Impact of machine learning on accountants and auditors

Accounts Payable's (AP) procure-to-pay process is a critical financial procedure that affects corporate profitability. Accounts Payable accountants have often stressed the need for automation in Accounts Payable; and the benefits and necessity of using it. 

An automated AP solution automates manual activities to increase accuracy. But only when combined with AI and Machine Learning can this automated AP solution deliver in terms of AP performance, productivity, and, ultimately, the bottom line. Traditional audit methods for Accounts Payable (AP) in financial statement audits can be streamlined using artificial intelligence (AI) and machine learning (ML) technology. As such, AP auditing is one such process that can leverage the power of Machine Learning to become more accurate and efficient.

Before we see how machine learning impacts account payable auditing and why should professionals learn accounts payable automation, let's first understand what is accounts payable auditing. 

What is Account Payable Auditing? 

Accounts Payable auditing enlists the help of an independent accountant or accounting firm to review your payable documentation. The accounts payable auditor compiles a report that outlines any faults found or general inefficiencies and provides suggestions for your company's future operations. 

The typical account payable auditing follows a 4-step process: Planning, Fieldwork, Report, and Follow-up. 

Planning

The accounts payable auditor notifies your organization of its intention to audit your accounts payable records. You schedule a meeting to go through all that will be addressed in this audit as well as the expected outcomes.

Based on the anticipated scope and the feedback received during the planning meeting, the auditor creates a plan and outline of the AP audit procedure.

Fieldwork

They'll look at relevant documentation, trades, accounts payable standard procedures, balance sheets, and vendor verifications to verify if your company's accounting measures are in order. The duration of an AP audit depends on the accounting complexities, the technique used to preserve financial data, the thoroughness of your paperwork, and if serious mistakes or fraud are discovered throughout the process. If the audit uncovers any inconsistencies, the auditor will mark the files and revisit them for a more thorough examination.

Report

The auditor writes a detailed report outlining their findings and suggestions. This data is delivered to senior management as well as key stakeholders. The auditor will answer questions regarding what they discovered and if the organization is adhering to best accounting standards as outlined by generally accepted accounting principles. They also discuss their suggestions for enhancing the accounts payable process, which might involve adjustments to controls, systems, and other procedures.

Follow up

In a year, the accounts payable auditor will contact your firm again. They check in to verify whether you've made the suggested changes and if the anticipated outcome from the planning stage has materialized.

At that point, your organization may wish to do another account payable auditing to see whether you've made improvements and if there are any additional suggestions for improvement.

How Does Machine Learning Benefit Auditors?

Increases efficiency

Currently, auditors frequently do manual audits of businesses and depend on statistical sampling to examine hundreds of documents. Using machine learning systems, large collections of papers can be scanned and examined. Furthermore, the systems that analyze the data can spot trends and abnormalities. Using a machine learning approach to determine transactions that have features linked with fraudulent operations, possible fraud in a company's financial statements might become easier to detect.

Global corporations confront considerable and increasingly complex tax compliance obligations; distributing income and costs to numerous taxation jurisdictions necessitates extensive data processing and analysis. Machine learning can assist tax professionals in staying current with applicable tax legislation. Machine learning is useful for developing algorithms that extract meaningful planning information from large volumes of data. Without the necessary and significant facts, effective tax planning is difficult; machine learning may make the information gathering and evaluation function much more efficient and effective.

Increases the number of data points

Auditors usually concentrate on the scant evidence that auditees provide. They then ask for further proof based on their previous experience. Machine learning and AI-enabled systems, on the other hand, can swiftly extract vast volumes of data from textual material, pictures, voice recordings, and other sources. As a result, auditors will no longer have to rely solely on evidence given by auditees. Deep learning algorithms can extract relevant and contextual information from a stream of different sources, such as contracts, conference calls, and emails, and use it as supporting evidence.

Continuous Auditing can be made possible

A continuous control monitoring system based on artificial intelligence might scan whole sets of records and detect control infractions. Based on the provided data, the system might additionally include a dashboard prioritizing the amount of danger associated with these infractions.

When fresh data is received, the AI system can analyze it right away and transform it into useful knowledge. The continuous control monitoring system may rearrange itself depending on input from prior outcomes thanks to machine learning and deep learning algorithms. This method can assist in guaranteeing that controls are planned, set, and applied as efficiently as possible with the least amount of human interaction.

Improved Collaboration in Auditing

AI-powered auditing tools enhance collaboration among audit teams. They enable real-time data sharing, remote access to financial records, and automated workflow management. Auditors can collaborate seamlessly across locations, ensuring efficient information exchange, and fostering teamwork to deliver more accurate and timely audits.

Reduced Costs in Auditing

AI streamlines auditing processes by automating routine tasks such as data extraction, categorization, and reconciliation. This reduces the need for extensive manual labour and decreases auditing-related costs. Additionally, AI-powered analytics can quickly identify discrepancies and anomalies, preventing potential financial losses and costly errors.

Learning and Improvement in Auditing

AI continuously learns from historical audit data, allowing it to improve accuracy and efficiency over time. By recognizing patterns and evolving its algorithms, AI-driven auditing systems adapt to changing regulations and business environments. This ensures that audits remain up-to-date and relevant, providing valuable insights for clients and auditors alike.

Predictive Analysis in Auditing

AI excels in predictive analysis by identifying potential risks and fraud indicators. It can analyze vast datasets to pinpoint irregularities or suspicious activities, helping auditors proactively address issues before they escalate. Predictive analytics in auditing not only improves risk management but also adds a layer of security and assurance to financial reporting.

Final Say 

Machine learning's advancement is likely to have a significant influence on business. It can help auditors improve their fraud detection and emphasize internal control. Accounting professionals will need to adapt to understand better how companies use technology to better focus their efforts on auditing financial statements. As the field develops and adapts to new technological advancements, learning accounts payable and harnessing the power of machine learning becomes vital for professionals. As accounting companies continue to leverage the promise of machine learning, young professionals will have access to new and exciting possibilities.