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The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

The study, within the Increasing Resilience of Cultural Heritage (ResCult) project, aims to support civil protection to prevent, lessen and mitigate disasters impacts on cultural heritage using a unique standardised-3D geographical information system (GIS), including both heritage and risk and hazard information.

A top-down approach, starting from existing standards (an INSPIRE extension integrated with other parts from the standardised and shared structure), was completed with a bottom-up integration according to current requirements for disaster prevention procedures and risk analyses. The results were validated and tested in case studies (differentiated concerning the hazard and type of protected heritage) and refined during user forums.

Besides the ensuing reusable database structure, the filling with case studies data underlined the tough challenges and allowed proposing a sample of workflows and possible guidelines. The interfaces are provided to use the obtained knowledge base.

The increasing number of natural disasters could severely damage the cultural heritage, causing permanent damage to movable and immovable assets and tangible and intangible heritage. The study provides an original tool properly relating the (spatial) information regarding cultural heritage and the risk factors in a unique archive as a standard-based European tool to cope with these frequent losses, preventing risk.

In 2018, the International Financial Reporting Standard 9-Financial Instruments became mandatory, effectively changing the underlying accounting principles of financial instruments. This paper systematically reviews the academic literature on the implementation effects of IFRS 9, providing a coherent picture of the state of the empirical literature on IFRS 9.

The study thrives on a systematic review approach by analyzing existing academic studies along the following three broad categories: adoption and implementation, impact on financial reporting, and risk management and provisioning. The study concludes by providing research prospects to fill the identified gaps.

We document data-related issues, forecasting uncertainties and the interaction of IFRS 9 with other regulatory standards as implementation challenges encountered. Also, we observe cross-country heterogeneity in reporting quality. Furthermore, contrary to pre-implementation expectations, we find improvement in risk management. This suggests that despite the complexities of the new regulatory standard on financial instruments, it appears to be more successful in achieving the intended objective of enhancing better market discipline and transparency rather than being a regulatory overreach.

As the literature on IFRS 9 is burgeoning, we provide state-of-the-art guidance and direction for researchers with a keen interest in the economic significance and implications of IFRS 9 adoption. The study identifies gaps in the literature that require further research, specifically, IFRS 9 adoption and firm’s hedging activities, IFRS 9 implications on non-financial firms. Lastly, existing studies are mostly focused on Europe and underscore the need for more research in under-researched jurisdictions, particularly in Asia and Africa. Also, to standard setters, policymakers and practitioners, we provide some insight to aid the formulation and application of standards.

This study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.

Based on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.

The results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.

This study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.

This paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

This study aims to empirically examine environment, social, and governance (ESG) as an effective strategy to reduce major impediments for a corporation in the form of costs of capital (COC) and systematic risk, especially for emerging markets such as India.

A sample of 114 Indian firms from eight prominent industries based on Thomson Reuters classification (TRBC) are used in the study. A panel regression with industry-fixed effects is carried out to account for industry heterogeneity. For robustness, the authors also carry out a matched sample analysis.

The authors observe a negative and significant relationship between ESG performance with COC and systematic risk, respectively. For the pillar-wise analysis, the authors observe that only governance performance is negatively and significantly related to COC whereas the environmental and social performances are negative and insignificant. For ESG pillar level analysis for beta, the authors observe that all pillars are negative and significant, thus making a case for how firms can fine-tune their ESG strategies according to each pillar.

As the ESG concept is still in a very nascent stage, data availability is a definite challenge in India.

As ESG is increasingly becoming relevant for multiple stakeholders, this study aims to provide evidence that can potentially guide the regulators, practitioners, and academicians to address the contemporary needs of these stakeholders, while also doing good for the firm in the traditional sense.

The transition to a sustainable economy is a challenge for emerging economies, especially for a country like India where stakeholders are not only varied but also huge in number. With this study's contribution towards an incremental understanding of ESG, Indian regulators and policymakers can bring forward mandates as to ESG compliances that are rewarding for the firms and give them enough impetus towards complying with ESG norms.

The extant literature on ESG majorly discusses the relationship between ESG performance and financial performance. This study addresses the lacuna of the relationship of ESG with COC and beta in the Indian context.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study aims to examine the impact of national culture on classification shifting in Eastern European Member States of EU Eastern European countries (EEU) vis-à-vis the Western Member States of EU (WEU). The EEU provides a unique sample to study the quality of financial reporting that the authors measure with classification shifting given that for more than five decades they were following the model of a centrally planned economy, where market-based financial reporting was absent. Yet, the EEU transitioned to a market-based economy and completed its accession to the EU.

This study uses a panel data set of firm year observations from 1996 and 2020 that covers the full transition of EEU. This empirical analysis is based on fixed effects panel regression analysis where the authors report a plethora of identifications.

This study finds classification shifting in the EEU countries since their transition to the market-based economy, though they have no long record of market-based financial reporting. This study also notices that cultural factors are associated with classification shifting across all Member States of the EU. This study further examines the impact of interactions between cultural characteristics and special items and reveal variability between WEU and EEU. As part of the robustness analysis, this study also tests the impact of culture on real earnings management measures for both WEU vs EEU, confirming the variability of the impact of culture on earnings management.

Future research could explore the role of religion differences in WEU vis-à-vis EEU states, as they are also subject to cultural differences.

The findings are important for regulators, external monitors and investors, as they show that cultural factors affect earnings management with some variability across countries in the EU, and they should be acknowledged in policymaking.

The findings show that cultural differences between EEU and the “old” Member States of the EU could explain classification shifting.

To the best of the authors’ knowledge, this is the first study that sheds light on the impact of national culture on classification shifting in EEU of EU vis-à-vis the “old” WEU of EU.