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In the contemporary global economic landscape, the imperative for enterprise financial informatization as a catalyst for efficiency, cost reduction, and innovation is evident. However, the deepening integration of informatization introduces corresponding risks, necessitating the establishment of effective risk management systems. This paper proposes a genetic algorithm-based model for enterprise financial informatization risk management, aiming to offer precise and actionable solutions. The study integrates genetic algorithms to enhance the adaptability and flexibility of the risk management model in the dynamic information environment. Through in-depth research on corporate financial informatization risks, a multi-dimensional risk management control model is constructed, considering technical, organizational, and environmental factors. The genetic algorithm introduces a new perspective for model optimization, enabling efficient search and optimization capabilities. The model not only identifies but also controls and governs risks, providing timely intervention and effective risk management. Moreover, the genetic algorithm facilitates intelligent decision support for risk management by adapting to changing environments. Empirical analysis validates the model’s feasibility and effectiveness in real enterprise cases, emphasizing its practicality.

The application of big data analytics in financial decision-making has become pivotal in addressing the complexities of modern financial markets. With the growing availability of high-dimensional and high-frequency data, traditional investment strategies often fail to capture dynamic market behaviors and multi-scale dependencies. Conventional methods, grounded in static models and linear assumptions, lack the flexibility and robustness required for optimizing financial decision-making in volatile and interconnected markets. This paper aligns with the scope of computational advancements in financial systems, introducing an Adaptive Investment Optimization Model (AIOM) that integrates deep learning, stochastic modeling, and reinforcement learning to enhance investment strategies. By leveraging multi-scale feature extraction, dynamic market state estimation, and a reinforcement learning-based optimization engine, the model achieves superior adaptability and precision. Our novel Market-Aware Optimization Framework (MOF) further refines portfolio management by dynamically adjusting allocations based on predictive market signals and advanced risk measures, such as Conditional Value at Risk (CVaR) and drawdown control. Experimental results demonstrate significant improvements in portfolio returns and risk management compared to traditional methods. This work exemplifies the potential of computational innovations in transforming financial decision-making, offering robust solutions for real-time, adaptive investment optimization.

With the wide application of the intelligent news algorithm in the news industry, its recommendation mechanism has become the primary way for news consumers to obtain information. This study explores the intelligent news algorithm recommendation mechanism’s risk management measures and optimization schemes. Thus, people can get transparent news information more efficiently. Firstly, the study classifies and analyzes the risks of information filtering bias, information cocoon effect, and information bubble in intelligent news algorithm recommendation mechanism and collects and introduces large-scale news data as a data source. Secondly, the intelligent news algorithm recommendation model based on the convolutional neural network is constructed. The model uses word embedding technology to transform news articles into vector representations and trains the model to learn the feature representations of news articles and the correlation between them. Moreover, the loss function and weight of the model are adjusted to improve the diversity and balance of the recommendation results. Finally, simulation experiments are carried out to evaluate the model’s performance. The results reveal that the information diversity of the system model in this study is increased by 15%, and user satisfaction and the information quality index are increased by 10% and 7%. It proves the importance of diversified data sources, algorithm transparency and explainability, user feedback and participation, and balanced recommendation strategies to reduce risk and improve the performance of recommendation mechanisms. Therefore, the research results guide the practical application of the intelligent news algorithm recommendation mechanism and provide a reference for further improvement and optimization of the recommendation algorithm.

The use of project management (PM) is of immense value in achieving project success that ultimately assists businesses to thrive and contribute to the growth of an economy. This research has one main research question “How does the use of PM techniques have positive impact on project outcomes in New Zealand (NZ) businesses?”. The research adopted inductive approach to build theory and qualitative research methodology to address the main research question using four research objectives. This is an exploratory research using a case study research strategy that is coherent with the inductive qualitative research design. Two organisations representing the construction services and the Information and Communication Technology (ICT) services business in NZ were chosen. In-depth interviews were used to collect data, data analysis was completed through thematic analysis techniques. Findings were assessed through a theoretical construct mainly based on the Project Management Institute’s (PMI) PM framework. Findings indicated that the two organisations had strong focus on PM function supported by their organisational structures. Organisation A relied more on Work Breakdown Structure (WBS) and Gantt chart technics while organisation B adopted an agile approach. Both considered designating project manager when deploying a formal PM function. While Company A has not implemented dedicated technology to support PM function, Company B has though not fully utilised. Hopefully, findings from this study will assist organisations in the construction and ICT services business sectors being significantly reliant on PM function to deliver project outcomes and to be successful in their businesses.

