This report is devoted to the problem of choosing the structure of financial investments in a portfolio of financial assets, and shows the effect of ambiguity in decisions in the case of maximizing income or minimizing risk. The purpose of this study is to demonstrate the characteristic points at which the amount of income and risk are the same for different structures of financial resource allocation. In this case, making decisions without additional criteria becomes a major problem. Its solution is possible according to additional criteria, in particular, using artificial intelligence models when applying the results of the income maximization and risk minimization models. The research methodology consists of financial portfolio theory and optimization models, as well as artificial intelligence models. The result of this research is a breakdown of the optimization algorithm by introducing artificial intelligence models capable of analyzing a choice at specific points, when the result is not obvious and it is not possible to make an unambiguous decision. Thus, it is possible to obtain scenarios within the framework of the application of new financial technologies for decision-making in the field of financial resource allocation. Artificial intelligence has the function of weighing constraints within the framework of conditional optimization and making a fundamental choice between decision-making criteria, since the latter will depend on the criteria under consideration. Understanding how this will work is a challenge for future developers of artificial intelligence systems, but the current limitations of portfolio selection should undoubtedly be included in its research.

Introduction

AI technology is improving business finance by making work faster and smarter. Three tools are used: machine learning (ML), natural language processing (NLP), and robotic process automation (RPA). These technologies process data automatically, predict financial outcomes better, speed up repeated tasks , and help manage financial risks. However, introducing AI systems produces problems because they need good-quality data to work perfectly, and current methods still have strong support from those who do not want to change their way of doing things. This current study will address a research gap by investigating how the latest techniques in AI in finance can help companies better manage themselves, act sustainably, and make smarter decisions.

Methodology: This study will explore the use of AI in corporate finance organisations through a qualitative research approach. We reviewed the existing literature to determine the common uses of AI in decision-making in finance. To better understand this problem, we will analyse the relevant literature further. In addition, we will conduct at least 10 semi-structured interviews with AI professionals working in the financial industry. A thematic analysis will be performed to identify their in-depth knowledge about AI usage.

Potential Outcomes: The expected outcomes of advancements in AI are versatile and evolving. Organisations can enhance business and finance operations by using artificial intelligent systems, budgeting, financial analysis and prediction, natural language processing techniques to interpret textual information, and robotic process automation to restructure tedious tasks, minimise errors, and reduce time spent. Adopting cutting-edge AI technologies will automate operations and enable efficient data analysis to enhance decision-making.

Conclusion: This study will present a holistic view of how AI techniques can improve financial performance, financial organisation, and efficient decision-making in corporate finance. The effectiveness of AI technologies relies on high-quality data and efficient change management, helping business organisations boost efficiency and sustainability.

Introduction: This study adopts underexploited global data from key institutions like the United Nations and International Monetary Fund to enhance practical applications of machine learning methods for economic and financial investigations. This study applies advanced sentiment analysis technologies to novel textual documents in order to clarify their influence on asset and energy market directions. This study leads with standard prediction methods in machine learning technologies that address present research questions and develop progressive knowledge on worldwide economic and financial systems.

Methodology: First, we cleaned and pre-processed the dataset. This study used current natural language processing algorithms to conduct sentiment analysis of textual documents from which interesting features were produced. The extracted results became part of three machine learning models that combined regression analysis with decision trees and neural networks. Multiple performance indicators and logical processing mechanisms guided the study to evaluate models while safeguarding both data confidentiality and model privacy.

Findings: The findings revealed that the sentiment data from IMF and UN textual content demonstrate substantial forecasting power for energy and commodity market movements, proving that ML techniques surpass previous prediction approaches. Neural networks surpassed the evaluated methods by achieving better accuracy to show their capacity to decode and prognosticate complicated market behaviours.

Conclusion: Empirical applications that cover alternative data sources allow financial experts to achieve an improved comprehension of global market functions. The system boosts decision-making capabilities through targeted analytical instruments that display optimal performance across various economic indicator datasets. This groundbreaking study extends beyond traditional financial analysis that depends on traditional banking records through examinations of nonstandard ML methods combined with noncommercial data resources. This research finds new machine learning applications across corporate finance, governance, and behavioural finance that fill existing study voids and broaden both economic and financial knowledge.

