Ever since ChatGPT outperformed professional financial analysts in a study by the University of Chicago, they have been asking themselves how they can use the tool as profitably as possible. The field of application in finance and accounting has recently been expanded considerably, and precision is increasing. This is also due to the improved mathematical capabilities of the model, which benefit financial analyses, forecasts and decision-making. Of course, there are still some shortcomings - compared to human analysts and professional enterprise AI software.
Growing relevance for the financial sector
In recent decades, finance has been increasingly characterized by data-driven processes. Complex financial decisions, whether in the area of treasury management, strategic controlling or company valuation, are now made on the basis of extensive data sets and detailed analyses. Against this backdrop, data processing and analysis technologies are becoming increasingly important. Artificial intelligence (AI), particularly in the form of language models, offers opportunities for automation and increased efficiency. Unlike traditional business intelligence tools or statistical models, ChatGPT has the ability to process natural language in a context-sensitive manner. This capability opens up new fields of application, as it allows the tool not only to analyze financial data, but also to present it in a narrative form - a function that is particularly valuable when communicating complex content with non-financial stakeholders.
In practice, ChatGPT can be used for automated financial reporting, scenario analyses or language translation of complex financial reports, among other things. It also offers starting points for decision simulations using hypothetical scenarios. This makes the application a low-threshold entry into data-based financial analyses, even for companies that do not have access to fully integrated enterprise solutions. However, the integration of ChatGPT into everyday professional finance raises important questions. From data protection and ethical use to the validity and robustness of the results - it is essential to take a critical look at these aspects and clearly define the areas in which its use makes sense and where specialized software solutions remain superior.
Practical application possibilities
The versatility of ChatGPT is demonstrated by the breadth of possible applications within the financial value chain. Here are some practical application examples for financial professionals:
Automated creation of financial reports
The preparation of regular reports, such as quarterly or annual financial statements and internal reports, is a central component of day-to-day financial work. KPI reports for management. In this area, ChatGPT can create significant time advantages by processing structured data records.
Example:
A financial analyst enters the most important key figures of a company for the past quarter - such as sales, operating costs, depreciation and net results. With appropriate input, such as "Create a narrative report on sales and cost development as well as net results and point out significant trends", ChatGPT generates a complete, structured report. Optionally, benchmark data can also be included to integrate a peer group analysis.
Development and comparison of scenarios for future cash flows
Another area of application is scenario analysis. ChatGPT can serve as a supporting tool, particularly when predicting future development opportunities, by modeling hypothetical business scenarios according to certain parameters.
Example:
A company's treasury team would like to simulate the effects of an inflation rate of 5 % and a simultaneous reduction in operating costs through efficiency measures. ChatGPT can perform a qualitative assessment of the scenario based on defined parameters (e.g. "What is the impact of scenario X on free cash flow?") and highlight potential risks.
Automation of peer analysis based on public financial data
ChatGPT can be used to compare publicly available reports from listed companies.
Example:
With a query such as "Compare the gross margins of ABC Corp, DEF Corp and GHI Inc. in 2022", ChatGPT extracts relevant information from the reports and generates a structured comparison overview. Such applications significantly save time and reduce the manual effort of reviewing reports individually.
Risk modeling for budget planning
The modeling of risks within a budgeting process is one of the core tasks of controlling. ChatGPT can evaluate hypothetical parameter structures in narrative analyses and thus support the decision-making process.
Example:
A company is preparing a budget plan for the next five years and would like to know what impact an interest rate increase of 2 % could have on projected investment loans. ChatGPT generates a qualitative assessment and suggests potential measures to mitigate the risk.
Further background information
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Fictitious use case: Scenario analysis to increase profits
BetaClean GmbH is a medium-sized supplier of cleaning systems for industrial applications. The company is facing the challenge of cushioning rising raw material costs with stagnating sales volumes. To address this, the management is considering a price increase of 8 %, coupled with optimizations in the supply chain. The aim is to secure the profit margin for the coming financial year. All data was generated and processed using ChatGPT as an example.
Calculations and scenario analysis
1. initial figures 2023 (reference):
- Turnover: € 100 million
- Variable costs: € 55 million
- Fixed costs: € 20 million
- Profit: € 25 million
2. forecasts for 2024 - without adjustment:
- Turnover remains at € 100 million, fixed costs remain constant.
- Variable costs increase to € 58 million.
- Profit falls from € 25 million to € 22 million (-12 %).
3rd forecast with price increase (+8 %) and reduced demand (-2 %):
- Calculated turnover:
€100m x (1.08⋅0.98)=€105.84m - Variable costs: Increase proportionally:
55m€ x 105.84/100=57.9m€. - Fixed costs remain unchanged at € 20 million.
- Profit:
105.84m€ - (57.9m€ + 20m€) = 27.94m€.
Results in comparison:
| Scenario | Turnover (€ million) | Variable costs (€ million) | Fixed costs (€ million) | Profit (€ million) |
|---|---|---|---|---|
| 2023 (actual) | 100,00 | 55,00 | 20,00 | 25,00 |
| 2024 (old, unoptimized) | 100,00 | 58,00 | 20,00 | 22,00 |
| 2024 (new, optimized) | 105,84 | 57,90 | 20,00 | 27,94 |
Findings from the modeling
- The planned price increase of 8 % more than compensated for the decline in demand of 2 %. As a result, turnover rose to € 105.84 million.
