Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses

Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain

Explainable credit scoring through generative adversarial networks

Credit scoring has been playing a vital role in mitigating financial risk that could affect the sustainability of financial institutions. An accurate and automated credit scoring allows to control the financial risk by using the state-of-the-art and data-driven analytics. The primary rationale of this thesis is to understand and improve financial credit scoring models. The key issues that occur in the process of developing credit scoring model using the state-of-the-art machine learning(ML) techniques, are identified and investigated. Through the proposed models using ML approaches in this thesis, the challenges in credit scoring can be resolved. Therefore, the existing credit scoring models can be improved by novel computer science techniques in realistic problem of the areas as follows. First, an interpretability aspect of credit scoring as eXplainable Artificial Intelligence (XAI) is examined by non-parametric tree-based ML models combining with SHapley Additive exPlanations (SHAP). In this experiment, the suitability of tree-based ensemble models is also assessed in imbalanced credit scoring dataset, comparing the performance of different class imbalance. In order to achieve explainability as well as high predictive performance in credit scoring, we propose a model named as NATE which is Non-pArameTric approach for Explainable credit scoring. This explainable and comprehensible NATE allows us to analyse the key factors of credit scoring by SHAP values both locally and globally in addition to robust predictive power for creditworthiness. Second, the issue of class imbalance is investigated. Class imbalance in datasets occurs when there are a huge number of differences of observations between the classes in the dataset. The imbalanced class in real-world credit scoring datasets results in the biased classification performance for credit worthiness. As an approach to overcome the limitation of traditional resampling methods for class imbalance, we propose a model named as NOTE which is Non-parametric Oversampling Techniques for Explainable credit scoring. By using conditional Wasserstein Generative Adversarial Networks (cWGAN)-based oversampling technique paired with Non-parametric Stacked Autoen-coder (NSA), NOTE as a generative model allows to oversample minority class with reflecting the complex and non-linear patterns in the dataset. Therefore, NOTE predicts the classification and explains the credit scoring model with unbiased performance on a balanced credit scoring dataset. Third, incomplete data is also a common issue in credit scoring datasets. This missingness normally distorts the analysis and prediction for credit scoring, and results in the misclassification for creditworthiness. To address the issue of missing values in the dataset and overcome the limitation of conventional imputation methods, we propose a model named as DITE which is Denoising Imputation TEchniques for missingness in credit scoring. By using the extended Generative Adversarial Imputation Networks (GAIN) paired with randomised Singular Value Decomposition (rSVD), DITE is capable of replacing missing values with plausible estimation through reducing the noise and capturing complex missing patterns in dataset. To evaluate the robustness and effectiveness of the proposed models for key issues, namely, model explainability, class imbalance, and missing-ness in the dataset, the performances of models using ML are compared against the benchmarks of literature on publicly available real-world financial credit scoring datasets, respectively. Our experimental results successfully demonstrated the robustness and effectiveness of the novel concepts used in the models by outperforming the benchmarks. Furthermore, the pro-posed NATE, NOTE and DITE also lead to a better model explainability, suitability, stability, and superiority on complex and non-linear credit scoring datasets. Finally, this thesis demonstrated that the existing credit scoring models can be improved by novel computer science techniques in real-world problem of credit scoring domain

Bankruptcy prediction model using cost-sensitive extreme gradient boosting in the context of imbalanced datasets

In the process of bankruptcy prediction models, a class imbalanced problem has occurred which limits the performance of the models. Most prior research addressed the problem by applying resampling methods such as the synthetic minority oversampling technique (SMOTE). However, resampling methods lead to other issues, e.g., increasing noisy data and training time during the process. To improve the bankruptcy prediction model, we propose cost-sensitive extreme gradient boosting (CS-XGB) to address the class imbalanced problem without requiring any resampling method. The proposed method’s effectiveness is evaluated on six real-world datasets, i.e., the LendingClub, and five Polish companies’ bankruptcy. This research compares the performance of CS-XGB with other ensemble methods, including SMOTE-XGB which applies SMOTE to the training set before the learning process. The experimental results show that i) based on LendingClub, the CS-XGB improves the performance of XGBoost and SMOTE-XGB by more than 50% and 33% on bankruptcy detection rate (BDR) and geometric mean (GM), respectively, and ii) the CS-XGB model outperforms random forest (RF), Bagging, AdaBoost, XGBoost, and SMOTE-XGB in terms of BDR, GM, and the area under a receiver operating characteristic curve (AUC) based on the five Polish datasets. Besides, the CS-XGB model achieves good overall prediction results

Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection

Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various analyses.Comment: 34 pages; 21 Figures; 8 Table

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings