A seamless, consistent, and accurate record to report processing calls for detecting anomalies in real time, the least manual intervention, and meaningful, real-time insights. Discover how record-to-report automation helps businesses achieve this.
As month-end approaches, CFOs and accountants across industries face immense pressure to ensure accurate and timely financial recording and reporting. With legacy accounting software in place, the struggle amplifies manifold as they find themselves in the maze of innumerable challenges. These include manual tasks and processes, follow-ups, slow financial closing, delayed decision-making due to a lack of real-time insights, siloed operations leading to poor user experience, and whatnot.
The only way businesses can solve these challenges is to automate the R2R process with autonomous accounting software that helps CFOs and accountants focus on what matters more and rely on automation and AI to detect anomalies in real time, automate tedious tasks, and deliver predictive insights for informed decision-making. In addition, they can also make financial close a proactive exercise with a record to report software identifying errors and rectifying them immediately, thereby cutting monthly close cycle time by 33%. Here are six ways AI-driven, record-to-report automation is emerging as a game changer for businesses.
● Reconciling and Matching Large Data Sets
Autonomous accounting software offers AI-driven rule discovery technology that automatically identifies and configures optimal matching rules, significantly reducing manual intervention. This helps businesses enable precise and efficient line-level transaction matching across multiple data sources, ensuring accurate and consistent reconciliation, streamlining financial close processes, and mitigating risks of human error. Additionally, AI assists businesses in unlocking up to 90% of transaction auto-match rates.
● AI/ML Driven Financial Task Automation
AI-powered tools driven by machine learning (ML) have transformed the record to report automation for tasks like data entry, transaction matching, and reconciliation. It leverages historical data, recognizing patterns and processing transactions at high speed and accuracy. The solution also automates bank and ERP data ingestion for bank reconciliation and automates data processing with little to no manual intervention, ensuring error-free and speedy reporting. In fact, the majority of businesses said that they were able to achieve a 90% accuracy rate using automated record-to-report software.
● Precise and Accurate Financial Reporting
AI algorithms help identify discrepancies and anomalies within vast datasets and effectively flag transactions that deviate from historical trends or patterns. This further helps reduce errors and enhance the integrity of financial statements. Additionally, autonomous accounting software offers AI-based identification of incorrect general ledger (GL) postings in the ERP data and also creates customizable, out-of-the-box reports and dashboards to enable businesses and stakeholders to get a bird’s view of potential omissions, errors, or misreporting in the source data.
Apart from this, autonomous accounting software provides features for automated journal entry preparation with just a one-time template setup. It also offers a highly customizable journal workflow that enables analysts and managers to track the progress and, in turn, take action and automate the posting of journal entries back to source ERP systems.
● Advanced Data Analysis
Automation of record-to-report processes helps analyze large volumes of data and employ predictive analytics and data mining techniques. AI identifies trends and patterns to provide meaningful insights into revenue forecasting, expense analysis, and better liquidity management. This helps accountants save time from automated data processing. The accounts team can then use this time to focus on other critical tasks during peak load like audit preparedness, adjustments, reporting, etc., thus further increasing the close efficiency.
● Predictive Financial Analytics
Autonomous accounting software, with the help of AI, analyzes historical financial data, market conditions, and other relevant external data sources and delivers forecasts with a high-level of accuracy and consistency. Additionally, the solution comes with self-serve analytics and allows accountants to track business-specific or any other type of custom metrics.
● Robust Anomaly Detection and Fraud Prevention
Anomaly management solution allows users to identify accounting ‘errors and omissions’ using AI. The record to report automation identifies errors by reviewing all current month-posted transactions and comparing them to the history of similar transactions. Similarly, the system detects omission by looking at the history of posted transactions and not finding a similar transaction in the current close month. Businesses later can get these anomalies as a worklist for the accountants’ review, almost like a real-time internal auditor.
In addition, the solution gives anomaly resolution in the form of automated corrective suggestions and anomaly workflows. It also corrects journal entries for some anomaly patterns to expedite the close process and increases an accountant’s productivity.
Accelerate Month-End Close With Automated Record To Report Software
Getting out-of-the-box features for the above capabilities is no cakewalk. These third-party integrations are not only fragile but also incur high costs and require heavy maintenance and reliance on the IT department. The right record to report software offers over 100 pre-configured close checklists and task templates to help you with quicker implementation of the solution. Moreover, your accounts and finance team can leverage Excel-like, easy-to-use templates for low-priority use cases, consolidate workflow management, automate journal posting, conduct daily revenue reconciliation, and more. Additionally, with record automation, you reduce days to close by almost 30% and increase efficiency by up to 40%.
