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 Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, or good afternoon depending on where you are. I am very pleased to have you back. The topic that we will be discussing today is large language models and their applications in accounting. But before we dive into it, Ayo, would you mind telling us a little more about yourself?
Ayo Fashina: Hi Emily, it’s good to see you again. My name is Ayo Fashina, I’m the CFO of Kobo360. It’s an e-logistics company startup, about five going on six years old now. We match goods owners to transporters and operate in seven African countries.
Emily: Got it. Thank you so much, Ayo, for the introduction. Today we’ll be talking about large language models (LLMs). It’s a vast topic in itself, but let’s start with the basics. Ayo, can you explain what large language models are and how they are relevant to the field of accounting?
Ayo Fashina: Thank you, Emily. In short, a large language model (LLM) is a type of artificial intelligence program that can recognize and generate text, among other tasks. LLMs are trained on huge datasets, hence the name large. They are built on machine learning, specifically a type of neural network called a transformer model. They analyze large datasets to learn what to look for when queried and use that knowledge to improve their performance. This makes them highly relevant to accounting, which involves data analysis and data collection.
Emily: Got it. Traditional methods of data analysis in accounting are human-based. Accounting staff or finance analysts do all the data entry and analysis, often using tools like Excel. How do LLMs compare to these traditional methods?
Ayo Fashina: One of the most significant benefits of LLMs is that they substantially increase the efficiency of accounting tasks. These models can process large volumes of data at an unprecedented speed, drastically reducing the time accountants spend on routine tasks such as data entry, transaction categorization, and report generation. Essentially, LLMs make the life of an accountant easier.
Emily: Understood. Can you dive into some specific applications of LLMs in accounting that you’ve come across or implemented?
Ayo Fashina: One application I’ve come across involves processing accounts payable. LLMs are trained on data from invoices, purchase orders, and delivery notes. They can match these documents to process accounts payable on behalf of accountants and even handle general ledger entries. Impressively, these models can read both typed and handwritten documents, which is a significant advancement.
Emily: How do LLMs contribute to improving efficiency, accuracy, and decision-making in accounting tasks?
Ayo Fashina: LLMs can process a lot of data very quickly and with high accuracy, thereby minimizing human errors. They ensure consistency in handling transactions and applying rules uniformly, which is crucial in accounting. By automating routine tasks, LLMs allow firms to allocate human resources to more strategic tasks, enhancing overall productivity. They also provide valuable insights for financial forecasting and decision-making by identifying trends and patterns that might be overlooked by human analysts.
Emily: What challenges do finance professionals face when implementing LLMs in accounting processes, and how can these challenges be addressed?
Ayo Fashina: Despite their advanced capabilities, LLMs lack human judgment and the ability to interpret complex and ambiguous financial situations. They operate based on the data provided to them without understanding the nuances of certain accounting decisions. Therefore, LLMs should be applied to tasks where human judgment is not a high priority. It’s also crucial to have robust data validation processes to ensure the quality of data used to train LLMs, as inaccurate data can lead to erroneous outputs.
Emily: What opportunities do LLMs present for innovation and advancement in accounting?
Ayo Fashina: LLMs hold great promise in revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. They can be used for risk assessment, fraud detection, and even forensic accounting. For example, LLMs can analyze data to uncover hidden patterns and trends, help identify risks, and flag anomalies in transactions, which aids in fraud prevention.
Emily: With the increasing use of LLMs in accounting, how do you ensure the security and privacy of sensitive financial data?
Ayo Fashina: Ensuring data confidentiality and security is paramount. Data hygiene is essential, meaning the data used to train LLMs should be sanitized of any personal identifiable information. Organizations should implement stringent data cleaning and sanitation procedures to remove sensitive information and identify potential biases and errors in the data.
Emily: How important is it for LLM-driven accounting solutions to integrate seamlessly with existing financial systems?
Ayo Fashina: It’s very important. If an LLM solution cannot integrate with existing systems, it defeats the purpose of having the LLM in the first place. Full integration ensures that the efficiency gains from LLMs are realized. Without it, the benefits are eroded by manual data transfer, which reintroduces human error.
Emily: What strategies do you recommend for ensuring smooth integration and compatibility with other accounting and enterprise systems?
Ayo Fashina: Testing compatibility ahead of full implementation is key. The provider of the LLM-driven solution should ensure seamless connection to existing systems and workflows. A sandbox test run before full implementation can help identify and resolve any integration issues.
Emily: What steps should organizations take to ensure compliance with relevant regulations while leveraging LLMs in accounting operations?
Ayo Fashina: Regulatory compliance is dynamic and always changing, which poses a challenge to LLMs. Ongoing monitoring and adjustments are required to keep LLMs compliant, which can be resource-intensive but necessary. Developers should ensure that LLMs are regularly updated to reflect changes in regulations.
Emily: Are there any emerging trends or advancements in LLMs that you believe will shape the future of accounting and finance?
Ayo Fashina: LLMs have the potential to revolutionize the finance sector in numerous ways. They can be used for risk assessment, fraud detection, and forensic accounting. LLMs can also simplify audit processes by organizing data for easier transaction tracing. As these technologies evolve, they will continue to provide valuable insights and efficiencies.
Emily: Based on your experience and insights, do you have any additional advice or recommendations for financial professionals looking to harness the power of LLMs?
Ayo Fashina: Financial professionals should consider learning Python, a versatile programming language that many LLMs are based on. Understanding Python fundamentals can help professionals leverage LLMs for code generation and task automation. As LLMs become more prevalent, the demand for Python skills will grow, making it a valuable skill for finance and accounting professionals.
Emily: Thank you so much, Ayo, for sharing your expertise on the applications of large language models in accounting. Is there anything else you would like to add before we conclude our discussion?
Ayo Fashina: Just to summarize, LLMs hold great promise for revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. However, it’s crucial to be aware of their limitations and the need for human judgment. Data security and regulatory compliance are also important considerations. Thank you, Emily, for having me. It’s been a pleasure.
Emily: Thank you, Ayo. It was great having you, and this discussion was truly insightful.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, or good afternoon depending on where you are. I am very pleased to have you back. The topic that we will be discussing today is large language models and their applications in accounting. But before we dive into it, Ayo, would you mind telling us a little more about yourself?
Ayo Fashina: Hi Emily, it’s good to see you again. My name is Ayo Fashina, I’m the CFO of Kobo360. It’s an e-logistics company startup, about five going on six years old now. We match goods owners to transporters and operate in seven African countries.
Emily: Got it. Thank you so much, Ayo, for the introduction. Today we’ll be talking about large language models (LLMs). It’s a vast topic in itself, but let’s start with the basics. Ayo, can you explain what large language models are and how they are relevant to the field of accounting?
Ayo Fashina: Thank you, Emily. In short, a large language model (LLM) is a type of artificial intelligence program that can recognize and generate text, among other tasks. LLMs are trained on huge datasets, hence the name large. They are built on machine learning, specifically a type of neural network called a transformer model. They analyze large datasets to learn what to look for when queried and use that knowledge to improve their performance. This makes them highly relevant to accounting, which involves data analysis and data collection.
Emily: Got it. Traditional methods of data analysis in accounting are human-based. Accounting staff or finance analysts do all the data entry and analysis, often using tools like Excel. How do LLMs compare to these traditional methods?
Ayo Fashina: One of the most significant benefits of LLMs is that they substantially increase the efficiency of accounting tasks. These models can process large volumes of data at an unprecedented speed, drastically reducing the time accountants spend on routine tasks such as data entry, transaction categorization, and report generation. Essentially, LLMs make the life of an accountant easier.
Emily: Understood. Can you dive into some specific applications of LLMs in accounting that you’ve come across or implemented?
Ayo Fashina: One application I’ve come across involves processing accounts payable. LLMs are trained on data from invoices, purchase orders, and delivery notes. They can match these documents to process accounts payable on behalf of accountants and even handle general ledger entries. Impressively, these models can read both typed and handwritten documents, which is a significant advancement.
Emily: How do LLMs contribute to improving efficiency, accuracy, and decision-making in accounting tasks?
Ayo Fashina: LLMs can process a lot of data very quickly and with high accuracy, thereby minimizing human errors. They ensure consistency in handling transactions and applying rules uniformly, which is crucial in accounting. By automating routine tasks, LLMs allow firms to allocate human resources to more strategic tasks, enhancing overall productivity. They also provide valuable insights for financial forecasting and decision-making by identifying trends and patterns that might be overlooked by human analysts.
Emily: What challenges do finance professionals face when implementing LLMs in accounting processes, and how can these challenges be addressed?
Ayo Fashina: Despite their advanced capabilities, LLMs lack human judgment and the ability to interpret complex and ambiguous financial situations. They operate based on the data provided to them without understanding the nuances of certain accounting decisions. Therefore, LLMs should be applied to tasks where human judgment is not a high priority. It’s also crucial to have robust data validation processes to ensure the quality of data used to train LLMs, as inaccurate data can lead to erroneous outputs.
Emily: What opportunities do LLMs present for innovation and advancement in accounting?
Ayo Fashina: LLMs hold great promise in revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. They can be used for risk assessment, fraud detection, and even forensic accounting. For example, LLMs can analyze data to uncover hidden patterns and trends, help identify risks, and flag anomalies in transactions, which aids in fraud prevention.
Emily: With the increasing use of LLMs in accounting, how do you ensure the security and privacy of sensitive financial data?
Ayo Fashina: Ensuring data confidentiality and security is paramount. Data hygiene is essential, meaning the data used to train LLMs should be sanitized of any personal identifiable information. Organizations should implement stringent data cleaning and sanitation procedures to remove sensitive information and identify potential biases and errors in the data.
Emily: How important is it for LLM-driven accounting solutions to integrate seamlessly with existing financial systems?
Ayo Fashina: It’s very important. If an LLM solution cannot integrate with existing systems, it defeats the purpose of having the LLM in the first place. Full integration ensures that the efficiency gains from LLMs are realized. Without it, the benefits are eroded by manual data transfer, which reintroduces human error.
Emily: What strategies do you recommend for ensuring smooth integration and compatibility with other accounting and enterprise systems?
Ayo Fashina: Testing compatibility ahead of full implementation is key. The provider of the LLM-driven solution should ensure seamless connection to existing systems and workflows. A sandbox test run before full implementation can help identify and resolve any integration issues.
Emily: What steps should organizations take to ensure compliance with relevant regulations while leveraging LLMs in accounting operations?
Ayo Fashina: Regulatory compliance is dynamic and always changing, which poses a challenge to LLMs. Ongoing monitoring and adjustments are required to keep LLMs compliant, which can be resource-intensive but necessary. Developers should ensure that LLMs are regularly updated to reflect changes in regulations.
Emily: Are there any emerging trends or advancements in LLMs that you believe will shape the future of accounting and finance?
Ayo Fashina: LLMs have the potential to revolutionize the finance sector in numerous ways. They can be used for risk assessment, fraud detection, and forensic accounting. LLMs can also simplify audit processes by organizing data for easier transaction tracing. As these technologies evolve, they will continue to provide valuable insights and efficiencies.
Emily: Based on your experience and insights, do you have any additional advice or recommendations for financial professionals looking to harness the power of LLMs?
Ayo Fashina: Financial professionals should consider learning Python, a versatile programming language that many LLMs are based on. Understanding Python fundamentals can help professionals leverage LLMs for code generation and task automation. As LLMs become more prevalent, the demand for Python skills will grow, making it a valuable skill for finance and accounting professionals.
Emily: Thank you so much, Ayo, for sharing your expertise on the applications of large language models in accounting. Is there anything else you would like to add before we conclude our discussion?
Ayo Fashina: Just to summarize, LLMs hold great promise for revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. However, it’s crucial to be aware of their limitations and the need for human judgment. Data security and regulatory compliance are also important considerations. Thank you, Emily, for having me. It’s been a pleasure.
Emily: Thank you, Ayo. It was great having you, and this discussion was truly insightful.
In the rapidly evolving landscape of finance and accounting, the integration of cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), automation, data security, generative AI, and large language models (LLMs) has very high potential to transform the operational processes. Chief Financial Officers (CFOs) need to be at the forefront of adopting these technologies to drive efficiency and innovation.
This detailed 6-month proposed learning plan is designed to equip CFOs with the necessary skills and knowledge to navigate these changes successfully, with a clear outline of the benefits associated with each section.
1. Skills to Acquire: Basics of data science, statistical analysis, Python programming, introduction to AI and ML concepts, and fundamental data security principles.
2. Courses:
3. Benefits: Acquiring these foundational skills enables CFOs to understand and leverage data more effectively, make informed decisions based on statistical analysis, and implement basic cybersecurity measures to protect sensitive financial information.
1. Skills to Acquire: Advanced ML techniques, AI applications in finance, robotic process automation (RPA), and an introduction to generative AI and LLMs.
2. Courses:
3. Benefits: Learning these skills helps CFOs to automate routine financial tasks, freeing up valuable time for strategic activities. Additionally, an understanding of generative AI and LLMs can unlock new possibilities for data analysis, report generation, and predictive modeling, enhancing the financial decision-making process.
1. Skills to Acquire: Strategic implementation of AI/ML and automation technologies, ethical considerations in AI, leading AI-driven transformation projects, and advanced data security strategies, with a focus on generative AI and LLMs.
2. Courses:
3. Benefits: This final phase empowers CFOs to confidently lead digital transformation initiatives, ensuring they are ethically sound and compliant with data protection laws. Advanced cybersecurity knowledge is crucial for protecting against increasingly sophisticated cyber threats, safeguarding the organizations financial data, and maintaining stakeholder trust.
This learning plan provides CFOs with a robust framework to master AI, ML, automation, data security, generative AI, and LLMs. By embarking on this learning journey, CFOs will gain a competitive edge in the digital transformation of finance, driving operational efficiencies, fostering innovation, and ensuring the highest standards of data security. The skills and knowledge acquired will not only enhance the strategic decision-making process but also position CFOs as visionary leaders in the digital age, ready to tackle the challenges and opportunities that lie ahead in the evolving landscape of finance.