Hedge funds are collective investment vehicles fast becoming popular with high net worth individuals as well as institutional investors. These are funds that are often established with a special legal status that allows their investment managers a free hand to use derivatives, short sell and exploit leverage to raise returns and cushion risk. Given that they have substantial latitude to invest, it is instructive to examine the performance of hedge funds as compared to other forms of managed funds. This paper provides an overview of hedge funds and discusses their empirical risk and return profiles. It also poses some concerns regarding the empirical measurements. Given the complexity of hedge fund investments, meaningful analytical methods are required to provide greater risk transparency and performance reporting. Hedge fund performance is also beset by a number of practical issues generating "practical risks". These risks are not fully addressed by the usual risk-adjusted performance measures in the literature. A penalty function to discount these extraneous risk dimensions is proposed. The paper concludes that further empirical work is required to provide informative statistics about the risk and return of hedge funds.

Malaysian employees' public pension plan was studied to analyze pension expenditure due to salary risk and demographic risk. By integrating risk management and System Dynamics (SD) approach, the risk factors involved were identified, a causal loop diagram was constructed, and the SD model was developed. By using a sample of actual data, the proposed model was then validated through behavior validity test and a risk analysis was conducted. Then, risk monitoring was performed through policy evaluation in which the impact of different policy scenarios on pension expenditure was analyzed. Risk management and dynamics simulation approach in analyzing pension expenditure were shown to be useful in evaluating the impact of changes and policy decisions on risk.

Managers are faced with increased complexity and unexpected risks. This article raises some reasons for the increase in complexity and risks. It also describes the tools and approaches used to anticipate some of these risks and how to mitigate against them. The usefulness of the scenario planning process is also indicated. The type of behavioral biases that makes risk identification difficult is also explained.

Risks are traditionally defined as the combination of probability and severity, but are actually characterized by additional factors. We believe the characteristics of risks include uncertainties, dynamics, dependence, clusterings and complexities, which motivate the utilization of various operational research tools. The objective of this issue is to survey the practice of using operational research tools in risk management, especially Asian risk management.

A modeling methodology for blog recommendation and forecasting based on information entropy is presented. With the increasing popularity of smartphones and the rapid development of the mobile Internet, the amount of user-generated content such as blogs is increasing daily. Valuable information, such as bloggers’ opinions, feelings, and attitudes, is often part of this content. Particularly in the context of an emergency, this information should also be used to facilitate decision making. The current blog recommendation model examines primarily users’ interests or content similarity, whereas in this paper, the value of the blog is considered. The primary contribution of this paper is the proposal of an information-entropy-based blog recommendation model for finding valuable blogs to facilitate decision-making in an emergency context. A series of indicators for evaluating a blog in an emergency context are proposed. Using the method of information entropy, a blog recommendation model is developed. The model can also be used to forecast the value of emergency blogs in the future. The model has been tested and validated using crawled data from the Sina Blog, and the results have demonstrated that the proposed model can effectively determine the value of emergency-related blogs.

E-finance industry is rapidly transforming and evolving toward more dynamic, flexible and intelligent solutions. This paper describes a model with dynamic multilevel workflows corresponding to a multilayer Grid architecture. The mining-grid is used for multiaspect analysis in building e-finance portals on the Wisdom Web. The application and research demonstrate that mining-grid centric design is effective for developing intelligent risk management and decision making financial systems.

