# Startup Sustainability Forecasting with Artificial Intelligence
## 3 Results
### Artificial Intelligence System for Startup Sustainability Forecasting  
The aforementioned survey revealed that stakeholders are primarily interested in an AI system that can forecast the sustainability of pre-seed startups. Furthermore, according to our study, the feasible data sources can be organized into five categories: business plan, web activity, psychometric assessment, financials, and macroeconomics. The business plan presents well-organized sections that can be extracted through NLP and presented in a structured format. The structured format includes records about the founders and their experience, the qualitative attributes of the product, the size of the potential market, and the competitors. This extraction process can utilize techniques such as Named Entity Recognition (NER), whereas the final output contains both tabular and graph structures. The data relating to web activity have to be extracted through web crawlers. The web crawlers will scan the startup website, including the available activity on social media. Then, an NLP process that is similar to the one described for business plans can be used to extract more structured data. The aforementioned sources, along with the personality assessment results through the serious game, form a repository of structured data that mainly describe the founders. However, they also describe other related entities such as their products and services, the market, and their competitors. A second source of data includes the financials, which may be historical or future projections. These capture the historical or expected profitability and growth of the startup. Another resource regards external macroeconomic indicators, as these can also have an effect on the success rate of startups. Indicators such as interest rates, inflation, and consumer sentiment can impact funding options and the levels of expected revenue. These data are organized as timeseries. The structured data along with the timeseries data form the final dataset that is used for training the machine learning algorithms. Supervised learning requires that we label a portion of the startups in this dataset. The first option is to use labels assigned by experts, such as empirical sustainability scores. Alternatively, self-supervised learning can be used to automate label extraction by forward-looking the status of previous startups. The exact nature of the label will depend on the task, as described below. Assuming the existence of labeled datasets, we investigate the potential of four supervised learning paradigms for solving issues related to startup sustainability: regression, classification, ranking, and survival analysis. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Trait** & **Mean Personality** & **Characteristics** \\ \hline Openness & high & creativity and adaptability, crucial for innovation and identifying opportunities \\ Consentiousness & medium & organization, reliability, and goal-setting, essential for effective business management \\ Extraversion & medium to high & networking, leadership, and communication \\ Agreeableness & high & teamwork, negotiation, and maintaining positive interactions with others \\ Neuroticm & low & emotional stability, good management of stress and ability to make rational decisions \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Personality assessment resultsRegression can be used to forecast a numerical feature related to a startup, also providing a corresponding confidence interval. Expert-assigned labels in the form of startup sustainability ratings can be used, or alternatively, a self-supervised label depending on future cashflow or runway (the amount of time (usually in months) that a startup has before it needs to secure additional funding). In either case, probabilistic regression can also indicate the worst-case-scenario rating or future cashflow, which should be considered for a potential investment. Classification is useful for predicting discrete states in which the startup will be or events that will happen.