Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

News-based soft information as a corporate competitive advantage

This study establishes a decision-making conceptual architecture that evaluates decision making units (DMUs) from numerous aspects. The architecture combines financial indicators together with a variety of data envelopment analysis (DEA) specifications to encapsulate more information to give a complete picture of a corporate’s operation. To make outcomes more accessible to non-specialists, multidimensional scaling (MDS) was performed to visualize the data. Most previous studies on forecasting model construction have relied heavily on hard information, with quite a few works taking into consideration soft information, which contains much denser and more diverse messages than hard information. To overcome this challenge, we consider two different types of soft information: supply chain influential indicator (SCI) and sentimental indicator (STI). SCI is computed by joint utilization of text mining (TM) and social network analysis (SNA), with TM identifying the corporate’s SC relationships from news articles and SNA to determining their impact on the network. STI is extracted from an accounting narrative so as to comprehensively illustrate the relationships between pervious and future performances. The analyzed outcomes are then fed into an artificial intelligence (AI)-based technique to construct the forecasting model. The introduced model, examined by real cases, is a promising alternative for performance forecasting. First published online 21 November 201

Occupational Health and Safety (OHS) Issues in Social Marketing

Social marketing has been contributing historically for a better application of public policy, health and safety, environment, education and human rights. Specifically, four major areas that social marketing efforts have focused over the years are health promotion,injurprevention,environmental protection, and community mobilization. Social marketing, at an industrial organization, emphasizes exchange of ideas between the target audience (i.e. the employees) and the marketer (i.e. the employer). This exchange requires that the employees be persuaded to give up the unsafe behaviors that they are accustomed to, to gain an enhanced level of safety with a greater likelihood of preventing injuries in the workplace. In an organizational context, the internal users are treated as customers and marketing inside the organization is an essential part of delivering value to the organization, and ultimately to the end customer. Therefore, effective management strategies are sought to develop the concept of internal marketing with a view to satisfy the employees and in turn, motivate them to do good work and produce a better product or service. The success of any business enterprise largely depends on its manpower with regard to their professional skill level, positive attitude, job satisfaction, and involvement in quality improvement activities. The important aspect of corporate social responsibility (CSR) is the concern for safety and sound health of the workforce, so that employees feel secured and motivated. The concern becomes manifold when the workforce is exposed to menial tasks and occupational risk situations. To make a safe and conducive environment, an organization must build a solid foundation with a clear vision of the future and specific means by which it will achieve the safety mission of the organization. Safety, health and environment systems needs a continual and systematically managed efforts in order to achieve sustainable growth. Presently, many industries are focusing attention on occupational health and safety (OHS) that may help to achieve competitive advantage. This research is concerned with the study of OHS issues in the context of injury prevention social marketing. A detailed study on workplace environment and safety climate makes the implementation of various social marketing principles easier. This may also be useful for the purpose of policy formulation on improving OHS in Indian industries. Three industrial sectors such as construction (Type 1), refractory (Type 2) and steel (Type 3) are considered in this study. These industries are generally viewed as hazardous due to usage of heavy equipment, unsafe and primitive tools, injurious materials and dust produced during operation. The study covers such organizations where size in manpower and investment varies, both organized and unorganized workforce exists, both public and private enterprises exist, and the level of sophistication of tools, methods, and work environment in terms of safety is poor. A study on risk perceptions and understanding of OHS has been conducted in three industrial sectors. Thirty four items are included in the questionnaire through review of related literature and discussion with a focus group. The items are framed to suit the local work practices and culture covering various aspects of OHS. Two hundred eighty eight (or 288) useful responses were tested to examine the validity and reliability of the scale to ensure a quantitative and statistically provenidentification of the responses. The test for quantitative variables was conducted by factor analysis on responses using the principal component method followed by varimax rotation to ensure that the variables are important and suitable for the model using SPSS 16.0. Finally, identified factors were again analyzed using discriminant analysis to highlight statistical difference among practices existing in three sectors. The pattern of influence of input parameters on outputs such as injury level and material damage is difficult to establish, possibly due to existence of some nonlinear relationship among them. Therefore, an artificial neural network (ANN) is adopted to carry out sensitivity analysis and important deficient items have been identified. A comparative evaluation on deficient items among three major types of Indian industries has been made. Quality function deployment (QFD) has been used to develop the system design requirements considering the deficient safety items as voice of customers. The interrelation among the system design requirements is represented in a digraph using Interpretive Structural Modelling (ISM) approach. A predictive methodology for forecasting various types of injuries has been proposed using fuzzy inference system. As fuzzy inference system can be used with little mathematical knowledge and needs only expert knowledge, it can be easily implemented in the field to predict injury types. Further, fuzzy inference system can deal effectively in imprecise and uncertain situations. In order to transfer best practices among various organizations, a benchmarking study has been carried out using data envelopment analysis (DEA). The study finally provides some useful guidelines for the managers for improving safety performance in selected Indian industrial settings

Production system efficiency optimization through application of a hybrid artificial intelligence solution

Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system’s efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML- GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real- world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution

Multidimensional approaches to performance evaluation of competing forecasting models

The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions and assess their merits. In sum, two main methodological contributions are presented. The first contribution consists of proposing a mathematical programming based approach, commonly referred to as Data Envelopment Analysis (DEA), as a multidimensional framework for relative performance evaluation of competing forecasting models or methods. As opposed to other performance measurement and evaluation frameworks, DEA allows one to identify the weaknesses of each model, as compared to the best one(s), and suggests ways to improve their overall performance. DEA is a generic framework and as such its implementation for a specific relative performance evaluation exercise requires a number of decisions to be made such as the choice of the units to be assessed, the choice of the relevant inputs and outputs to be used, and the choice of the appropriate models. In order to present and discuss how one might adapt this framework to measure and evaluate the relative performance of competing forecasting models, we first survey and classify the literature on performance criteria and their measures – including statistical tests – commonly used in evaluating and selecting forecasting models or methods. In sum, our classification will serve as a basis for the operationalisation of DEA. Finally, we test DEA performance in evaluating and selecting models to forecast crude oil prices. The second contribution consists of proposing a Multi-Criteria Decision Analysis (MCDA) based approach as a multidimensional framework for relative performance evaluation of the competing forecasting models or methods. In order to present and discuss how one might adapt such framework, we first revisit MCDA methodology, propose a revised methodological framework that consists of a sequential decision making process with feedback adjustment mechanisms, and provide guidelines as to how to operationalise it. Finally, we adapt such a methodological framework to address the problem of performance evaluation of competing forecasting models. For illustration purposes, we have chosen the forecasting of crude oil prices as an application area

Developing a neural network model to monitor and predict waiting times in the emergency department

In parallel with manufacturing context, quality control toward provided services in service organisations have been growing as well including healthcare industry, but often models of healthcare service quality face challenges in measuring quality. The developed meta-algorithm and ANN models in this thesis can facilitate measuring service quality in Healthcare industry

Climate Change, Carbon Capture, Storage and CO2 Mineralisation Technologies

This Special Issue delivered 16 scientific papers, with the aim of exploring the application of carbon capture and storage technologies for mitigating the effects of climate change. Special emphasis has been placed on mineral carbonation techniques that combine innovative applications to emerging problems and needs. The aim of this Special Issue is to contribute to improved knowledge of the ongoing research regarding climate change and CCS technological applications, focusing on carbon capture and storage practices. Climate change is a global issue that is interrelated with the energy and petroleum industry