Finance and accounting (F&A) are critical to the operational efficiency and strategic decision-making of any business. The advent of artificial intelligence (AI) presents a transformative opportunity for these functions. This article analyzes manual, analytical, and strategic activities within these functions and determines the most optimal AI adoption roadmap.
The following table estimates the volume of manual, analytical, and strategic activities in these functions as high, medium, or low:
Functions | Manual | Analytical | Strategic |
Procure to Pay | High | Medium | Low |
Order to Cash | High | Medium | Medium |
Expense Management | High | Medium | Low |
Tax and Compliance | Medium | High | High |
Treasury | Medium | High | High |
Financial Planning & Analysis | Low | High | High |
Mergers & Acquisitions | Low | High | High |
The next-generation AI technologies are mature and can be applied well in Finance and Accounting with a significant financial impact. We recommend prioritizing the Procure-to-Pay, Order-to-Cash, and Expense Management functions for AI adoption.
AI techniques for interpreting unstructured data have advanced significantly in recent years. These techniques now permit what was previously considered human-level intelligence tasks.
Transformer-based frameworks allow for unstructured content understanding, language generation as well as predictive tasks. Large Language Models (LLMs) accelerate the ability of AI systems in language understanding, information retrieval, summarization, text generation, and conversational AI. Data-driven econometrics models for forecasting and trend analysis enable numeric and financial data analysis.
In finance automation, this is how these AI techniques can radically transform each of these tasks:
The structured and orderly nature of finance processes, underpinned by a robust ERP knowledge base, provides a solid foundation to leverage sophisticated machine learning and AI methodologies. Now is an opportune moment to invest in the adoption of AI-native strategies for a substantial positive business impact.
The P2P function involves numerous repetitive and manual activities where AI can significantly increase efficiency and reduce errors.
AI Capabilities | Readiness |
Uses machine learning and LLMs to achieve straight-through processing for 80% of invoices. This includes automated invoice extraction, understanding, validation, matching, GL coding, and posting | Short-term |
Uses forecasting systems to automate accruals | Short-term |
Uses predictive and prescriptive models for optimal vendor payment timings | Short-term |
Uses advanced ML techniques to detect fraudulent and duplicate invoices | Short-term |
Uses classification techniques to classify expenses for capitalization | Short-term |
Uses AI models and tax dictionaries to verify the sales and other types of applicable taxes | Short-term |
Builds company and F&A-specific conversational AI models to provide chatGPT-like analytics | Medium-term |
Optimizes vendor selection using predictive analytics | Medium-term |
The O2C function is also highly manual and prone to AI automation.
AI Capabilities | Readiness |
Uses machine learning and text processing techniques to extract and validate purchase order information from customers PO documents and contracts and auto-uploads information into the ERP system, resulting in 95% plus automation | Short-term |
Uses advanced AI techniques to generate customer invoices based on purchase orders, customer master, inventory, and shipment information to generate 100% First Time Right (FTR) invoice. | Short-term |
Uses recommender systems to create a daily/weekly priority list of customers for collections. | Short-term |
Uses dynamic models to enhance customer credit scoring | Short-term |
Uses advanced data science techniques for cash management including discovery of discrepancies, over and under-payments | Short-term |
Uses generative AI to automatically communicate with customers on invoices and payments, including follow-ups | Short-term |
Uses generative AI for conversational analytics on O2C data | Medium-term |
Employee expense management continues to be tedious for employees and the finance teams. This process can make use of AI to achieve a very high degree of automation.
AI Capabilities | Readiness |
Uses image and text processing techniques to automatically extract information from receipts and bills, and validate and auto-create expense reports for employees. | Short-term |
Uses machine learning techniques to verify expense reports against policies and proofs. | Short-term |
Uses advanced ML techniques to detect fraudulent and duplicate expenses. | Short-term |
Uses classification techniques to identify the correct GL code for each expense | Short-term |
Uses generative AI to communicate and answer employee queries | Medium-term |
AI has a high potential to optimize and streamline the tax and compliance function.
AI Capabilities | Readiness |
Automates collection and validation of data required to file tax returns, ensuring higher accuracy and reduced human effort | Medium-term |
Applies the correct withholding rates based on payer and recipient jurisdiction, reducing errors | Medium-term |
Helps organize documentation related to taxation for audit purposes | Long-term |
Tracks applicable sales and use taxes across jurisdictions, ensuring accurate application to transactions | Long-term |
Uses generative AI to map financial statements to the latest reporting standards. Facilitates SOX compliance | Long-term |
AI can provide substantial value by automating routine activities and improving decision-making in treasury management.
AI Capabilities | Readiness |
Machine learning models analyze historical cash flow patterns to predict future cash needs. | Medium- term |
Monitors cash balances across accounts and recommends the most efficient pooling techniques. | Medium- term |
Compares fee structures across banks, helping to negotiate better terms. | Long-term |
It uses advanced predictive models to assess market risks and helps optimize long-term portfolio allocation. It also recommends low-risk, high-return, short-term investment opportunities. | Long-term |
Predicts currency fluctuations to help develop effective hedging strategies. Identifies forex arbitrage opportunities. | Long-term |
Builds models to identify market and operational risks using various internal and external debts. | Long-term |
FP&A involves many analytical and strategic activities. AI can help improve decision-making for these activities.
