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Highlights
- Today, accounting is more than just the simple act of bookkeeping— tracking debits and credits— it's the language of business strategy and a system of managing all items that can be measured in monetary terms.
- In continuous accounting, key activities happen at short intervals through automation using Robotic Process Automation (RPA), Machine Learning (ML), and Artificial Intelligence (AI), so that accountants and decisionmakers always have access to real-time data and insights.
- Enterprises need to relook at long-standing accounting practices which were established back in the age of paper-based systems, systems that may no longer be the best practice and must now be automated to increase visibility, control, and efficiencies.
- Transitioning to continuous accounting is a strategic move and requires detailed planning, and TCS has a recommended transition planning approach
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BACKGROUND
Today, accounting is more than just the simple act of bookkeeping— tracking debits and credits— it's the language of business strategy and a system of managing all items that can be measured in monetary terms.
Accountants translate the complexities of finance into information that the various teams within an organization can understand.
Most enterprises want their finance organization to be a source of "quick decision-making" that uses real-time, accurate financial data to identify errors early, capitalize on opportunities, and respond to changing markets.
Business and pressure go hand-in-hand. CFOs of leading organizations are prioritizing transformation by adopting technologies and delivery models that reduce unit costs and enhance business forecasting. This approach frees up critical capacity for mergers and acquisitions, capital re-investment, and rapid, data-driven decision-making.
OVERVIEW
What is continuous accounting?
Traditional accounting teams wait until just before the end of a month to carry out various finance close tasks. In a common period-end scenario, a company's finance department closes transaction processing for the prior month, reconciles accounts, creates adjusting journal entries, runs currency revaluations, calculates margin eliminations, and creates consolidated statements.
Most of that work begins just after the last day of each month and continues until the work is done. When close activities are disseminate through the entire month, instead of pushing for completion at the end of the month, accountants are far less fatigued and overloaded by those peak days at the end of every month and have more time to carry out value added work like analysis.
In continuous accounting, key activities happen at short intervals through automation using Robotic Process Automation (RPA), Machine Learning (ML), and Artificial Intelligence (AI), so that accountants and decisionmakers always have access to real-time data and insights.
Continuous accounting aims to modernize the process by integrating accounting tasks into the natural flow of daily business. This type of accounting is a contemporary approach that utilizes digital interventions like RPA, ML, and AI to track and reconcile every aspect of a business's financial activity in real-time.
With continuous accounting, a finance department spreads closing tasks over time and attempts to complete as much work as possible before the actual period-ending date. This allows you to make informed decisions about resource allocation, funding strategies, and growth initiatives as your month end close become smoother and faster.
APPROACH
The continuous accounting approach represents the end of the traditional month-end close.
The continuous accounting approach is based on three pillars.
- Automate repetitive accounting tasks which are transactional in nature
Distribute the workload in small chunks over a short period (i.e., one month)
Carry out tasks at smaller intervals regularly and rigorously and look for continuous improvement opportunities
The approach requires enterprises to relook at long-standing accounting practices which were established back in the age of paper-based systems, systems that may no longer be the best practices and must now be automated to increase visibility, control, and efficiencies. Companies that move beyond traditional financial closing cycles gain an edge by responding to market shifts instantly.
AI AND AUTOMATION
AI and automation are reshaping continuous accounting.
Traditionally, accountants focus on meticulous number-crunching, complex calculations, and compliance-driven tasks. AI is creating a new era where machines take on the monotonous, rule-based functions, allowing accountants to focus more strategic activities to realize greater value.
The following use cases gives detailed insights into how RPA, ML, and AI will be leveraged in the continuous accounting journey.
Intercompany: The new approach is to reconcile intercompany [IC accounts] regularly at short intervals. This will help avoid discrepancies, issues at month end, and early resolutions of Due to Due From [DTDF] disconnects, if any. AI-powered risk analysis of intercompany transactions and applied AI with predictive controls help find errors, recommend fixes, and provides guidance based on historical behavior before transactions are booked. Streamlined intercompany processes eliminate complex IC reconciliations manually.
Transaction matching: Manual task can now be automated using RPA. These tasks could include sorting, data insertion, form completion, and interpretation of text and data. The above example of an intercompany approach would be a good candidate for transaction matching for unreconciled items using AI. Another candidate could be bank reconciliation and overall GL reconciliations.
Data entry automation: Using RPA and ML, invoices, receipts, payments, expenses reports can be coded and posted in the GL accounting system. Also, at the month end accountants can schedule a list of automated journals to run and post on a specific day. This process also enhances audit efficiency and monitors compliance with company policies.