This paper concentrates on how to develop a mining-grid centric e-finance portal (MGCFP), not only for supplying effective online financial services for both retail and corporate customers, but also for intelligent credit risk management and decision making for financial enterprises and partners.

Data-driven smart investment decisions are important for financial development, which has not received much attention from academia. As a result, this paper resorts to the evolutionary game theory, and proposes a novel multi-agent financial investment decision method. Specifically, an evolutionary game theory-based decision-making approach is formulated as the main model for the research purpose. By considering the strategic choices and adaptability among various entities, a comprehensive analysis of the behavior and decision-making process of entities in the financial market is achieved. This paper combines stock exchanges and financial data providers (Bloomberg and Thomson Reuters) to conduct case studies on this method, verifying its effectiveness and feasibility in practical applications. By comparing traditional financial investment decision-making methods, it can be seen that the proposal has significant advantages in improving investment efficiency, reducing risks, and responding to market volatility. This paper delves into the multi-agent financial investment decision-making method based on the evolutionary game, providing new ideas and methods for academic research and practical applications in the financial field.

Software systems in safety-critical industrial automation systems, such as power plants and steel mills, become increasingly large, complex, and distributed. For assessing risks, like low product quality and project cost and duration overruns, to trustworthy services provided by software as part of automation systems there are established risk analysis approaches based on data collection from project participants and data models. However, in multi-disciplinary engineering projects there are often semantic gaps between the software tools and data models of the participating engineering disciplines, e.g., mechanic, electrical, and software engineering.

In this paper we discuss current limitations to risk assessment in (software+) engineering projects and introduce the SEMRISK approach for risk assessment in projects with semantically heterogeneous software tools and data models. The SEMRISK approach provides the knowledge engineering foundation to allow an end-to-end view for service-relevant data elements such as signals, by providing a project domain ontology and mappings to the tool data models of the involved engineering disciplines.

We empirically evaluate the effectiveness and efficiency of the approach based on a real-world industrial use case from the safety-critical power plant domain. Major results are that the approach was effective and considerably more efficient than the current approach at the industry partner.

Software Product Line (SPL) Engineering focuses on systematic software reuse, which has benefits such as reductions in time-to-market and effort, and improvements in the quality of products. However, establishing a SPL is not a simple matter, and can affect all aspects of the organization, since the approach is complex and involves major investment and considerable risk. These risks can have a negative impact on the expected ROI for an organization, if SPL is not sufficiently managed. This paper presents a mapping study of Risk Management (RM) in SPL Engineering. We analyzed a set of thirty studies in the field. The results points out the need for risk management practices in SPL, due to the little research on RM practices in SPL and the importance of identifying insight on RM in SPL. Most studies simply mention the importance of RM, however the steps for managing risk are not clearly specified. Our findings suggest that greater attention should be given, through the use of industrial case studies and experiments, to improve SPL productivity and ensure its success. This research is a first attempt within the SPL community to identify, classify, and manage risks, and establish mitigation strategies.

Currently, software acquisition is strategic for organizations. Companies need support to succeed in software acquisition projects because such projects commonly present high failure rates. To solve this problem, it is necessary to design mechanisms for software acquisition, such as risk classification to identify the typical risks that may affect the success of a software acquisition project. Thus, herein, we present a case study, showing the implementation of a risk taxonomy whose structure is designed using a method for its construction, with a mechanism to reduce the risk of acquisition projects that adversely affect their success.

Managing risks in real-world software projects is of paramount importance. A significant class of such risks is related to the engineering of requirements, commonly involving the presentation and analysis of risk management arguments from both software engineers and clients involved in collaborative debates. In this work, drawing inspiration from argumentation theory in Artificial Intelligence, we introduce a number of “argumentation schemes” and associated “critical questions” to support such discussions. In doing so, we propose schemes related to risks due to excessive numbers of requirements; inadequate client representatives and poor understanding of client needs; incorrect, incomplete and conflicting requirements; complex and non-traceable requirements; non-stable requirements; and low quality requirements. We also discuss a case study and two experiments where the developed schemes supported the discussion of requirement risks in software projects. The overall results of these experiments indicate that our schemes are useful in the identification, proposition and analysis of requirement risks, adequately supporting debates on requirement risks.