Automates the data extraction from structured and unstructured sources like documents and ERPs | Long-term |
Analyzes the historical data to build predictive budgets and rolling forecasts | Long-term |
Simulates scenarios and recommends outcomes | Long-term |
Helps in variance analysis between planned and actual budgets | Long-term |
Analyzes capital allocations and predicts ROI using historical data | Long-term |
Predicts future cashflows based on historical trends | Long-term |
AI can play a significant role in M&A, improving efficiency and strategic decision-making.
AI Capabilities | Readiness |
Analyzes financial reports and news articles to assess potential targets. | Long-term |
Builds sophisticated financial models using machine learning to provide a more accurate valuation of the target companies. | Long-term |
Analyzes contracts and other financial statements for risks and liabilities. | Long-term |
Identifies and predicts potential risks. | Long-term |
Provides data-backed insights into potential negotiation points. | Long-term |
Now that we have analyzed the specific AI-based automation of the above finance functions, we can estimate the financial impact it can create.
Having evaluated the financial impact on all F&A functions, we can recommend the AI adoption roadmap.
The next-generation AI technologies are mature and can be applied well in Finance and Accounting with a significant financial impact. We recommend prioritizing the Procure-to-Pay, Order-to-Cash, and Expense Management functions for AI adoption.
Moderated by Niharika Sharma, Head of Marketing at Hyperbots
Niharika: Good morning and welcome to today’s discussion, everyone. In the realm of finance and operations, the quest for efficiency is ever-present, with organizations constantly seeking to streamline processes and optimize resources. One area that stands out as both critical and challenging is the end-to-end straight-through processing of invoices. Today, we are joined by Anna Tiomina, an experienced CFO who will shed light on why achieving this goal remains a significant technological challenge. Anna, thank you for joining us today. Could you please start by explaining why achieving end-to-end straight-through processing of invoices is still such a formidable task?
Anna Tiomina: Good morning. Thank you for having me here. Yes, indeed, in spite of all the technological progress and advancements, manual invoice processing is still prevalent in many organizations. I came across research by Arden Partners, which said that approximately 40% of businesses still rely on manual methods for processing invoices. The primary reason for that is the complexity of the invoicing process itself. From the moment an invoice is received to its final payment, there are multiple touch points, many stakeholders involved, and many points of failure along the way. Each step involves various systems, checks, formats, and levels of human intervention, which introduces complexities.
Niharika: Can you elaborate on some of the specific challenges involved in achieving straight-through processing?
Anna Tiomina: Yes, certainly. One of the key challenges is data quality. Invoices come in various formats through different channels. Sometimes they lack standardization, and sometimes they lack specific information, so you need to do a lot of data preparation to make it possible for an automated process to take these tasks. Even with advancements in optical character recognition or data extraction technologies, there are still a lot of errors and discrepancies when using technology to extract data from invoices. Another challenge is integration. Many organizations operate in different systems for procurement, accounts payable, and ERP, and achieving seamless integration between these systems to enable end-to-end automation is a complex task. It requires a lot of customization and testing. And last but not least, compliance and regulatory requirements add another layer of complexity. Invoices must comply with tax regulations, accounting standards, and internal policies. These may vary across jurisdictions and industries, and ensuring that automated processes adhere to these standards and requirements without compromising efficiency is a huge challenge.
Niharika: Right, it seems like there are multiple layers to consider in this case. How do you envision overcoming these challenges?
Anna Tiomina: Yeah, that’s a very good question, and it requires a 360-degree approach. Companies can start by investing in advanced technologies, such as artificial intelligence or machine learning. This can improve the accuracy and efficiency of invoice processing. These technologies can also help to improve data extraction and decision-making as they learn from historical data and previous mistakes, reducing the need for manual intervention over time. There is a learning curve when using these technologies. Secondly, to make this work better, organizations should focus on standardizing processes and data formats to streamline integration. For some organizations, this is an easier task, but for others, it’s more challenging, especially when they operate in a versatile market environment and work with many vendors from different industries and types of companies. Here, collaboration with external partners and vendors becomes essential. Working closely with suppliers and service providers, organizations can establish common standards and protocols for invoice exchange, which will, in the future, reduce friction and complexity in the invoicing process.
Niharika: Fascinating insights. As we wrap up, what do you see as the future of end-to-end straight-through processing of invoices?
Anna Tiomina: Well, I think that with the current advancements in technology, especially in the field of artificial intelligence, this is becoming a much more attainable task. At this point in time, organizations should invest in exploring these new technologies, ensuring that their internal processes are well prepared for the adoption and integration of these new technologies. With further integration of AI-driven solutions, companies will have many more opportunities to achieve an end-to-end STP process, automating all the steps along the way. This will reduce costs, enhance transparency, compliance, and overall business agility. So, I feel very optimistic about this, and I hope that the 40% of organizations relying on manual processing will be reduced to a maximum of 5% in the next couple of years.
Niharika: Thank you for answering that for us, Anna. I think the discussion has been very fruitful. Thank you for the valuable insights, and it’s been a pleasure speaking with you today.
Anna Tiomina: Thank you. Thank you for having me.