A hybrid XGBoost-MLP model for credit risk assessment on Digital Supply Chain Finance

Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk being the biggest risk they face. Therefore, credit risk assessment based on the application of digital SCF is of great importance to commercial banks’ financial decisions. This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit risk of Digital SCF (DSCF). In this paper, 1357 observations from 85 Chinese-listed SMEs over the period 2016–2019 are selected as the empirical sample, and the important features of credit risk assessment in DSCF are automatically selected through the feature selection of the XGBoost model in the first stage, then followed by credit risk assessment through the MLP in the second stage. Based on the empirical results, we find that the XGBoost-MLP model has good performance in credit risk assessment, where XGBoost feature selection is important for the credit risk assessment model. From the perspective of DSCF, the results show that the inclusion of digital features improves the accuracy of credit risk assessment in SCF

Supplier selection with support vector regression and twin support vector regression

Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir.Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Predicting business failure using artificial intelligence system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information for decision making for financial institutions, investors, creditors, and other participants in the business market. Financial reporting systems provide relevant information that can be used to assess the financial position of firms. It is crucial to have classification and prediction models that can analyse this financial information and provide accurate assurance for users about business health. Recent studies have explored the use of machine learning tools as substitute for traditional statistical methods to develop classification models to classify firm insolvency according to financial statement information. However, these models have no ideal classifier, since each provides a certain percentage of wrong outputs, which is a crucial consideration; every percentage of wrong response can mean massive financial losses for stakeholders. Therefore, this study proposes new insolvency classification and perdition models based on machine learning modelling techniques to develop an improved classifier. Individual modelling techniques using statistical methods and machine learning were used to develop the classification model of business insolvency. The results showed that machine learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest performance based on all performance measurements used in the study, and it was the best individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble- Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it has been proven that DPL modelling approach is useful for business insolvency classification. A key contribution in enhancing individual classifier outputs is the use of traditional combining methods with two new aggregation methods in business insolvency (Fuzzy Logic and Consensus Approach). The Consensus Approach showed the best improvement in the results of all individual classifiers with average accuracy of 97.7%, and it is considered the best classification method not only in comparison with individual classifiers, but also with traditional combiners. This study pioneers the development of a time series business insolvency prediction model with Big Data for UK businesses. The aim of the model is to provide early prediction about a business health. Three prediction models were developed based on Nonlinear Autoregressive with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR), and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of 83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in comparison with the best individual classifier (deep learning)

Destek Vektör Regresyon ve İkiz Destek Vektör Regresyon Yöntemi ile Tedarikçi Seçimi

Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR.Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir

Destek Vektör Regresyon ve İkiz Destek Vektör Regresyon Yöntemi ile Tedarikçi Seçimi

Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression (SVR) and twin support vector regression (TSVR) techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR.Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon (DVR) ve ikiz destek vektör regresyon (İDVR) teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir

Machine learning using Multi-Modal Data Predicts the Production of Selective Laser Sintered 3D Printed Drug Products

Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate developments

Enhancing Future Skills and Entrepreneurship

This open access book presents the proceedings of the 3rd Indo-German Conference on Sustainability in Engineering held at Birla Institute of Technology and Science, Pilani, India, on September 16–17, 2019. Intended to foster the synergies between research and education, the conference is one of the joint activities of the BITS Pilani and TU Braunschweig conducted under the auspices of Indo-German Center for Sustainable Manufacturing, established in 2009. The book is divided into three sections: engineering, education and entrepreneurship, covering a range of topics, such as renewable energy forecasting, design & simulation, Industry 4.0, and soft & intelligent sensors for energy efficiency. It also includes case studies on lean and green manufacturing, and life cycle analysis of ceramic products, as well as papers on teaching/learning methods based on the use of learning factories to improve students’problem-solving and personal skills. Moreover, the book discusses high-tech ideas to help the large number of unemployed engineering graduates looking for jobs become tech entrepreneurs. Given its broad scope, it will appeal to academics and industry professionals alike

