used to identify opportunities, mitigate risks, and optimize business processes.

The role of AI in AP analytics and data accuracy

Artificial intelligence (AI) is revolutionizing analytics capabilities in finance and accounting by enabling more accurate, efficient, and insightful data processing. Through advanced algorithms and machine learning, AI can analyze vast amounts of financial data at unprecedented speeds, uncovering patterns and trends that were previously difficult to detect. This enhances forecasting accuracy, risk management, and fraud detection, providing finance professionals with deeper insights and actionable intelligence. 

An AI-driven analytics solution can surface data in real time for strategic analysis and decision-making. By continuously learning and adapting, an AI system also improves over time, offering increasingly precise and relevant insights that can drive better financial planning and performance. 

What to look for in an AP analytics solution

1. Real-time operational insights

An AP analytics solution should readily provide useful day-to-day metrics. Some of these could include invoice processing time, invoice processing volume by team members, the accuracy of vendor records, payment volume and amounts, early discount opportunities, and much more. With access to operational insights, financial leaders can quickly evaluate team performance, track approvers, see vendor trends, and better forecast cash flow. 

2. Accountable metrics

A leading analytics solution should present metrics showing how well the solution works and provide accountability. In other words, it should provide insights and data demonstrating the software is doing what it promised. Analytics delivers real-time data on how well the platform works over time regarding invoice processing accuracy rates, percent of no-touch invoices, and volume over time — and even tracks the organization's financial return on investment (ROI).

3. Supports audits and helps prevent fraud

Analytics software for AP teams should make it easier to complete the end-of-month close and routine auditing. Accruals reporting, general ledger tracking, and payment reconciliation dashboards and reports can help. More accurate operational analytics can also help identify any patterns or anomalies that could be a sign of potential fraud.

4. Easy integration with your existing tech stack

Seamless integration into existing systems and tools is a no-brainer when selecting new technology. Ask critical questions about ERP integration capabilities and understand how the technology will be implemented

5. Incorporates AI technology

Innovative technology should include some AI capabilities or functionality. As the latest breakthrough in our digital age, AI algorithms and machine learning are being built or incorporated into most leading software platforms. When evaluating a new technology vendor or platform, ask about their AI innovation and capabilities and how their product development team is leveraging AI to provide the best possible solutions for their customers. By using leading technology, organizations will only increase their competitive advantage and achieve greater operational excellence., for example, is an AI-first solution that is purpose-built for accounting professionals and uses proprietary algorithms to offer highly accurate autonomous invoice processing and bill pay.

6. Is part of a comprehensive platform

Purchasing a standalone AP analytics software is always an option, but it can add tech debt and may require a custom integration to work with existing software. To reduce disparate systems and streamline operations, consider looking for a line-of-business software platform, such as an invoice processing and bill pay solution, with robust analytics included. 

When actionable data and insights can surface within a platform that solves an operational problem, team members will have more visibility and control over the entire process. When exploring potential AP automation solutions, ask about comprehensive analytics capabilities — this should be an essential topic when evaluating potential vendors.

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