The occurrence of consecutive devastating events has an adverse influence on investment avenues and investing behavior. As Islamic cryptocurrencies are novel digital financial assets based on Islamic law, it is crucial to explore their connectedness with green assets during periods of financial and economical struggle. This study emphasizes the safe haven attribute of both considered assets by revealing the spillover transmitter and receiver behavior. The extended version of the quantile connectedness (QVAR) technique by Ando et al. (2022) is employed for the period 28 December 2018 to 12 August 2022. The events covered are the Bitcoin Price Crash in 2018, the COVID-19 pandemic, the global plummet in oil demand in 2020, and the Russia–Ukraine War. The findings of static quantile connectedness disclose that at the median quantile (normal market condition), there is a weak connectedness between Islamic cryptocurrencies and green assets. However, inbearish and bullish market conditions, the degree of connectedness progresses. The outcomes of dynamic quantile connectedness demonstrate that total connectedness between Islamic cryptocurrencies and green assets is unstable and fluctuates with disastrous health, financial, and economic crises. These results accentuate that in both normal and extreme market conditions, investors and policymakers should continually study the market's behavior and spillover movement to alter their investment distributions.

Introduction: Employing people with psychiatric disorders poses social and financial challenges, such as high turnover rates and increased onboarding costs. This study attempts to address the gap in evidence-based policies for risk mitigation in employment through the development of Smart Job Support, an AI model aimed at optimizing career rehabilitation and human capital investment.

Objective: The main objective is to evaluate economic risk after implementing AI-assisted recruitment and monitoring in active employment for individuals with psychiatric disorders, in order to reduce financial exposure and encourage employment.

Methods: The model combines psychometric data obtained from standardized assessments, biometric data from wearable devices, and immersive job simulations to capture behavioral data. Machine learning algorithms were created to allocate individual profiles to appropriate job positions. A pilot study with 50 diagnosed participants was conducted. Employability outcomes included job retention rate, productivity measures, absenteeism, and employer-perceived ROI.

Results: The first assessment indicated a 35% improvement in job placement satisfaction, a 28% reduction in early turnover, and a 21% decrease in onboarding costs. Employers noted improvements in the quality of work and a reduction in absenteeism due to lower stress levels, which suggests that AI-powered assistance helped in both socio-economic inclusion and cost optimization.

Conclusions: The research findings demonstrate the ability of Smart Job Support to augment occupational rehabilitation through assistive technologies by aligning economically driven inclusivity with strategic planning. The results justify the expansion of the model and its implementation in business settings focused on integrating marginalized groups with controlled financial risks.

Statistical Dangerousness is a novel concept that introduces a dynamic and probabilistic approach to risk assessments in complex systems. Unlike traditional models that focus on static data or the average outcomes, Statistical Dangerousness incorporates the statistical variability in a system and the probability of exceeding a critical threshold, providing a more comprehensive understanding of potential dangers. This method is particularly applicable to fields like finance, where markets are inherently volatile, and extreme events are often difficult to predict. In finance, Statistical Dangerousness enhances risk assessments by capturing fluctuations in market conditions, asset prices, and financial indicators, allowing for the identification of periods when the likelihood of surpassing dangerous thresholds is high. By integrating both variability and probabilistic analysis, this tool enables the forecasting of potential financial crises, such as market crashes or institutional failures, which the traditional models often overlook. It allows financial institutions and investors to understand the likelihood of extreme outcomes better, improving their decision-making and the development of risk management strategies. Moreover, Statistical Dangerousness can be used to optimize the stability of financial systems by proactively detecting rising risk levels, thus preventing financial catastrophes. By focusing on the possibility of extreme deviations, it provides a forward-thinking approach to finance, enabling more accurate predictions and the timely mitigation of risks. As such, Statistical Dangerousness represents a significant advancement in financial risk management, offering valuable insights for anticipating and managing the uncertainties that shape the financial landscape.

An experiment was conducted at the University of Granada (Spain) to evaluate the effectiveness of different methods for delivering financial education for postgraduate decision-making. The participants were final-year undergraduate students at the University of Granada Business School. They were divided into three groups: one received financial education through a chatbot, another received education through traditional videos, and a control group did not receive any educational intervention.

The financial education provided in both formats, through the chatbot and video, consisted of two main modules, with a total duration of approximately 15 minutes. The first module focused on teaching the students how to calculate the economic feasibility of investing in a master's degree using the Net Present Value criterion. The second module focused on showing students how to finance their master's degree investment through a postgraduate student loan. It explained a reasonable amount based on the expected income, interest rates, and repayment terms.

