In the contemporary business landscape, enterprises grapple with the pursuit of marginal profits, recognizing that the bedrock of their success lies in financial sustainability. Consequently, the mitigation or prevention of business financial risks (BFR) emerges as a pivotal factor in enhancing firms' profitability prospects. Various technological strategies are employed to address the challenges posed by financial risks, with machine learning (ML) standing out as a particularly pertinent and effective solution across diverse business sectors. In the current milieu, the significance and efficacy of ML are keenly acknowledged, with businesses leveraging this cutting-edge technology to tackle challenges associated with BFR.
TopIntroduction
Machine Learning (ML) holds significant relevance in the realm of business, particularly in the domain of financial analysis, as highlighted by Naim (2022). The infusion of ML has ushered in a new and diverse dimension to finance, contributing to the feasibility of business operations (Lahmiri and Bekiros, 2019). Notably, the application of ML has proven instrumental in providing firms with a competitive edge, leading to substantial advantages in their business practices (Leo et al., 2019). The current business landscape witnesses a widespread integration of Artificial Intelligence (AI), ML, and Deep Learning (DL) across various operational facets, resulting in notable achievements and returns for enterprises. Figure 1, as presented by Wong et al. (2022), delineates the levels and definitions of AI, ML, and DL.
The year 2000 marked a pivotal moment with a severe crash affecting businesses across all sectors. Subsequently, Information Technologies began expanding their applications to mitigate risks and automate business processes extensively. Technologies such as Artificial Intelligence, Machine Learning, Deep Learning, and blockchain experienced rapid growth, impacting nearly every domain of business processes. During this period, ML and AI played crucial roles in assessing Business Financial Risk (BFR) and extending their applications to mitigate such risks.
It is imperative to note that Machine Learning (ML) is not a substitute for Artificial Intelligence (AI); rather, it is a subfield of AI, as elucidated by Naim (2022). In broad terms, AI is defined as the ability of a system to act, behave, and respond like a human. A paramount benefit of AI lies in its facilitation of complex decision-making processes, akin to the cognitive actions of humans (Leo et al., 2019). ML, on the other hand, can be conceptualized as a tool for transforming systems to think and respond in a manner reminiscent of human cognition. To achieve this objective, ML employs neural networks, employing sequences of algorithms that enable systems to emulate human responses and actions (Naim, 2022).
Figure 1. Definition of AI, ML and DL
ML has four types and figure 2 shows the general depiction.
Figure 2. (Wong et al., 2022) ML has received overwhelming acceptance from users from all disciplines such as financial analysts, social media analysts, advertising and media experts (Lahmiri and Bekiros 2019). This is because of ML’s advancements in using Natural Language Processing, Computer Vision, and Robotics. The business practices which are based on quantitative analysis and have big data sets extensively use ML types for solutions (Lahmiri and Bekiros 2019).
Business Financial Risks (BFR) is measured by the applications of ML and financial analysts take decisions to reduce risks and improve profit and financial efficiency (Naim and Hassan, 2022).
Financial Risk (FR) is a concept that explains the ability of any business to take care and deal with financial instabilities. These instabilities can be described as the liabilities and financial commitments any firm has to pay back. FR mostly ascends because of loss in business and financial markets, changes in stock market and in process, change in the currency value, etc.