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Abstract: Operating in today's turbulent environment is characterized by, among other things, significant risks. The current trends of deregulation and globalization of markets increase the impact of risks on the company's operations and, consequently, on its value, being a source of potential unforeseen losses due to changes in market prices, financial difficulties or other difficulties of the company. As business practices, environments, and trends change, some areas of risk management become increasingly important. When distinguishing between the terms risk and uncertainty, it can be said that they are related but different. While uncertainty is unknown and changing, risk is the result of uncertainty. All of these factors can have a significant impact on a company's cash flow, earnings, and enterprise value. The nature of risk has directed risk management to protect companies from financial difficulties, and recognizing different categories of risk such as credit, market, or ESG risks can become an important step to ensure uninterrupted business operations and protection of corporate assets. By achieving the fundamental goal of identifying and managing various forms of risk, the company will become increasingly better able to survive and operate successfully in the modern economic environment, ultimately contributing to increased shareholder value. A further step in understanding and evaluating the concept of integrated risk management in organizations is the composite risk management index, which according to numerous authors represents a significant contribution and advance in the quantitative approach to measuring and evaluating the risk management process. The CRM index represents a comprehensive dimension of CRM that effectively measures the construct. The CRM Index is proposed as an effective measure of CRM implementation and was constructed based on data derived from a thorough review of the literature. The said index uniformly assesses the data on the frequency, depth and completeness of risk management practices in organizations. The purpose of the doctoral dissertation implies and includes a theoretical and practical contribution in the field of corporate risk management and the formation of a composite index as a measurement tool for the implementation of the above process in medium-sized and large companies, as well as ways of creating, generating and measuring newly created value as a product of their interaction, with the expectation of understanding and strengthening interest in this research subject as a broad topic. A unique scientific contribution from both a theoretical and practical perspective is given. From a theoretical perspective, the research conducted makes a significant contribution to the scientific knowledge on the effects of corporate risk management as a function of the creation of medium and large firms in multiple and multiplicative ways. The academic literature is improved by conceptually upgrading various research niches, concepts and phenomena of this field of knowledge, and the interrelationships and interdependencies of corporate risk management are recognized and identified, and their effects on the creation and growth of value of non-financial firms are observed. In addition, it consolidates the different academic views on the process of enterprise risk management and further clarifies its concept, defines its scope and determines the level of action.
Keywords: corporate risk management, firm performance, corporate risk management index, value growth
Published in DKUM: 25.11.2025; Views: 0; Downloads: 5
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Abstract: This study contributes towards understanding tax compliance by identifying taxpayers’ characteristics affecting it. The purpose of this study was to identify significant characteristics of Slovenian taxpayers that affect corporate tax compliance. Data were acquired through the desk audit process conducted by the Financial Administration of the Republic of Slovenia (FURS), focusing on the investment allowance of Slovenian corporate taxpayers. Econometric testing determined that a binary logit model was the most appropriate fit. Consequently, logistic regression analysis was performed on a large sample of Slovenian corporate taxpayers for the period between 2018 and 2021. A wide range of characteristics were considered, including the type of enterprise, age, size, industry, location and financial indicators. Eight characteristics were found to significantly impact tax compliance in Slovenia, including the size of the enterprise, organizational type, residing tax office branch, business sector, and the year the observed audit was conducted. Besides finding eight significant characteristics of taxpayers determining tax compliance, results also indicate the presence of local bias, with one of the tax office branches significantly impacting the number of irregularities found during audits.
Keywords: tax compliance, taxpayers, characteristics, local bias
Published in DKUM: 05.11.2025; Views: 0; Downloads: 2
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Abstract: Magistrska naloga obravnava uporabo umetne inteligence (UI) v bančništvu, kjer postaja ključni dejavnik za izboljšanje učinkovitosti poslovanja, personalizacije storitev in upravljanja tveganj. Sodobne metode UI, kot so strojno in globoko učenje ter obdelava naravnega jezika, že omogočajo avtomatizacijo procesov, prepoznavanje tveganj ter preprečevanje goljufij. V teoretičnem delu naloge so predstavljeni različne tehnike umetne inteligence, zgodovinski razvoj tehnologije ter regulativni in etični izzivi, ki spremljajo njeno uporabo v finančnem sektorju. Poseben poudarek je na prednostih, ki jih uporaba umetne inteligence prinaša v bančništvo, kot so nižji stroški, hitrejše storitve in večja prilagodljivost potrebam strank, vendar je hkrati opozorjeno na regulativne izzive in potrebo po zaščiti osebnih podatkov. V empiričnem delu naloga analizira raziskavo, ki vključuje anketni vprašalnik o uporabi in zavedanju strank glede umetne inteligence v bančnih storitvah. Rezultati kažejo, da je uporaba UI še vedno v zgodnji fazi in da je med strankami zaznana zadržanost glede uporabe novih tehnologij, zlasti zaradi skrbi za varnost podatkov. Zaključki naloge potrjujejo, da UI predstavlja ključni dejavnik prihodnje transformacije bančništva, vendar zahteva previden pristop pri svoji implementaciji in regulaciji.
Keywords: umetna inteligenca, bančništvo, tehnologija
Published in DKUM: 04.09.2025; Views: 0; Downloads: 47
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Abstract: This study explores the characteristics of individual investors in crypto asset markets using machine learning and explainable artificial intelligence (XAI) methods. The primary objective was to identify the most effective model for predicting the likelihood of an individual investing in crypto assets in the future based on demographic, behavioral, and financial factors. Data were collected through an online questionnaire distributed via social media and personal networks, yielding a limited but informative sample. Among the tested models, Efficient Linear SVM and Kernel Naïve Bayes emerged as the most optimal, balancing accuracy and interpretability. XAI techniques, including SHAP and Partial Dependence Plots, revealed that crypto understanding, perceived crypto risks, and perceived crypto benefits were the most influential factors. For individuals with a high likelihood of investing, these factors had a strong positive impact, while they negatively influenced those with a low likelihood. However, for those with a moderate investment likelihood, the effects were mixed, highlighting the transitional nature of this group. The study’s findings provide actionable insights for financial institutions to refine their strategies and improve investor engagement. Furthermore, it underscores the importance of interpretable machine learning in financial behavior analysis and highlights key factors shaping engagement in the evolving crypto market.
Keywords: crypto investors, identification, characteristics, machine learning, coarse tree model, artificial intelligence
Published in DKUM: 09.07.2025; Views: 0; Downloads: 10
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Abstract: The banking sector is experiencing significant disruption due to technological advancements and evolving customer demand. This study analysed over 2000 banking and/or neobanking users across 28 countries. A multinomial logit model was applied to examine three user characteristics groups: demographics, banking habits, and neobanking habits. Several interesting effects were found. Higher-educated and single users are more likely to use neobanks, while self-employed and lower-income users are less likely. Neobank users prioritize affordability, availability, and speed, while traditional bank users prioritize stability and personal interaction. We have developed a tool to identify clients likely to leave traditional banks, fully or partially, with high reliability. Even partial outflows mean banks lose important services generating significant revenue to competitors. A crucial factor here is the single banking market, which eases switching between banks. Neobanks further reduce barriers, enhancing customer mobility. Moreover, opening an account with a neobank takes only minutes. The findings of this study provide valuable insights for banks and neobanks, allowing for a more comprehensive understanding of users’ characteristics that reflects current customer demand and enables new strategies to better address them.
Keywords: traditional banks, neobanks, users, characteristics, demand
Published in DKUM: 04.07.2025; Views: 0; Downloads: 11
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