Artificial Intelligence And Credit Risk
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Artificial Intelligence And Credit Risk
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Author : Rossella Locatelli
language : en
Publisher: Springer Nature
Release Date : 2022-09-13
Artificial Intelligence And Credit Risk written by Rossella Locatelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-13 with Business & Economics categories.
This book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as “traditional” and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the new AI toolbox on new (unconventional) data to enhance the models’ predictive power, without neglecting problems due to results’ interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions.
Machine Learning And Artificial Intelligence For Credit Risk Analytics
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Author : Tiziano Bellini
language : en
Publisher: Wiley
Release Date : 2023-06-26
Machine Learning And Artificial Intelligence For Credit Risk Analytics written by Tiziano Bellini and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Business & Economics categories.
Machine Learning and Artificial Intelligence for Credit Risk Analytics provides a comprehensive, practical toolkit for applying ML and AI to day-to-day credit risk management challenges. Beginning with coverage of data management in banking, the book goes on to discuss individual and multiple classifier approaches, reinforcement learning and AI in credit portfolio modelling, lifetime PD modelling, LGD modelling and EAD modelling. Fully worked examples in Python and R appear throughout the book, with source code provided on the companion website. Machine Learning and Artificial Intelligence for Credit Risk Analytics fully covers the key concepts required to understand, challenge and validate credit risk models, whilst also looking to the future development of AI applications in credit risk management, demonstrating the need to embed economics and statistics to inform short, medium and long-term decision-making.
Fintech In Financial Inclusion Machine Learning Applications In Assessing Credit Risk
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Author : Majid Bazarbash
language : en
Publisher: International Monetary Fund
Release Date : 2019-05-17
Fintech In Financial Inclusion Machine Learning Applications In Assessing Credit Risk written by Majid Bazarbash and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Business & Economics categories.
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Bio Inspired Credit Risk Analysis
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Author : Lean Yu
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-04-24
Bio Inspired Credit Risk Analysis written by Lean Yu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-24 with Business & Economics categories.
Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.
Deep Credit Risk Chinese
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Author : Harald Scheule
language : en
Publisher: Deep Credit Risk
Release Date : 2021-07-22
Deep Credit Risk Chinese written by Harald Scheule and has been published by Deep Credit Risk this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-22 with categories.
- 了解流动性，房屋净值和许多其他关键银行业特征变量的作用； - 选择并处理变量； - 预测违约、偿付、损失率和风险敞口； - 利用危机前特征预测经济衰退和危机后果； - 理解COVID-19对信用风险带来的影响； - 将创新的抽样技术应用于模型训练和验证； - 从Logit分类器到随机森林和神经网络的深入学习; - 进行无监督聚类、主成分和贝叶斯技术的应用； - 为CECL、IFRS 9和CCAR建立多周期模型； - 建立用于在险价值和期望损失的信贷组合相关模型； - 使用更多真实的信用风险数据并运行超过1500行的代码... - Understand the role of liquidity, equity and many other key banking features - Engineer and select features - Predict defaults, payoffs, loss rates and exposures - Predict downturn and crisis outcomes using pre-crisis features - Understand the implications of COVID-19 - Apply innovative sampling techniques for model training and validation - Deep-learn from Logit Classifiers to Random Forests and Neural Networks - Do unsupervised Clustering, Principal Components and Bayesian Techniques - Build multi-period models for CECL, IFRS 9 and CCAR - Build credit portfolio correlation models for VaR and Expected Shortfal - Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code - Access real credit data and much more ...
Powering The Digital Economy Opportunities And Risks Of Artificial Intelligence In Finance
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Author : El Bachir Boukherouaa
language : en
Publisher: International Monetary Fund
Release Date : 2021-10-22
Powering The Digital Economy Opportunities And Risks Of Artificial Intelligence In Finance written by El Bachir Boukherouaa and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Business & Economics categories.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Artificial intelligence categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Credit Intelligence Modelling
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Author : Raymond A. Anderson
language : en
Publisher: Oxford University Press
Release Date : 2022
Credit Intelligence Modelling written by Raymond A. Anderson and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Credit analysis categories.
Credit Intelligence and Modelling provides an indispensable explanation of the statistical models and methods used when assessing credit risk and automating decisions. Over eight modules, the book covers consumer and business lending in both the developed and developing worlds, providing the frameworks for both theory and practice. It first explores an introduction to credit risk assessment and predictive modelling, micro-histories of credit and credit scoring, as well as the processes used throughout the credit risk management cycle. Mathematical and statistical tools used to develop and assess predictive models are then considered, in addition to project management and data assembly, data preparation from sampling to reject inference, and finally model training through to implementation. Although the focus is credit risk, especially in the retail consumer and small-business segments, many concepts are common across disciplines, whether for academic research or practical use. The book assumes little prior knowledge, thus making it an indispensable desktop reference for students and practitioners alike. Credit Intelligence and Modelling expands on the success of The Credit Scoring Toolkit to cover credit rating and intelligence agencies, and the data and tools used as part of the process.
Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Business & Economics categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.
Artificial Intelligence For Risk Mitigation In The Financial Industry
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Author : Ambrish Kumar Mishra
language : en
Publisher: John Wiley & Sons
Release Date : 2024-05-29
Artificial Intelligence For Risk Mitigation In The Financial Industry written by Ambrish Kumar Mishra and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-29 with Computers categories.
Artificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.