Financial Risk assessment of AI industry using a new machine Learning model

Lead Research Organisation: University of Manchester
Department Name: Alliance Manchester Business School


AI business has become the most anticipated technology industry and attracts a lot of investment. AI technology is closely related to all aspects of our lives and promotes innovation in traditional industries, including healthcare, education, telecommunications, manufacturing, retail, finance, etc. Moreover, at the national level, it affects citizens' safety and privacy, climate protection, industrial governance and economic policies. Countries are committed to building and developing long-term AI strategies. Therefore, the healthy and sustainable development of AI industry is essential. However, there are many AI start-ups that have become bankrupt due to suffering from financial risks, which has brought adverse impacts on stakeholders and society. Financial risks exist in every part of business management and are affected by various uncontrolled factors, which may result in poor financial status, credit default or even bankruptcy. It is imperative to build a warning model to assess and predict the financial distress of AI firms.

Financial risk assessment is in essence a multiple criteria decision analysis (MCDA) problem, aiming to sort many alternatives or select the best solution through information aggregation. This study will use the Evidence Reasoning (ER) approach which is a data-driven machine learning method to predict corporate financial risk. Unlike traditional industries, the operation of AI industry has greater uncertainties, such as larger investment in R & D, more rapid technology update, and higher uncertainty in capital recovery periods, profit models or market demand forecast. General quantitative financial indicators alone (e.g. operational capability or profitability) cannot comprehensively evaluate the financial risks of AI start-ups. Combining the characteristics of AI start-ups, this research will also introduce a variety of qualitative criteria, such as investor status, technological innovation, team strength, talent motivation, market potential, competitive environment, human resource risk, etc. The ER approach is unique in dealing with MCDA problems of both quantitative and qualitative criteria, and will therefore be applied in this research. Data will be collected from failure and non-failure of AI start-ups during 2015-2019 in UK, USA and China, and training data and validating data will be separated by time. The research objects are firms that provide products or services highly correlated with AI technology, for example, firms whose main business income comes from AI research and development, including speech recognition, computer vision, natural language processing, cloud computing, sensors, robots, etc. More than 10,000 firms will be observed. Furthermore, in-depth interviews with a number of selected firms' managers or experts will be conducted by choosing two firms from each of the countries to be studied (e.g. China, USA and UK) as case studies to test the accuracy of the model. In particular, this research will consider whether AI start-ups are affected by COVID-19 since businesses may take years to recover from this pandemic.
The contributions of this study are as follows. (1) This research will build a new financial risk prediction model of AI start-ups through the use of advanced data analytics and the ER approach with appropriate qualitative criteria, in order to enrich the theory of financial risk management. (2) It will provide an early warning mechanism to assist AI companies to identify problems or defects in financial management and to improve managers' risk awareness and ability. In addition, it can help investors and other stakeholders to rationally invest in or cooperate with AI firms. (3) Since AI technology has an important impact on economic development and social progress, this study can support countries to make AI industry specifications or technical standards, thereby guiding the healthy and sustainable development of AI industry.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/P000665/1 01/10/2017 30/09/2027
2488399 Studentship ES/P000665/1 01/10/2019 30/09/2022 Mengmeng Tan