- Profit improves despite increased variable costs (€ 57.9 million) and remains robust at € 27.94 million. This corresponds to an increase of € 27 % compared to the unsupported 2024 scenario (€ 22 million).
- Supply chain optimization will only have a limited impact as long as raw material prices continue to rise. Continuous monitoring of these factors is essential.
Visualization
The following prompt can be used to visualize the development of the parameters:
Prompt:
"Create a stacked bar chart that compares the turnover, variable costs, fixed costs and profit of BetaClean GmbH in 2023, in the unoptimized scenario 2024 and in the optimized scenario 2024. The X-axis should show the years (2023, 2024-old, 2024-new), the Y-axis the amounts in € million. Use contrasting colors to clearly highlight the individual parameters."
Additional visualization idea:
- A separate line graph to show the percentage profit share of sales (profit margin).
- Color highlighting of the "leap" in profit development due to the optimization measures could be another added value.
Study: ChatGPT compared with analysts
The Study by the University of Chicago investigated the performance of GPT-4, one of OpenAI's most advanced language models, in financial analysis. The aim was to find out whether a Large Language Model (LLM) like GPT-4 is able to predict the future profit development of companies based on standardized, anonymized balance sheet and profit and loss statements (P&L) - without any textual context. An innovative method was used here: so-called "chain-of-thought" prompts systematically guided the model through the analysis process of a financial analyst. This approach included key steps such as identifying trends, calculating key financial ratios and drawing conclusions about future developments.
The result: GPT-4 achieved a prediction accuracy of 60 %, which is above the range of 53-57 % typically achieved by human analysts. In addition, a F1 score of 0.609 was achieved, which further underlines the precision and robustness of the predictions. The researchers note that GPT-4's extensive knowledge base and pattern recognition capability allows it to draw intuitive conclusions even with incomplete data. Impressively, GPT-4 has proven itself in a traditionally difficult area for AI - namely numerical analysis. Despite its potential, however, the researchers emphasized that the model's results should always be validated by human experts. The study emphasizes that such LLMs could not replace the work of financial analysts, but rather complement it and make it more efficient by speeding up time-consuming tasks such as financial data analysis. The study thus shows the transformative potential of AI in finance, especially in precise and data-driven decision-making.
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Limitations and risks
Despite the potential benefits of ChatGPT in financial analysis, financial professionals should be aware of some critical limitations and risks. These arise from both the technical nature of the tool and the specific requirements of the financial sector:
- Data quality and scope for interpretation - The accuracy and reliability of the insights provided by ChatGPT depends on the quality of the underlying data. Inaccurate or insufficiently cleansed data can lead to misleading forecasts or analyses. In addition, ChatGPT's ability to interpret data correctly is limited to the available inputs and contextual information, which can be problematic for complex financial models or market determinations. Errors due to text-based probability assumptions cannot be ruled out either.
- Limited industry specificity and application understanding - Even though ChatGPT has impressive text processing capabilities, the AI lacks a deep understanding of nuanced industry or business-specific topics. For example, the model is not able to comprehensively understand the highly specialized requirements of areas such as investment banking, risk management or treasury operations. This can lead to industry-specific patterns, legal peculiarities or regulatory requirements being overlooked.
- Data protection, security and compliance - A key risk lies in the processing of sensitive data by public AI models. Financial data is among a company's most sensitive information and is subject to strict legal and regulatory requirements. Feeding such data into an AI such as ChatGPT can not only violate data protection regulations such as the GDPR, but also create potential security vulnerabilities. Companies must ensure that confidential information does not end up in external systems whose security mechanisms can only be traced to a limited extent.
- Lack of transparency and traceability - The "black box" nature of many AI models, including ChatGPT, is an obstacle for many financial experts. The lack of transparency in decision-making can be particularly problematic in the context of compliance audits or the creation of audit-proof documentation. It is essential for financial professionals to be able to justify analyses or forecasts in detail - a requirement that generative AI models cannot always fulfill.
The solution - specialized AI software
Konfuzio's specialized AI software overcomes ChatGPT's limitations in financial analytics by providing industry-specific functionalities, highest data security and traceability:
- Functionality - Konfuzio is specialized in specific financial applications such as Balance sheet analysis or Analysis of annual financial statements recognizes data from complex formats and delivers precise, customizable results that take industry-specific requirements into account. Data pipelines can be individually defined in order to be used for corresponding Traceability and controls to provide. In addition, individually customizable Conversational AI for banks available.
- Data security & hosting - With the possibility of On-prem hostingguaranteed GDPR compliance and full data control, Konfuzio minimizes risks that exist with public AI models such as ChatGPT. Sensitive financial data remains on its own servers or in the private cloud.
- Integration - Konfuzio can interact with various company applications via web-based interfaces and is therefore not dependent on manual input. Compared to ChatGPT, a significantly greater variety of data is used with a higher degree of automation.
Conclusion
ChatGPT is a relevant tool for operational financial processes, especially when it comes to increasing the efficiency of standardized tasks. For complex applications with high demands on precision, transparency and data protection, specialized software solutions, such as those offered by Konfuzio, remain indispensable. The decisive criterion when using ChatGPT is the integration of the most robust validation and control processes in order to guarantee the integrity of all financial results.
Would you like to carry out secure and precise financial analyses using AI? Feel free to send us an Messageto get advice from experts.
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