Effectiveness, Objectivity and Ethics in Business Valuation Through Machine Learning Algorithms
Abstract: In the current economic context, we are witnessing a progressive and unstoppable fusion between Artificial Intelligence (AI) and financial processes, thus fostering new perspectives in corporate valuation. The integration of traditional methods (Discounted Cash Flow – DCF) with new algorithms (Machine Learning – ML) allows for a more dynamic and informed valuation, reducing the margin of error and better adapting to the complexities of the current economic context. However, the adoption of such technologies raises ethical questions regarding transparency, reliability and accountability in automated financial decisions.
Keywords: #ArtificialIntelligence #GenerativeAI #finance #economics #AI #artificialintelligence #economy #machinelearning #DiscountedCashFlow #TechInnovations #CompanyValuation #DigitalTransformation #etica #algoreticcs #diegoserafin #ethicasocietas #ethicasocietasrivista #scientificreview #humansciences #socialsciences #ethicasocietasupli
Lolita Guliman, psychologist and psychotherapist, university professor, expert in assessment and treatment of distress among operators in at-risk sectors, coordinator of working groups relating to family support also through the use of cognitive artificial intelligence.
Diego Serafin, Graduated in Statistical and Actuarial Sciences, with a specialization in Data Science, at the University of Trieste. Scholar of applied artificial intelligence, statistical modeling and complex systems. He is passionate about data science models applied in economic, financial and business contexts.
Introduction
Artificial Intelligence (AI) is transforming how we evaluate companies. Thanks to its ability to analyze large volumes of data and identify complex patterns, AI helps improve the accuracy of valuations. Moreover, AI reduces the subjectivity of assumptions and enhances transparency in the valuation process. These tools, with their capacity for iterative learning and processing vast amounts of data, hold significant promise for improving financial forecasting accuracy and reducing human bias. Business valuation is a critical activity for investors, analysts and companies themselves.
Nevertheless, integrating algorithmic tools into valuation processes is not without issues. Ethical implications tied to model opacity, data biases, and accountability in automated decisions demand careful reflection. In this context, it is essential to explore hybrid models that combine the theoretical rigor of traditional methods with the flexibility and analytical power of emerging technologies.
This contribution aims to propose a business valuation model that integrates the DCF method with Machine Learning techniques, highlighting advantages, limitations, and ethical, organizational, and systemic implications. Even without analyzing a real business case, the article proposes a synthetic application example and focuses on the conceptual architecture of the model, as well as on the critical reflections that its adoption entails for the theory and practice of business valuation.
Traditional Model
The most widespread traditional method is the Discounted Cash Flow (DCF) approach. For decades, this method has been one of the most appreciated tools for estimating a company’s intrinsic value. Its strength lies in its underlying financial logic, which ties a company’s present value to its ability to generate future cash flows, discounted at a rate reflecting business risk. This approach is based on a solid theoretical framework consistent with the core principles of corporate finance.
However, the practical application of DCF is far from neutral. Projected cash flows, the discount rate used, the time horizon considered, and the implicit growth assumptions all require a significant degree of subjective judgment. As a result, valuations that are nominally “objective” may vary considerably depending on the assumptions made by the analyst. Furthermore, the DCF model shows limited responsiveness to sudden changes in economic and financial conditions. Multi-year forecasts often crystallize expectations that may not materialize, especially in unstable, volatile, or rapidly transforming technological markets.
International standards such as the International Valuation Standards (IVS) clearly emphasize the need for caution, transparency, and rigorous documentation in model choices and underlying assumptions. Yet even when well implemented, DCF remains highly dependent on the quality of historical data and the analyst’s forecasting ability.
In summary, while DCF remains a fundamental reference, it exposes the valuation process to risks of distortion, inconsistency, and opacity, particularly when not supported by tools that can adapt to continuous data and business context evolution.
