  The core activity of a Private Equity (PE) firm is to invest into companies
in order to provide the investors with profit, usually within 4-7 years. To
invest into a company or not is typically done manually by looking at various
performance indicators of the company and then making a decision often based on
instinct. This process is rather unmanageable given the large number of
companies to potentially invest. Moreover, as more data about company
performance indicators becomes available and the number of different indicators
one may want to consider increases, manual crawling and assessment of
investment opportunities becomes inefficient and ultimately impossible. To
address these issues, this paper proposes a framework for automated data-driven
screening of investment opportunities and thus the recommendation of businesses
to invest in. The framework draws on data from several sources to assess the
financial and managerial position of a company, and then uses an explainable
artificial intelligence (XAI) engine to suggest investment recommendations. The
robustness of the model is validated using different AI algorithms, class
imbalance-handling methods, and features extracted from the available data
sources.