Self-learning modules for spectra evaluation

Monitoring milk processing is an essential task as it affects the quality and safety of the final product. The aim of this investigation was to develop and analyse the self-learning system for the supervision of the processing of milk. In the self-learning evaluation module, several algorithms for data analysis of near infrared (NIR) and Raman spectra was implemented for the prediction of sample quality and safety. In the first part of this thesis, the use of NIR spectroscopy for controlling milk processing was investigated. For this reason, a high-quality quartz flow cell with a 1 mm pathlength including temperature controlling option for liquids was implemented. For sample preparation, UHT-milk with 1.5 % fat content was measured at 5 °C and considered as the reference milk. Samples with various changes such as added water and cleaning solution, different fat content and temperature as well as milks from various suppliers were investigated as the modified samples. A data set from reference and modified samples was obtained with NIR measurements. In this study, first Savitzky-Golay derivative with second polynomial order and window size of 15 was applied. It was compared with the usefulness of raw spectrum and also the combination of raw and first derivative spectrum. For the self-learning sector, an autoencoder neural network was employed. Within this thesis, it was shown that the trained autoencoder using first derivative spectra was capable to detect 5 % added water and 9 % cleaning solution in the milk. However, by using the combination spectra, the difference of 0.1 % in fat concentration was perfectly recognized. These two procedures were able to detect milks from different suppliers and difference of 10 °C in the measurement temperature. Another part of this work was done using Raman spectroscopy. The aim of this part was to check if the previous result can be improved. In this step, the circulation method was again employed the same as in the previous part. However, because of the heat introduced to the sample by the laser using in Raman spectroscopy and the length of plastic tubes which can be affected by the temperature of the laboratory, the measurement temperature was kept at 10 °C. 1.5 % fat UHT milk was utilized as the reference sample. Milks with various changes such as different fat contents, various measurement temperatures and added water or cleaning solution were investigated as the modified samples. In this investigation, not only the autoencoder but also some chemometric models were utilized with the purpose of anomaly detection. Principal component analysis (PCA) was investigated to check if the various samples can becategorized separately. In addition, two chemometric modelling techniques such as principal component regression and Gaussian process regression were tested to check the ability for change detection. By using the results obtained by PCA, a sufficient categorization of various samples was not achieved. While the PCR did not present a promising prediction as the related R2 was 0.7, Gaussian process regression with R2 of 0.97 predicted the changes almost perfectly. The trained autoencoder and Gaussian process regression both were able to define 5 % water and cleaning solution, difference of 0.1 % fat content, and variation of 5 °C in the measurement temperature. In comparison between the autoencoder and Gaussian process regression, it should be mentioned that the Gaussian process regression was capable to determine more abnormal signals than the autoencoder, however, it must be trained with all the possible changes. In contrast, the autoencoder can be trained once just with reference signals and used in online monitoring properly. As the final part and to detect which type of anomalies happened during the milk processing, several classification approaches such as linear discriminant analysis, decision tree, support vector machine, and k nearest neighbour were utilized. While decision trees and linear discriminant analysis failed to effectively characterize the various types of anomalies, the k nearest neighbor and support vector machine presented promising results. The support vector machine presented an accuracy of 81.4 % for test set, while the k nearest neighbor showed an accuracy of 84.8 %. As a result, it is reasonable to assume that both algorithms are capable of classifying various groups of data accurately. It can help the milk business figure out whats going wrong during the processing of milk. In general, Raman spectroscopy produced better findings than NIR spectroscopy, because the typical absorption bands of milk components in NIR spectrometers may be impacted by high water absorption combined with substantial light scattering by fat globules. Additionally, the autoencoder as self-learning system was capable of correctly detecting changes during milk processing, however, classification algorithms can aid in obtaining more details.Die Überwachung der Milchverarbeitung ist eine wesentliche Aufgabe, da sie die Qualität und Sicherheit des Endprodukts beeinflusst. Das Ziel dieser Untersuchung war die Entwicklung und Analyse eines selbstlernenden Systems zur Überwachung der Milchverarbeitung. In dem selbstlernenden