The first group, which interacted with the chatbot, received information in an interactive and personalized manner, allowing them to ask questions and receive immediate responses in a conversational format. The second group, which used traditional videos, received the same information but in a more passive format, without the ability to interact directly. The control group did not receive any specific training, serving as a baseline for comparison.

After the intervention, the financial knowledge of all of the participants was assessed through an objective test. The results showed that both the chatbot and video groups scored higher on the financial knowledge test compared to the control group, indicating a positive impact from the financial education. However, the students who received education through the chatbot performed significantly better than those who used the videos. This suggests that the personalized interaction and real-time query resolution provided by the chatbot offered an additional educational advantage over the video format.

Since the implementation of registration system reform, the information disclosure mechanisms of Chinese listed companies have consistently garnered attention. The purpose of information disclosure is to effectively transfer information from within an organization to external parties, with public texts serving a crucial role in this process. However, the conciseness and accuracy of this information largely depend on the willingness and strategy of the discloser. Based on this, this paper aims to construct a systematic readability assessment system using linguistics theories, comprising 14 sub-indicators from four dimensions—Morphology, Grammar, Semantics, and Multimodal—to measure the comprehensive readability level in the annual reports of listed companies through a hierarchical dimensionality reduction method. Furthermore, this paper explores the manipulative information disclosure strategies of managers driven by self-serving incentives. This paper selected more than 4000 listed companies as the sample for its empirical analysis. It is revealed that companies with poor operating results in the current period tend to have low readability levels in their annual reports. The readability level in the current period has a positive impact on the stock market in the next one to three years, especially for companies with stock returns below the industry average. Moreover, companies with lower readability levels in their annual reports tend to demonstrate higher excess compensation of their managers. This paper provides insights into the regulatory policy and the review framework for information disclosure in the context of registration system reform.

The Government of Indonesia as the controlling shareholder sets a target for SOE risk management maturity by 2024 to reach a score of 4.2 on a scale of 1 - 5. Risk management plays a role in minimising risks in realising post-corporate action synergies, such as the establishment of the Ultra Micro SOE Holding in Indonesia. This study examines how the elements of SOE risk management, namely risk governance, risk management frameworks and processes, and internal control systems work to improve risk management maturity and provide strategic implications for allocating risk management resources effectively. The Structural Equation Modelling (SEM) approach was chosen to map the influence of complex interactions among these elements in improving risk management maturity. The results show that all risk management elements are reliable in the proposed risk management maturity improvement model. The majority of hypothesis testing results of the influence relationship between elements in the model are significant. Allegedly, the influence relationship between framework elements with risk management processes and processes with risk management maturity was not significant. It can be concluded that the proposed risk management maturity improvement model can work effectively, so the implementation strategy can be developed by taking into account these findings. Further research is needed to better understand the complex dynamics involving risk management process elements in the construct, identify other variables that mediate the relationship between the framework and risk management processes, or other contextual factors that influence this relationship as well as identify other factors that may have a more significant influence on the effectiveness of risk management processes.

This study explores the integration of machine learning (ML) techniques into technical trading strategies, evaluating their performance against traditional methods across diverse financial markets. It employs key technical indicators like Moving Averages, the Relative Strength Index (RSI), and other analytical tools to boost prediction accuracy. Historical market data, sourced from the yfinance library, forms the basis for designing and testing these strategies, enabling a detailed assessment of the profitability and effectiveness of ML-enhanced approaches. The research aims to showcase machine learning’s ability to uncover intricate patterns and relationships in financial data—insights often missed by simpler, conventional systems—thereby improving forecasting precision.

By merging ML models, such as neural networks or decision trees, with established trading indicators, this work seeks to transform trading from a field reliant on specialists’ intuition into a more data-driven discipline. This hybrid approach combines human expertise with algorithmic power, aiming to maximize profits and efficiency. The study uses yfinance-extracted data to simulate and validate strategies, demonstrating how ML can detect subtle market trends that traditional methods overlook. This shift promises to enhance decision-making by grounding it in empirical analysis rather than subjective judgment.

The implications of this research are significant. It bridges the gap between human instincts and advanced computational techniques, introducing innovative strategies that could reshape financial trading. By improving prediction accuracy and optimizing outcomes, ML-integrated systems offer a competitive edge, potentially revolutionizing how markets operate. This study not only highlights the practical benefits of combining machine learning with technical analysis but also sets the stage for broader adoption of such technologies in mainstream finance, paving the way for smarter, more adaptive trading practices.