Innovative models: Artificial Intelligence in Business Valuation
Artificial Intelligence (AI), particularly its Machine Learning (ML) subset, is radically transforming how organizations analyze data and make decisions. In business valuation as well, these technologies are emerging as promising tools to complement – and in some cases redefine – traditional methods.
Unlike static models such as DCF, ML-based systems can learn from historical data, identify recurring patterns, and generate adaptive forecasts. Algorithms such as Artificial Neural Networks (ANNs) and gradient boosting models (e.g., XGBoost) can manage a wide array of variables – both financial and non-financial – capturing complex or nonlinear relationships that are often difficult to detect using traditional analysis.
One of AI’s main advantages is its ability to continuously update forecasting models in light of new data, thereby increasing the valuation process’s responsiveness to market dynamics. Additionally, the adoption of data-driven models can help reduce analyst subjectivity, fostering greater methodological consistency.
However, incorporating AI into valuation processes also raises significant concerns. The so-called “algorithmic black box” – the difficulty in understanding the inner workings of some complex models – can compromise the transparency and auditability of valuations. Furthermore, ML models heavily depend on the quality and neutrality of the training data, with a risk of reproducing or amplifying pre-existing biases.
Lastly, the use of AI in business valuation necessitates new professional skills and a redefinition of decision-making roles: human judgment remains essential but must coexist with automated systems in a still-evolving balance.
In this context, AI is not a replacement for traditional methods, but a powerful complement that can strengthen their predictive foundations, provided its use is guided by principles of responsibility, transparency, and critical awareness.
Hybrid Model
In an effort to overcome the limitations of traditional methods without abandoning their theoretical robustness, the proposed model integrates the Discounted Cash Flow (DCF) approach with Machine Learning (ML) techniques, adopting a methodology of complementarity. A hybrid model that combines traditional methods with AI could represent the future. This model helps enhance valuation accuracy, reduce subjectivity in assumptions, and respond more effectively to rapidly changing market conditions.
The underlying logic foresees using ML models to improve the forecasts of key financial variables that feed into DCF, especially future operating cash flows. Instead of relying on subjective estimates or linear projections, ML learns from historical, financial statement, and – when available – exogenous data (industry, macroeconomic, sentiment, etc.) to generate more robust and adaptive forecasts. Once expected cash flows are estimated, the DCF method is applied to determine the firm’s value by discounting these cash flows using a rate that canalso be refined through predictive techniques, while retaining human oversight over critical assumptions. This synergy allows the theoretical structure of DCF to be preserved while enhancing its forecasting capacity and minimizing subjectivity in the assumptions. The result is a more dynamic and transparent model, capable of rapid adaptation to context.
Table 1: Advantages, Limitations, and Precautions of the Hybrid Model
|
Main advantages |
Limitations and Precautions |
| More accurate cash flow forecasts | Operational and technical complexity |
| Greater adaptability to change | Dependence on data quality and availability |
| Reduction in subjectivity | Need for interpretability and human supervision |
| Improved documentation of choices |
Let Us Consider a Summary Example
An investment fund aims to assess the growth potential of an emerging tech company named TechKosei, currently engaged in developing a highly innovative product. Several data sources are available to support the analysis: historical cash flows, industry data, competitive landscape information, and characteristics of the target market.
In this context, adopting a hybrid model unfolds in two main phases. In the first phase, a Machine Learning algorithm analyzes historical and contextual data to estimate the company’s future cash flows, accounting for the market’s complexity and variability. In the second phase, these estimates are used within a Discounted Cash Flow (DCF) model to calculate the present value of the company.
For illustrative purposes, while the application of the DCF alone – based on linear assumptions and deterministic scenarios – produced a business value of approximately 420 million dollars, the ML-integrated model produced a higher estimate of 500 million dollars. It also highlighted a potential annual growth rate of 20% for the next five years. This difference stems from the predictive model’s ability to detect latent dynamics in the data and weak signals often overlooked by traditional models.
