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| supervisory guidance on model risk management: Supervisory Guidance on Model Risk Management (SR 11-7) Versus Enterprise-Wide Model Risk Management for Deposit-Taking Institutions (E-23) Nicholas Kiritz, 2019 The Federal Reserve Board (Fed) and Office of the Comptroller of the Currency (OCC) issued SR 11-7 (OCC 2011-12 for the OCC) on April 4, 2011, and the Federal Deposit Insurance Corporation (FDIC) adopted it as FIL-22-2017 on June 7, 2017. The Office of the Superintendent of Financial Institutions (OSFI) issued the Enterprise-Wide Model Risk Management for Deposit-Taking Institutions (E-23) Guideline in September 2017, with an effective date of November 1, 2017. The authors of E-23 were almost certainly aware of the text and application of SR 11-7 when writing their document. The significant differences between the two documents then, were conscious -- and likely reflect either differences in style and approach to writing documents of this type, differences in substantive expectations regarding model risk management in financial institutions, or both. This paper attempts to discern the differences in expectations regarding model risk management for large, complex institutions between the U.S. supervisors (Fed, OCC, and FDIC) and OSFI.The U.S. supervisors' approach, as expressed in SR 11-7, is more prescriptive than OSFI's approach in E-23. Although both supervisors see their documents as principles-based, the Canadian document, with much less detail, seems to be more principles-based than the U.S. one. Given the less detailed language in E-23 and the Canadian supervisory approach more broadly, Canadian supervisors will likely have more leeway regarding the application of these principles in individual institutions than U.S. supervisors will. SR 11-7 often gives one specific, relatively detailed approach to fulfilling a general principle included in E-23, which may expect multiple possible approaches to meeting a specific principle. This divergence in approaches to writing directions for model risk management poses a challenge when trying to compare the supervisory expectations of U.S. and Canadian supervisors, which is exacerbated by the fact that E-23 is only now coming into force in Canada. Given the global prominence of SR 11-7, it is likely that Canadian supervisors considered the U.S. supervisory text, and that any high-level omissions are likely intentional. It is also important to keep in mind that SR 11-7 is “Guidance,” not regulation, and U.S. supervisors may not enforce all parts of SR 11-7 at all institutions. |
| supervisory guidance on model risk management: Recommendations for Central Counterparties Group of Ten. Committee on Payment and Settlement Systems, 2004 |
| supervisory guidance on model risk management: Federal Reserve Manual , 1918 |
| supervisory guidance on model risk management: Risk Management Handbook Federal Aviation Administration, 2012-07-03 Every day in the United States, over two million men, women, and children step onto an aircraft and place their lives in the hands of strangers. As anyone who has ever flown knows, modern flight offers unparalleled advantages in travel and freedom, but it also comes with grave responsibility and risk. For the first time in its history, the Federal Aviation Administration has put together a set of easy-to-understand guidelines and principles that will help pilots of any skill level minimize risk and maximize safety while in the air. The Risk Management Handbook offers full-color diagrams and illustrations to help students and pilots visualize the science of flight, while providing straightforward information on decision-making and the risk-management process. |
| supervisory guidance on model risk management: Understanding and Managing Model Risk Massimo Morini, 2011-11-07 A guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications. |
| supervisory guidance on model risk management: International Convergence of Capital Measurement and Capital Standards , 2004 |
| supervisory guidance on model risk management: Model Risk Management with SAS SAS, 2020-06-29 Cut through the complexity of model risk management with a guide to solutions from SAS! There is an increasing demand for more model governance and model risk awareness. At the same time, high-performing models are expected to be deployed faster than ever. SAS Model Risk Management is a user-friendly, web-based application that facilitates the capture and life cycle management of statistical model-related information. It enables all stakeholders in the model life cycle — developers, validators, internal audit, and management – to get overview reports as well as detailed information in one central place. Model Risk Management with SAS introduces you to the features and capabilities of this software, including the entry, collection, transfer, storage, tracking, and reporting of models that are drawn from multiple lines of business across an organization. This book teaches key concepts, terminology, and base functionality that are integral to SAS Model Risk Management through hands-on examples and demonstrations. With this guide to SAS Model Risk Management, your organization can be confident it is making fact-based decisions and mitigating model risk. |
| supervisory guidance on model risk management: Audit and Accounting Guide Depository and Lending Institutions AICPA, 2019-11-20 The financial services industry is undergoing significant change. This has added challenges for institutions assessing their operations and internal controls for regulatory considerations. Updated for 2019, this industry standard resource offers comprehensive, reliable accounting implementation guidance for preparers. It offers clear and practical guidance of audit and accounting issues, and in-depth coverage of audit considerations, including controls, fraud, risk assessment, and planning and execution of the audit. Topics covered include: Transfers and servicing; Troubled debt restructurings; Financing receivables and the allowance for loan losses; and, Fair value accounting This guide also provides direction for institutions assessing their operations and internal controls for regulatory considerations as well as discussions on existing regulatory reporting matters. The financial services industry is undergoing significant change. This has added challenges for institutions assessing their operations and internal controls for regulatory considerations. Updated for 2019, this industry standard resource offers comprehensive, reliable accounting implementation guidance for preparers. It offers clear and practical guidance of audit and accounting issues, and in-depth coverage of audit considerations, including controls, fraud, risk assessment, and planning and execution of the audit. Topics covered include: Transfers and servicing; Troubled debt restructurings; Financing receivables and the allowance for loan losses; and, Fair value accounting This guide also provides direction for institutions assessing their operations and internal controls for regulatory considerations as well as discussions on existing regulatory reporting matters. |
| supervisory guidance on model risk management: Validation of Risk Management Models for Financial Institutions David Lynch, Iftekhar Hasan, