Artificial Intelligence And Credit Risk

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Artificial Intelligence and Credit Risk

Rossella Locatelli,Giovanni Pepe,Fabio Salis
Artificial Intelligence and Credit Risk Book PDF

Author : Rossella Locatelli,Giovanni Pepe,Fabio Salis
Publisher : Springer Nature
Page : 115 pages
File Size : 43,8 Mb
Release : 2022-09-13
Category : Business & Economics
ISBN : 9783031102363

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Artificial Intelligence and Credit Risk by Rossella Locatelli,Giovanni Pepe,Fabio Salis Pdf

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

Tiziano Bellini
Machine Learning and Artificial Intelligence for Credit Risk Analytics Book PDF

Author : Tiziano Bellini
Publisher : Wiley
Page : 304 pages
File Size : 42,7 Mb
Release : 2023-06-26
Category : Business & Economics
ISBN : 1119781051

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Machine Learning and Artificial Intelligence for Credit Risk Analytics by Tiziano Bellini Pdf

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.

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 50,8 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

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.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance Book PDF

Author : El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Publisher : International Monetary Fund
Page : 35 pages
File Size : 54,9 Mb
Release : 2021-10-22
Category : Business & Economics
ISBN : 9781589063952

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Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar Pdf

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.

FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk

Majid Bazarbash
FinTech in Financial Inclusion Machine Learning Applications in Assessing Credit Risk Book PDF

Author : Majid Bazarbash
Publisher : International Monetary Fund
Page : 34 pages
File Size : 51,9 Mb
Release : 2019-05-17
Category : Business & Economics
ISBN : 9781498316033

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FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk by Majid Bazarbash Pdf

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.

Disrupting Finance

Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Disrupting Finance Book PDF

Author : Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Publisher : Springer
Page : 194 pages
File Size : 49,5 Mb
Release : 2018-12-06
Category : Business & Economics
ISBN : 9783030023300

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Disrupting Finance by Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins Pdf

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.

Bio-Inspired Credit Risk Analysis

Lean Yu,Shouyang Wang,Kin Keung Lai,Ligang Zhou
Bio Inspired Credit Risk Analysis Book PDF

Author : Lean Yu,Shouyang Wang,Kin Keung Lai,Ligang Zhou
Publisher : Springer Science & Business Media
Page : 244 pages
File Size : 46,8 Mb
Release : 2008-04-24
Category : Business & Economics
ISBN : 9783540778035

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Bio-Inspired Credit Risk Analysis by Lean Yu,Shouyang Wang,Kin Keung Lai,Ligang Zhou Pdf

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)

Harald Scheule,Daniel Rösch
Deep Credit Risk Chinese Book PDF

Author : Harald Scheule,Daniel Rösch
Publisher : Deep Credit Risk
Page : 456 pages
File Size : 49,7 Mb
Release : 2021-07-22
Category : Electronic
ISBN : 0645245208

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Deep Credit Risk (Chinese) by Harald Scheule,Daniel Rösch Pdf

- 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 ...

The AI Book

Ivana Bartoletti,Anne Leslie,Shân M. Millie
The AI Book Book PDF

Author : Ivana Bartoletti,Anne Leslie,Shân M. Millie
Publisher : John Wiley & Sons
Page : 782 pages
File Size : 46,8 Mb
Release : 2020-04-09
Category : Business & Economics
ISBN : 9781119551928

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The AI Book by Ivana Bartoletti,Anne Leslie,Shân M. Millie Pdf

Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important

Credit Intelligence & Modelling

Raymond A. Anderson
Credit Intelligence Modelling Book PDF

Author : Raymond A. Anderson
Publisher : Oxford University Press
Page : 934 pages
File Size : 42,6 Mb
Release : 2022
Category : Credit analysis
ISBN : 9780192844194

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Credit Intelligence & Modelling by Raymond A. Anderson Pdf

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.

Artificial Intelligence and Law

Rushil Chandra,Karun Sanjaya
Artificial Intelligence and Law Book PDF

Author : Rushil Chandra,Karun Sanjaya
Publisher : Academic Guru Publishing House
Page : 272 pages
File Size : 55,7 Mb
Release : 2024-02-29
Category : Study Aids
ISBN : 9788119843947

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Artificial Intelligence and Law by Rushil Chandra,Karun Sanjaya Pdf

‘Artificial Intelligence and Law’ is a ground-breaking book that delves into the intersection of artificial intelligence (AI) and the legal domain, providing a comprehensive exploration of the evolving relationship between technology and the legal framework. Authored with meticulous research and expertise, the book offers a nuanced understanding of how AI technologies impact various facets of law, from legal practice to policy considerations. The authors skillfully navigate the intricate landscape of AI and its implications on legal processes, addressing challenges and opportunities presented by the integration of advanced technologies. With a focus on both theoretical and practical aspects, the book explores key themes such as the ethical considerations surrounding AI applications in law, the automation of legal tasks, and the implications for the legal profession. Readers will find insightful discussions on topics such as machine learning algorithms, natural language processing, and the use of AI in legal research. The book goes beyond a mere analysis of the present state, offering thoughtful insights into the future trajectory of AI in the legal domain and its potential impact on the justice system. ‘Artificial Intelligence and Law’ serves as an indispensable resource for legal professionals, scholars, and technologists seeking a comprehensive guide to the evolving landscape where AI and the law intersect. With its well-researched content and forward-looking perspective, the book contributes significantly to the ongoing discourse on the integration of artificial intelligence into the legal sphere.

Artificial Intelligence in Asset Management

Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Artificial Intelligence in Asset Management Book PDF

Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Page : 95 pages
File Size : 50,8 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781952927034

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Artificial Intelligence in Asset Management by Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari Pdf

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Artificial Intelligence in Financial Markets

Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos
Artificial Intelligence in Financial Markets Book PDF

Author : Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos
Publisher : Springer
Page : 349 pages
File Size : 42,6 Mb
Release : 2016-11-21
Category : Business & Economics
ISBN : 9781137488800

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Artificial Intelligence in Financial Markets by Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos Pdf

As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Machine Learning for Financial Risk Management with Python

Abdullah Karasan
Machine Learning for Financial Risk Management with Python Book PDF

Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
Page : 334 pages
File Size : 47,9 Mb
Release : 2021-12-07
Category : Computers
ISBN : 9781492085201

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Machine Learning for Financial Risk Management with Python by Abdullah Karasan Pdf

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: 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 Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models

Artificial Intelligence for Capital Markets

Syed Hasan Jafar,Hemachandran K,Hani El-Chaarani,Sairam Moturi,Neha Gupta
Artificial Intelligence for Capital Markets Book PDF

Author : Syed Hasan Jafar,Hemachandran K,Hani El-Chaarani,Sairam Moturi,Neha Gupta
Publisher : CRC Press
Page : 163 pages
File Size : 41,5 Mb
Release : 2023-05-15
Category : Computers
ISBN : 9781000867664

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Artificial Intelligence for Capital Markets by Syed Hasan Jafar,Hemachandran K,Hani El-Chaarani,Sairam Moturi,Neha Gupta Pdf

Artificial Intelligence for Capital Market throws light on the application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book. Features: Showcases artificial intelligence in finance service industry Explains credit and risk analysis Elaborates on cryptocurrencies and blockchain technology Focuses on the optimal choice of asset pricing model Introduces testing of market efficiency and forecasting in the Indian stock market This book serves as a reference book for academicians, industry professionals, traders, finance managers and stock brokers. It may also be used as textbook for graduate level courses in financial services and financial analytics.