Auswertungsmodul wurden verschiedene Algorithmen zur Datenanalyse implementiert, um die Qualität und Sicherheit der Proben mit Hilfe spektroskopischer Methoden vorherzusagen. Im ersten Teil dieser Arbeit wurde der Einsatz der Nahinfrarot-Spektroskopie (NIR) zur Kontrolle der Milchverarbeitung untersucht. Zu diesem Zweck wurde eine hochwertige Quarzdurchflusszelle mit einer Schichtdicke von 1 mm und einer Temperiermöglichkeit für Flüssigkeiten eingesetzt. Zur Probenvorbereitung wurde UHT-Milch mit 1,5 % Fettgehalt bei 5 °C gemessen und als Referenzmilch betrachtet. Als modifizierte Proben wurden Proben mit verschiedenen Veränderungen wie Wasser- und Reinigungsmittelzusatz, unterschiedlichem Fettgehalt und Temperatur sowie Milch von verschiedenen Lieferanten untersucht. Mit NIR Messungen wurde ein Datensatz von Referenz- und modifizierten Proben gewonnen. In dieser Studie wurde die erste Savitzky-Golay-Ableitung mit zweiter Polynomordnung und einer Fenstergröße von 15 verwendet. Sie wurde mit der Auswertegüte des Rohspektrums und auch der Kombination aus Roh- und erstem Ableitungsspektrum verglichen. Für den selbstlernenden Bereich wurde ein neuronales Netz als Autoencoder eingesetzt. Im Rahmen dieser Arbeit wurde gezeigt, dass der trainierte Autoencoder unter Verwendung der ersten Ableitung in der Lage war, 5 % zugesetztes Wasser und 9 % Reinigungslösung in der Milch zu erkennen. Durch die Verwendung der Kombinationsspektren wurde auch der Unterschied von 0,1 % in der Fettkonzentration perfekt erkannt. Diese beiden Verfahren waren in der Lage, Milch von verschiedenen Lieferanten und einem Unterschied von 10 °C bei der Messtemperatur zu erkennen. Ein weiterer Teil dieser Arbeit wurde mit der Raman-Spektroskopie durchgeführt. Ziel dieses Teils war es, zu prüfen, ob das vorherige Ergebnis verbessert werden kann. In diesem Schritt wurde wieder die gleiche Zirkulationsmethode wie im vorherigen Teil verwendet. Wegen der Wärme, die durch den Laser bei der Raman-Spektroskopie in die Probe eingebracht wird, und der Länge der Kunststoffrohre, die durch die Temperatur im Labor beeinflusst werden kann, wurde die Messtemperatur jedoch bei 10 °C gehalten. Als Referenzprobe wurde UHT-Milch mit 1,5 % Fett verwendet. Milch mit verschiedenen Veränderungen wie unterschiedlichen Fettgehalten, verschiedenen Messtemperaturen und Zusatz von Wasser oder Reinigungslösung wurde als modifizierte Probe untersucht. In dieser Untersuchung wurden nicht nur der Autoencoder, sondern auch einige chemometrische Modelle zur Erkennung von Anomalien eingesetzt. Die Hauptkomponentenanalyse (PCA) wurde untersucht, um zu prüfen, ob die verschiedenen Proben separat kategorisiert werden können. Darüber hinaus wurden zwei chemometrische Modellierungstechniken wie die Hauptkomponentenregression und die Gaußsche Prozessregression getestet, um die Fähigkeit zur Erkennung von Veränderungen zu prüfen. Mit den Ergebnissen der PCA konnte keine ausreichende Kategorisierung der verschiedenen Proben erreicht werden. Während die Hauptkomponentenregression (PCR) keine vielversprechende Vorhersage lieferte, da das zugehörige R2 bei 0,7 lag, sagte die Gaußsche Prozessregression mit einem R2 von 0,97 die Veränderungen nahezu perfekt voraus. Sowohl der trainierte Autoencoder als auch die Gaußsche Prozessregression waren in der Lage, 5 % Wasser und Reinigungslösung, einen Unterschied von 0,1 % Fettgehalt und eine Variation der Messtemperatur von 5 °C zu detektieren. Im Vergleich von Autoencoder und der Gaußschen Prozessregression ist zu erwähnen, dass die Gaußsche Prozessregression in der Lage war, mehr anormale Signale zu bestimmen als der Autoencoder, allerdings muss sie mit allen möglichen Änderungentrainiert werden. Im Gegensatz dazu muss der Autoencoder nur einmal mit Referenzsignalentrainiert und kann dann für die Online-Überwachung verwendet werden. Als letzter Teil und umzu erkennen, welche Art von Anomalien während der Milchverarbeitung auftraten, wurden verschiedene Klassifizierungsansätze wie lineare Diskriminanzanalyse, Entscheidungsbaum, Support Vector Machine und K Nearest Neighbour verwendet. Während die Entscheidungsbäume und die lineare Diskriminanzanalyse nicht in der Lage waren, die verschiedenen Arten von Anomalien effektiv zu charakterisieren, lieferten die K Nearest Neighbour und die Support Vector Machine Methode vielversprechende Ergebnisse. Die Support Vector Machine wies eine Genauigkeit von 81,4 % für den Testsatz auf, während die K Nearest Neighbour Methode eine Genauigkeit von 84,8 % ergab. Daher kann man davon ausgehen, dass beide Algorithmen in der Lage sind, verschiedene Datengruppen genau zu klassifizieren. Dies kann der Milchwirtschaft helfen, herauszufinden, was bei der Verarbeitung von Milch falsch läuft. Im Allgemeinen lieferte die Raman-Spektroskopie bessere Ergebnisse als die NIR-Spektroskopie, da die typischen Absorptionsbanden der Milchbestandteile in NIR-Spektrometern durch eine hohe Wasserabsorption in Kombination mit einer erheblichen Lichtstreuung durch Fettkügelchen beeinträchtigt werden können. Darüber hinaus war der Autoencoder als selbstlernendes System in der Lage, Veränderungen während der Milchverarbeitung korrekt zu erkennen, jedoch können Klassifizierungsalgorithmen helfen, mehr Details zu erhalten