Generative AI (Gen AI) has huge potential in accounting processes, as they require both extensive professional expertise and repetitive tasks. However, its adoption within the accounting sector has been notably slower compared to other sectors such as marketing. The accounting sector is currently on the wave of adopting cloud solutions, moving away from physical ledgers and siloed systems. As such, the accounting process is ripe for disruption, and we see many opportunities in leveraging Gen AI to transform the accounting landscape.
Before we dive into the use cases, it is good to keep in mind the strengths and weaknesses of Gen AI to better understand its application in the use cases.
Gen AI Strengths
Firstly, Gen AI is very versatile and efficient in processing unstructured data such as texts. It can convert these unstructured data into a structured format such as tabular data which is more useful for tasks. As such, many manual tasks involving large amounts of documents and texts can now be automated at scale. Secondly, Gen AI is very good at generation tasks such as generating a reply based on a query and generating personalized recommendations. This allows more human-like interactions between systems and users which previously were performed through a human agent or the system user interface. Thirdly, Gen AI is also good at understanding language, summarization and translation tasks.
Gen AI Weaknesses
However, Gen AI do have a few weaknesses to note. Firstly, while Gen AI seems to be good at understanding and performing maths, it is often falls short in logical reasoning and deduction, and multi-step planning without additional support systems. Secondly, it is difficult to control the outputs and make adjustments to the Gen AI models. These models are mostly black boxes, making it difficult to understand why a certain output is provided. This could be a critical weakness in many use cases where transparency, explainability and controls are crucial. Thirdly, Gen AI is not deterministic in nature, meaning that it may provide different outputs given the same inputs. This lack of reproducibility would be a concern for many systems with high precision or accuracy requirements. Gen AI systems are also highly dependent on the data it is trained with. It will display the same bias that exist in the data, and mitigating these biases is extremely difficult without going through the time and resource-intensive re-training of the model.
In short, Gen AI excels at processing unstructured text and imitating human-like responses, making it especially useful for tasks such as document processing, data entry, as well as providing suggestions and recommendations. However, for tasks that require high-precision, involve high-risk decision-making, or where transparency and control are critical, it is not recommended to rely solely on Gen AI without additional validation measures.
Top Use Cases in Accounting
The following use cases demonstrate the strengths of Gen AI, where it automates complex tasks by including general reasoning into workflows, generates customized and actionable insights by understanding the context, and enable high-level strategic decision-making by providing narratives to the analysis.
Automating AR/AP Processes
Industry: All industries
Problem: Due to complex approval processes and diverse supplier/customer base, accounting professionals often find themselves in an intricate tangle of manual processing and time-consuming email correspondences, which significantly burdened the financial operations and administrative workflows.
Solution: Gen AI has the capability to enhance AR/AP processes by:
Automating the generation and processing of invoices.
Aligning invoices with corresponding purchase orders and payment terms.
Tracking and scheduling payments.
Proactively issuing reminders for overdue balances.
This advancement reduces manual data entries, thereby reducing errors and streamlining workflows, and helps ensure timely payments and healthy cash flows. Moreover, AI's ability to scrutinize payment behaviours can enable companies to predict potential delays and suggest proactive measures, contributing to company’s overall financial health.
Example: When a company sells goods or services to its customers, AI tools can extract data (e.g. goods/services sold, quantity, amount, etc.) from contracts and purchase orders and monitor the transaction. The extracted data can be reviewed by accountants prior to inputting into the company’s accounting system or ERP. Once services and goods are delivered, it calculates the receivables to generate invoices, tracks payments and sends personalized reminders to clients, and collects payments if integrated with the banking system. It can also analyze payment behaviors, identify clients that consistently pay late, which can be used to adjust credit terms or ask for upfront payment, and forecast future cash flows.
Industry Example: Higher Ground, a global school operator managing an extensive network of campuses, leveraged Gen AI to transform their financial process. A centralized AI engine now handles a substantial portion of their invoice processing and approvals across different locations, significantly reducing administrative overhead and improving financial efficiency. [1]
Streamlining Expense Management
Industry: All industries
Problem: For businesses with numerous store locations, managing expenses can be daunting, particularly when dealing with complex invoices that span several entities. This complexity often leads to a heavy load of accounting work, which can slow down operations and affect the company’s financial health.
Solution: Gen AI can transform expense management by taking over tasks such as:
Scanning and extracting relevant data from receipts.
