Efficiency Enhancement through AI: Opportunities and Limitations in the Small-Cap Sector for FDD

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AI tools can be utilized in FDD within the Small Cap segment for further optimization, automation, and support. Similar to LDD, the AI tool in FDD can be used for analyzing extensive documents to extract essential information. A fundamental and recurring step in FDD is to transfer the profit and loss statement (P&L) as well as the balance sheet and other data such as customer evaluations from documents like financial statements into a meaningful format. This process is necessary to conduct further analyses based on it. Artificial intelligence can automate these tasks and significantly contribute to enhancing the efficiency of the entire process. Generally, AI tools are also suitable for conducting initial content evaluations or quality checks.

AI is often associated with human thinking. It is modeled after the process through which humans become adaptable to their environment through learning. AI models work in a similar framework. To continually advance the learning of the AI model, it requires a large amount of data from which it can learn. The model is provided with fitting and desired output concerning the data. With each pass through the data, the model learns the properties of the data to generate the corresponding output. Difficulties arise already during data collection for model development. Documents from Small Cap companies are often difficult to use as training data set for models due to data protection regulations and disclosure requirements. The AI model can only be as good as the data used to train it. 

Training AI models in the Small Cap environment is particularly challenging. Data consistency is generally not given here, and the available data often only exist in scanned form, without proper controlling. The lack of standardized processes and irregular booking of transactions make it significantly challenging to create a coherent and reliable training set for AI. These unpredictabilities and irregularities pose a significant hurdle, as AI models typically rely on consistent data to effectively learn patterns and make predictions. Dealing with unstructured data, especially in small companies, may require special adjustments and more intensive manual monitoring of the training process. The use of the AI model often faces unknown document types and errors, which are more common in the Small Cap sector. This leads to impairments in the quality of the model output. Adjusting the model in turn requires historical data, which may not be available in sufficient quantities. As a result, the entire process lags behind, affecting the effectiveness of the models. Another general problem with AI is its difficulty in understanding industry-specific language or peculiarities. Additionally, it is not always transparent how the model made a particular decision. Overall, this means that the implementation of AI in Small Cap companies requires not only technological expertise but also a deep understanding of the individual peculiarities and challenges of these companies. Therefore, the need for human expertise remains to ensure the quality and accuracy of the AI model.

Specific challenges arise in the training process of AI models in the Small Cap sector, as data consistency is often not given or more prone to errors in smaller companies. The development of AI tools specifically designed for the FDD process is progressing relatively slowly. However, the combination of Financial Due Diligence and artificial intelligence holds significant potential for enhancing efficiency and more targeted use of resources. However, it is important to note that even advanced AI models are not error-free. Indeed, in many cases, human input is still essential to ensure the quality and accuracy of the analysis and to detect possible errors. Therefore, the integration of AI into the FDD process should be considered as a complement to human expertise to achieve optimal results.