The advantages of the hybrid approach are evident: greater alignment with real data, adaptive capacity, and the possibility to continuously update the model as market conditions evolve. However, certain challenges remain, including data availability and quality, as well as the limited interpretability of ML models, which require expert oversight to ensure their ethical and methodological soundness.
The adoption of hybrid models for business valuation, based on the integration of traditional techniques and Machine Learning algorithms, is not a simple technological advancement. It determines structural changes in decision-making processes, which require critical reflection on the ethical, organizational and systemic level. In particular, four key dimensions emerge:
1. Transparency and comprehensibility
One of the main risks associated with the use of AI in valuation processes is the reduction of transparency. Complex models, especially those based on neural networks, can be opaque even for those who have implemented them. This algorithmic “black box” calls into question the principle of traceability of economic-financial decisions, hindering the possibility of verification by third parties. To ensure ethically sustainable use, it is essential to develop explainability mechanisms, i.e. tools and methodologies that make the results of ML models interpretable. The responsibility for the valuation cannot be delegated entirely to the algorithm: human supervision remains essential.
2. Responsibility and accountability
The automation of the valuation process raises questions about the distribution of responsibility. Who is responsible for an incorrect valuation if the forecasts are generated by an algorithmic model? The company being valued, the analyst, the programmer of the model, or the entity that adopts it? It is therefore essential that organizations that employ hybrid models develop a clear accountability framework, capable of distinguishing human responsibilities from algorithmic ones, and that they define internal policies that regulate the adoption of these tools.
3. Bias and algorithmic justice
Another critical aspect concerns the possible reproduction of bias in the data. If the datasets used to train the models reflect pre-existing inequalities or distortions, the model output risks amplifying these dynamics. This can also have significant implications in the assessment of creditworthiness, financial reliability or growth potential, negatively influencing investment decisions. To counteract these effects, it is necessary to act on the quality and representativeness of the data, as well as on the validation and continuous monitoring techniques of the model.
4. Impact on professional roles and the organization
The adoption of hybrid models also redefines professional roles within organizations. Today, financial analysts are required to possess, in addition to traditional skills, a basic knowledge of predictive models, their limits and interpretation methods. This transformation requires companies to invest in continuous staff training and to promote organizational cultures oriented towards collaboration between economic-financial, statistical and IT skills. Only in this way will it be possible to govern innovation responsibly, avoiding technocratic drifts or de-responsibilizations.
Conclusions
The evolution of business valuation methods reflects a broader transformation in how we interpret economic value in the digital age. The integration of traditional approaches, such as Discounted Cash Flow (DCF), with innovative tools based on Machine Learning represents a concrete response to the growing complexity of markets, the need for predictive accuracy, and the abundance of available data.
The hybrid model proposed here does not simply combine two techniques but offers a new synthesis between theoretical rigor and adaptive capacity, where artificial intelligence supports – but does not replace – human judgment. In this sense, technology becomes an enabling tool that can reinforce the robustness of valuations, provided it is adopted with critical awareness.
However, this evolution raises significant ethical and organizational questions: from model transparency to responsibility distribution, from data quality to the transformation of required skills.
Only through careful and multidisciplinary governance will it be possible to ensure the fair, transparent, and responsible use of these technologies.
Ultimately, the transition to more advanced valuation models does not merely involve adopting new tools but requires a cultural shift in how we conceive valuation itself: no longer as a mere numerical estimate but as a systemic, interactive process oriented toward the common good.
References:
Arrieta, A. B., Dìaz-Rodrìguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., . . . Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion.
Bartram, S. M., Branke, J., & Motahari, M. (2020). Artificial Intelligence in Asset Management. CFA Institute Research Foundation.
Bhadamkar, A., & Bhattacharya, S. (2022). Tesla Inc. Stock Prediction using Sentiment Analysis.
Australasian Accounting, Business and Finance Journal (AAB&FJ), 52-66.
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013, Giugno). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, pp. 471-482.