Bank reconciliation: With continuous accounting, enterprises can perform bank reconciliation at short intervals so that subledgers for payables and receivables can be updated regularly. This provides updated outstanding reports for both suppliers and customers. An updated aging report for receivables will help speed up the collection, as updated data for outstanding receivables is available near real-time. RPA, ML, and AI can help build a risk matrix of reconciliations based on balances and required adjustment trends, types of account, explanation details, and user feedback. This leads to an efficient and improved reconciliation process. AI powers the process of matching financial transactions with corresponding invoices and between general ledgers and bank statements. Through pattern identification and data analytics, AI tools can promptly identify inconsistencies and anomalies and trigger further review by human accountants. This not only accelerates the overall reconciliation process but also enhances accuracy by minimizing the risk of errors and omissions.
Cash application: Continuous accounting allows users to process receipts from customers and carry out cash application rapidly so that updated outstanding receivable reports are made available. This also updates bank reconciliations and reduces customer sub-ledger reconciliation issues. Similarly, once payments were made to vendors, immediate cash application would be recognized on the updated outstanding payables reports. This would help update the bank reconciliation with clean payables aging and reduce vendor sub-ledger reconciliation issues. RPA, ML, and AI work together to automate and optimize the entire process, reduce manual efforts, improve accuracy, and accelerate cash flows.
Allocation of expenses: The new process can allocate expenses at short intervals and does not require users to update expenses as a “batch” at the month end. Such rule-based allocations can easily be automated using RPA. At times organizations use allocation of activity-based expenses using capacity, units, activity level, etc., and RPA and ML can be used to automate this entire process.
Expenses reimbursement: A straight-through process, which allows reimbursement of expense claims as per company policies using RPA, ML, and AI, leads to less time spent on accruals at period-end. Automating such low value reimbursement of expenses quickly also helps improve employee morale. AI can help flag out-of-policy claims before submission.
AP invoice processing: Coding of accounts payable invoices to the correct general ledger expense account, matching open purchase orders to supplier invoices can be simplified by continuous accounting using RPA and ML as an ongoing activity. By automating the repetitive and manual AP invoice processing tasks, you can increase efficiency, reduce errors, and free up resources for more analytical work. Also AI can be leveraged to identify duplicate invoices, over payments, and unauthorized vendors.
The principles applied in the use cases mentioned above can be summarized as:
a) RPA will handle your structured, rule-based data entry
b) AI and ML will process your unstructured data and improve accuracy over time
c) OCR and NLP will extract and interpret text from various sources
TRANSITION PLANNING
Transitioning to continuous accounting is a strategic move and requires detailed planning.
The TCS recommended transition steps include:
- Mapping and identifying processes and bottlenecks: Conduct a granular analysis of your current accounting cycle and the process steps involved in your closing process. Identify bottlenecks that create delays and errors.
Envisioning the future state: Using the analysis created in step one, envision the ideal state of your accounting functions once they are built on the continuous accounting approach described above. While arriving at your future state, special attention has to be given to tasks which can be automated and the integration of various systems and platforms to ensure seamless data input and output for real-time updates and redistribution of the workload among team members.
Creating a master tasks list: Break down your month-end, quarter-end, and year-end task lists into small manageable tasks and steps.
Merging tasks into a daily work list: Arrange the task list created in step three into a daily schedule of tasks to be perform by the team. Ensure that the task list becomes part of routine day-to-day activities and that tasks are carried out regularly at smaller intervals.
Bringing in automation: Identify opportunities for automation using RPA, ML, and AI for repetitive tasks like invoice processing, cash application, reconciliations, etc.
Checking continuously for improvements: Monitor and closely track the tasks list using technology platforms and check the effectiveness of your continuous accounting strategy. For example, you can measure metrics like the number of days to close.
Reviewing results regularly: Carry out regular reviews to compare results with your planned vision, created in step two. Learning from such reviews will help refine your continuous accounting strategy for the future.
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THE WAY FORWARD
Continuous accounting improves visibility into your financial future by comparing close performance and driving continuous improvement using the latest information available in real time.
This strategic approach improves control with system-driven close performance monitoring which allows you to increase accuracy by identifying discrepancies and delaying and plugging in resources immediately to rectify any issues.
Efficiency is enhanced as manual repetitive tasks are automated, templates are standardized, and system-driven tracking becomes a way of life.
By combining data analytics with continuous accounting and using past financial data and industry benchmarks, you can better create predictive models. This allows you to forecast various P&L components and cash flows with improved accuracy.
Continuous accounting goes beyond operational improvements and paves the way for thinking about financial management in the organization at large. Companies that adopt this approach are shifting to real-time decision-making with proactive financial strategies to mitigate risk with increased chances of secure, long-term success.