Risk management contributes to software projects success, but agile software development methods do not offer specific activities to manage risks. Therefore, this study aims to propose a list of risk management practices for agile projects, aiming to increase their chances of success. We analyzed 129 works on agile methods that afforded 127 risk management practices. We categorized and ranked practices using the AHP multi-criteria method with the participation of experts in the subject. The study presents risk management practices for daily meetings, increment, prototype, product backlog and Sprint planning as the most important for the risk management effectiveness. This study identified specific risk management practices for agile methods, not converging with other studies. Results contribute to the risk management improvement in agile projects and, consequently, increase their chances of success.

In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic information. This paper provides a comprehensive overview of the research about knowledge graph in social crisis management, focusing on three key areas: knowledge graph construction and inference, knowledge graph-based interpretable crisis attribution, and risk management. Specifically, the interpretable semantics in crisis knowledge graphs enables attribution of intention. To illustrate the significance of knowledge graphs in group intention mining, the COVID-19 and China–US game events are selected as two case studies. Finally, the paper proposes future research directions to solve the limitations of existing knowledge graph-related methods in social crises.

This paper examines the problem of selecting an alternative in situations in which there exists uncertainty in our knowledge of the state of the world. We show how the ordered weighted averaging aggregation operators provide a unifying approach to selecting alternatives under uncertainty. In particular, we see how these operators provide a type of probability associated with our degree of optimism. We also show how the Dempster-Shafer belief structure provides a general framework for representing the information a decision maker has regarding relevant events. We then propose a methodology for decision making under uncertainty, integrating the ordered weighted averaging aggregation operators and the Dempster-Shafer belief structure. The proposed methodology is applied to a real world case involving risk management at one of the nation’s largest banks.

In this paper, we present a fuzzy dynamic programming procedure for long term risk management. This approach is designed to provide insights on trade-offs between potential risks and rewards, the dynamics of interacting economic factors, and the feasibility of corporate goals over a long term planning horizon. This approach is applicable to many long term planning problems involving selection from a number of alternatives, when the decision parameters are imprecise and absolute requirements and decision thresholds can not be specified. A few examples of problems of this type include: portfolio optimization, risk management, evaluation of securities, liquidity management, and asset/liability management (which is given particular emphasis in this paper). Our formulation provides a computationally efficient and natural representation of the decision trade-offs inherent in many long term money management problems. This, in turn, facilitates the exploration and analysis of many alternative investment strategies under many possible future scenarios.

Today’s commonly used risk management procedures allow the planning team to focus on sensitive areas. It focuses the project team’s attention on activities and resources when a great risk threat exists or when the most time- and effective-engineering solutions can achieve expense reductions. Industrial security issues have worsened dramatically over the last decade. Over time, the risks in the sector rose as limited, single-train or batch operations shifted to massive multi-train operations. Often an accident leads to detrimental effects: shutting down the operation, loss of life, environmental disruption, and loss of business. The reparation of losses from the budget is unwelcome since the government’s reserve will not cope with these needs. This paper has a definitive comparative edge for company sponsors for project risk management (PRM) strategy. The sponsors who knowingly take chances, forecast unfavorable developments, defend themselves against unforeseen incidents and gain experience in price danger take the lead. However, this market benefit is largely dependent on the method to initially identify risks in the architecture of extensive multidisciplinary capital ventures. This paper explores how a sustainable market strategy works in reality. Using the market model, the business issues that need to be considered during the implementation or expansion of urban strategies are discussed. The PRM strategy shows a better-quality assurance ratio of 97.2%, probability ratio of 95.3%, safety rate of 97.5%, reduced risk management rate of 22.6%, an accident rate of 17.4%, cost management of 25.1% and fuel consumption ratio of 23.7% when compared to existing strategies.