Brealey, R. A., Myers, S. C., & Allen, F. (2011). Principles of Corporate Finance. McGraw-Hill Education.
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., . . . Dafoe, A. (2020). Toward trustworthy AI development: Mechanisms for supporting verifiable claims.
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (p. 785-794).
Copeland, T., Koller, T., & Murrin, J. (2000). Valuation: Measuring and Managing the Value of Companies. Wiley.
Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. Hoboken, Canada: Wiley.
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, p. 1709–1734.
Fabozzi, F. J., Focardi, S. M., & Jonas, C. A. (2014). Artificail Intelligence in Asset Management. Wiley. Farahani, M. S. (2024, Luglio 19). Analysis of business valuation models with AI emphasis. Sustainable Economies, pp. 1-13.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review.
Geertsema, P., & Lu, H. (2023). Relative Valuation with Machine Learning. Journal of Accounting Research, 329-376.
Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., Giannotti, F., & Ruggieri, S. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 1-42.
Haich, A. P. (2021). Deep Learning Applied to Public Company Valuation for Value Investing. A Thesis Submitted to the Graduate Faculty to the North Dakota State University of Agriculture and Applied Science.
Hitchner, J. R. (2011). Financial Valuation: Applications and Models. Hoboken, Canada: Wiley.
Hoang, D., & Wiegratz, K. (2022). Machine Learning Methods in Finance: Recent Applications and Prospects. KIT Working Paper Series in Economics, No. 158.
How AI is Transforming the Business Valuation industry. (2024, Aprile 28). Tratto da Equitest – Valuation Methods Blog: https://equitest.net/business-valuation-blog/valuation-methods- blog/item/295-how-ai-is-transforming-the-business-valuation-industry.html
IEEE. (2017). Cybersecurity and AI Ethics. IEEE Standards Association.
IEEE. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. IEEE Standards Association.
International Valuation Standards Council (IVSC). (2024). International Valuation Standards (IVS). International Valuation Standards Council.
Kantar, L., & Ayrancı, E. A. (2022). Estimating Financial Failure in Businesses Using Artificial Neural Networks: Turkish Manufacturing Industry Model Study. Journal of Corporate Governance, Insurance, and Risk Management, 327-340.
Machová, V., & Vochozka, M. (2019). Analysis of Business Companies Based on Artificial Neural Networks. SHS Web of Conferences.
Mehrabi, N., Morstatter, F., Saxena, N., & Lerman, K. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 1-35.
Mestiri, S. (2024). Financial Applications of Machine Learning Using R Software. Munich Personal RePEc Archive (MPRA).
Miciula, I., Kadlubek, M., & Stepien, P. (2020). Modern Methods of Business Valuation-Case Study and New Concepts. Sustainability.
Mittelstadt, B. D., Allo, P., Taddeo, M., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society.
Nei, P. P., Tobi, S. M., & Jasimin, T. H. (2023). Big Data Application in Automated Valuation Model for Valuation Process. Journal of Advanced Research in Business, 1-15.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.
Torrez, J., Al-Jafari, M., & Juma’h, A. (2006). Corporate Valuation: a Literature Review. Inter Metro Business Journal, 2(2), 39-58.
Tsai, H.-Y. (2021). The Impact of Artificial Intelligence on Sustainable Corporate Brand: a Netnography Study of Tesla.
Unal Guner, P., & Unal, S. N. (2023). An artificial neural network based method for company valuation. Journal of Business, Economics and Finance (JBEF), 91-101.
Wilkowski, W., & Budzyński, T. (2006). Application of Artificial Neural Networks for Real Estate Valuation.
Zugravu, A., Mansour, T., da Cunha, A., & Espérandieu, A. (s.d.). Using AI in economic development: Challenges and opportunities. Tratto da McKinsey & Company: https://www.mckinsey.com/industries/public-sector/our-insights/using-ai-in-economic- development-challenges-and-opportunities#/
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