Machine learning applied to banking supervision a literature review

Guerra, P., & Castelli, M. (2021). Machine learning applied to banking supervision a literature review. Risks, 9(7), 1-24. [136]. https://doi.org/10.3390/risks9070136Machine learning (ML) has revolutionised data analysis over the past decade. Like in-numerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a compre-hensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms “machine learning” and (“bank” or “banking” or “supervision”). No language, date, or Journal filter was applied. Papers were then screened and selected according to their relevance. The final article base consisted of 41 papers and 2 book chapters, 53% of which were published in the top quartile journals in their field. Results are presented in a timeline according to the publication date and categorised by time slots. Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. The most relevant ML techniques encompass k-nearest neigh-bours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). Recent trends include developing early warning systems (EWS) for bankruptcy and refining stress testing. One limitation of this study is the paucity of contributions using supervisory data, which justifies the need for additional investigation in this field. However, there is increasing evidence that ML techniques can enhance data analysis and decision making in the banking industry.publishersversionpublishe

Research on the Design of Financial Management Model Based on SOM-PNN Driven by Digital Economy

This study proposes a novel financial risk prediction methodology by harnessing the power of self-organizing mapping (SOM) neural network and probabilistic neural network (PNN). The amalgamation of SOM and PNN\u27s advantageous characteristics is seamlessly integrated into the algorithm posited within this paper. In order to collate and prognosticate data, the SOM network employs a two-dimensional topological framework comprising of two layers of neurons. Subsequently, the PNN model expeditiously furnishes the final classification outcomes by processing the output results obtained from the SOM model. The technique developed atop this composite model offers accelerated computation, effectively mitigates the impact of noisy samples, and significantly augments model accuracy. Finally, the effectiveness of the proposed method was demonstrated through a comprehensive financial risk analysis of listed companies from 2016 to 2020. The experimental results show that the SOM-PNN method has achieved high accuracy in predicting the financial difficulties experienced by traditional companies in the selected company samples, exceeding 85%. Especially when the sample data is insufficient, its accuracy reaches 80%, surpassing other algorithms. Statement: In the modern era, financial institutions use big data to perform background analysis and review, continuously optimize, and adjust, in order to introduce quantitative analysis methods into every link of risk management as far as possible. This allows financial institutions to quickly achieve balance in the game process of risk and income, and achieve Profit maximization in local or even more space

Tradition and Innovation in Construction Project Management

This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

The Greek Current Account Deficit:Is it Sustainable after all?

The large Greek current account deficit figures reported during the past few years have become the source of increasing concern regarding its sustainability. Bearing in mind the variety of techniques employed and the views expressed as regards the analysis and the assessment of the size of the current account deficit, this paper resorts to using neural network architectures to demonstrate that, despite its size, the current account deficit of Greece can be considered sustainable. This conclusion, however, is not meant to neglect the structural weaknesses that lead to such a deficit. In fact, even in the absence of any financing requirements these high deficit figures point to serious competitiveness losses with everything that these may entail for the future performance of the Greek economy.Neural Networks; Current Account Deficit Sustainability

Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

Banks soundness plays a crucial role in determining economic prosperity. As such, banks are under intense scrutiny to make wise decisions that enhances bank stability. Artificial Intelligence (AI) plays a significant role in changing the way banks operate and service their customers. Banks are becoming more modern and relevant in people’s life as a result. The most significant contribution of AI is it provides a lifeline for bank’s survival. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions opportunities from the main strand of CAMELS into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). It is highly evident that banks will soon extinct if they do not embed AI into their operations. As such, AI is a done deal for banks. Yet will AI contribute to bank soundness remains to be seen

An empirical analysis on the credit scoring and the intermediary role of financing guarantee institutions of China's car loans

By the end of 2018, China's car ownership has reached 240 million, an increase of 10.51% over 2017, which leads to the increase of automobile financial services and hence the associated automobile credit risks. In order to transfer risks, financial institutions increasingly are choosing to issue auto loans through financing guarantee companies. Therefore, the industry pays more attention to the credit scoring, as it acts as the main risk control measure of auto financing guarantee companies. This leads to the study of the role the financing guarantee company plays and how effective the credit rating is as a risk control mechanism. The purpose is to investigate whether the auto financing guarantee company plays a mediating role by providing credit score. The empirical approach is as follows: a two-stage regression method is used to control or eliminate the influence of personal characteristics and other third-party credit ratings. Through which, we firstly test whether the credit score of an auto financing guarantee company contains additional information besides personal characteristics and third-party credit scores. Second, we test whether additional information of auto financing guarantee company can significantly explain the post-loan performance of whether default or non-default. The conclusions show that even after controlling the third-party credit score and personal characteristics, the credit scoring system of auto financing guarantee companies still has a significant explanation on the performance of post-loan default. In other words, it plays an intermediary role by providing credit evaluation services, which has a direct decision reference for the financial institutions that ultimately provide credit. Based on this, this study puts forward corresponding management enhancement and loan risk management suggestions.No final de 2018, a propriedade automóvel na China atingiu 240 milhões, um aumento de 10.51% sobre 2017, o que leva ao aumento dos serviços financeiros automóvel e, portanto, dos riscos de crédito automóvel associados. Para mitigar riscos, as instituições financeiras optam, cada vez mais, por conceder empréstimos automóvel através de empresas de garantia. Por conseguinte, a indústria presta mais atenção à pontuação do crédito, uma vez que esta atua como a principal medida de controlo do risco das empresas de garantia de financiamento-automóvel. Isto conduz ao estudo do papel desempenhado pela empresa de garantia de financiamento e da eficácia da sua notação de crédito como mecanismo de controlo dos riscos. Com base no sistema de notação de crédito da T’s e num total de 119.798 registos de empréstimos, este estudo examina o poder explicativo da notação de crédito das empresas de garantia de financiamento automóvel no incumprimento dos mutuários e as funções mediadoras destas empresas. Utiliza-se um método de regressão em dois estágios para controlar ou eliminar a influência de características pessoais e outros ratings, testando primeiro se a notação de crédito de uma empresa de garantia contém informações adicionais e testando, depois, se as informações adicionais da empresa de garantia podem explicar significativamente o desempenho do mutuário pós-empréstimo, As conclusões mostram que, mesmo após controlar a notação de crédito de terceiros e as características pessoais, o sistema de notação de crédito das empresas de garantia tem uma explicação significativa no desempenho do mutuário pós-empréstimo. Ou seja, ele desempenha um papel mediador, fornecendo serviços de avaliação de crédito que têm influência direta na decisão das instituições financeiras que, finalmente, fornecem crédito. Correspondentemente, esta investigação apresenta sugestões de melhoramento da gestão do risco de crédito

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: Next Frontier for Intelligent Safe-Driving Assessment

Securing safe-driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Besides, diverse malicious network attacks become ubiquitous along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. Motivated by this, in this article, we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), that offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then, we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks.Comment: 8 pages, 6 figures. This paper has been accepted for publication by IEEE Networ