In the fast-evolving world of finance, the adoption of hyperautomation is revolutionizing how organizations approach Financial Accounting. Hyperautomation, combining the powers of Low Code/No Code (LCNC), Robotic Process Automation (RPA), and Cognitive AI, is reshaping processes within Accounts Receivable (AR), Accounts Payable (AP), and Record to Report (R2R). Let’s explore how these technologies converge to create a more intelligent and streamlined Financial Planning and Analysis (FP&A) landscape.
What is Hyperautomation
Hyperautomation is an advanced, integrated approach to automation, combining technologies like Robotic Process Automation (RPA), Artificial Intelligence (AI), machine learning, and Low Code/No Code (LCNC) platforms. It extends beyond basic automation to create smarter, more adaptive systems capable of handling complex tasks and decision-making processes. By leveraging this blend of technologies, hyperautomation enables organizations to significantly enhance efficiency, accuracy, and productivity, particularly in intricate domains like financial accounting.
Below table lists some of the key usecases for hyperautomation within each of the sub process area of financial accounting
| LCNC (Low code/ No code) | RPA | Cognitive AI | |
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AR |
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AP |
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R2R |
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Accounting |
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Financial planning & Analysis |
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Digital toolbox
In the realm of Hyperautomation, the digital toolbox is a dynamic ensemble of cutting-edge tools and technologies that collectively propel organizations towards unprecedented efficiency and innovation. This toolbox typically includes:
- Low Code/No Code (LCNC) platforms, which empower non-developers to design and deploy automation solutions with minimal coding efforts. Microsoft Power platform, Outsystems are some of the technologies that can be leveraged.
- Robotic Process Automation (RPA) tools play a pivotal role, automating repetitive and rule-based tasks, ensuring accuracy and speed in data processing. UiPath, Automation anywhere, Microsoft Power Automate can be used of building RPA BOTs
- Complementing these, Cognitive AI tools bring intelligence to automation, enabling systems to learn, adapt, and make informed decisions based on complex data patterns.
Machine learning algorithms within this toolbox facilitate predictive analytics, offering organizations the ability to forecast trends and make data-driven decisions. Together, these digital tools form the cornerstone of Hyperautomation, providing organizations with the agility and scalability required to navigate the complexities of modern business processes in an increasingly digital world.
Illustrative usecases for hyperautomation:
1. Low Code/No Code (LCNC):
Automated Collections Workflow:
Follow-up process for overdue payments, issuing dunning letters, monitoring payments are some of the subprocesses that can be automated. Automation streamlines invoicing, payment processing, reconciliation, reducing manual errors and accelerating cash flow for improved liquidity.
Automated Invoice Approval Workflows:
Automations help streamline invoice processing, approval workflow and payment disbursement, improving accuracy, reducing cycle times, and enhancing overall efficiency.
Automated Journal Entry Creation:
Journal entries assist the client with year-end activities such as closing books for taxation, reconciliations, and restatements. Creating automated workflows for routine journal entries regarding a transaction like date, amount, purpose, payee, payor etc will help reducing manual data entry and minimizing the risk of errors.
2. Robotic Process Automation (RPA):
Invoice Processing Automation:
RPA bots can be deployed to extract information (invoice amount, due date, customer address) from incoming invoices, enter data into the system, and initiate approval workflows. This reduces manual effort, minimizes errors, and accelerates the payment cycle.
Vendor Payment Automation:
RPA bots perform 2 or 3 way match and help in automating the reconciliation of purchase orders, goods receipt and vendor invoices, ensuring accuracy in invoice processing.
Bank Reconciliation Automation:
RPA bots automate the reconciliation of bank statements with the general ledger, ensuring accuracy and real-time financial records.
3. Cognitive AI:
Dynamic Credit Risk Assessment:
Cognitive AI algorithms can be created to analyse historical payment data, customer behaviour, and market trends to carry out credit risk assessment, enabling organizations to make informed decisions on credit terms, minimize bad debt exposure, and optimize working capital.
AI-Powered Invoice Data Extraction:
Cognitive AI algorithms are applied to extract relevant information from invoices, enhancing accuracy and expediting the invoice processing cycle.
Anomaly Detection in Financial Data:
Cognitive AI analyses large datasets to detect anomalies in financial transactions, helping finance teams proactively identify and address potential issues.
Conclusion:
Hyperautomation in Financial Accounting, powered by the convergence of LCNC, RPA, and Cognitive AI, is reshaping the landscape of FP&A. Organizations that strategically implement these technologies are not only optimizing their financial processes but also positioning themselves as agile and resilient players in the dynamic world of financial planning and analysis. As the journey toward hyperautomation continues, the potential for efficiency gains, accuracy improvements, and strategic insights becomes increasingly evident, promising a transformative future for financial accounting.
Nextwealth specializes in delivering hyperautomation services, offering a blend of AI, RPA, and advanced analytics to streamline financial processes. Our expertise lies in enhancing operational efficiency and accuracy, transforming the way businesses handle financial accounting.