Organizing expenses into categories.
Validating entries against company policies.
Recognizing spending patterns and anticipating future expenses.
The use of AI technology reduces manual labour, minimizes errors, and saves time for finance teams, allowing managers to handle financial records and control costs more effectively.
Example: When companies or staffs make purchases from suppliers, AI-driven tools can scan documents to extract relevant information from receipts, compare and validate against other documents such as POs, sort expenses into predefined categories, validate against company policies, generate ledger entries, and analyse spending patterns to predict future expenses, all of which contributing to streamlined processes and optimized cash flows.
Industry Example: A franchise owner of 9 UPS stores in South Dakota utilized AI-powered approach to automate expense management. It can analyse payment history, categorize expenses, handle multi-store accounting, and constantly learn from historical transaction patterns, streamlining the preparation of financial reports and resulting in cost savings. [2]
Simplifying Tax Compliances
Industry: All industries
Problem: Tax professionals are often facing the task of accessing accurate data from diverse sources, communicating complex tax issues, and keeping up with rapid regulatory changes, all of which hinder the strategic impact tax professionals can make.
Solution: Gen AI tools offer a breakthrough in handling the nuanced processes of tax compliance and advisory services. These tools can:
Automate data processing and compilation for tax reporting purposes.
Enhance existing compliance technologies by providing a narrative to the analysis.
Analyse tax and business data, summarize tax due diligence findings, formulate tax planning strategies, and evaluate the tax implications of business decisions.
Enhance stakeholder communication by translating complex information into easily understandable formats.
Note: It is recommended to use AI tools for tax advisories as a decision support tools, and not relying on it solely due to the potential high implications of errors in tax filings.
Example: AI-powered tools can leverage its understanding of natural languages and contexts to analyse complex transactions, evaluate tax implications, answer challenging tax research questions, and generate tax optimization strategies. This could help tax professionals reduce the time spent in research and reporting and focus on advising clients on more complicated issues.
Industry Example: KPMG’s virtual assistant powered by Gen AI helps employees to collect and analyse tax data and prepare corporate tax reports. Tax professionals can now navigate the complexities of global tax obligations more smoothly and tackle new regulatory requirements in corporate taxation. [3]
Generating Comprehensive Business Intelligence
Industry: All industries
Problem: In the context of financial planning and analysis (FP&A), the following pain points highlight the challenges faced by organizations:
Complex data management due to the large volume and variety of data sources, making it difficult to accurately interpret and utilize the information.
The need for frequent updates due to dynamic market conditions, making the process time-consuming and error-prone, as teams struggle to keep pace with rapid changes.
Traditional methods are inefficient in generating actionable insights, and often fail to capture the unique aspects of specific businesses or industries.
Solution: Gen AI could revolutionize FP&A by providing context-aware and customized insights. The generation of insights is made possible through Gen AI capabilities, including:
Contextual Data Analysis: By processing both structured and unstructured data and understanding the context and nuances, Gen AI provides a holistic view of the financial landscape, leading to accurate insights.
Coherent Narrative Generation: Leveraging natural language processing, Gen AI can produce clear summaries of business performances.
Customized and Dynamic Insights: Gen AI can generate tailored recommendations based on specific business needs and changing market scenarios, offering personalized insights for strategic decision-making.
Example: Gen AI-powered tools can automatically import data into templates to create recurring financial reports. By combining financial reports, market intelligence and other internal data, it can conduct predictive and budgeting analysis to optimize business processes and detect anomalies. It can also provide personalized narrative or reports for business stakeholders to easily access important financial data and metrics for faster and better decision making.
Industry Example: A large multinational retailer in the United States leveraged a Gen AI tool to analyse diverse internal data, identify key performance drivers across business lines and deliver insights for each region, highlighting profitable and growth areas. This enabled the FP&A team to make informed decisions and provide strategic recommendations to executives. [4]
Keep an eye on this page (WIP) as we continuously gather and share the most interesting use cases of Generative AI in different areas.
If you're keen to dive deeper into the world of Generative AI, explore our recommended articles on fundamentals of Gen AI, the cost of LLMs, how to assess Gen AI projects, and the EU AI Act.
Note: The information provided in this article is for informational purposes only. We do not have any business relationship with the vendors and solutions mentioned herein, nor do we endorse or recommend them in any way.
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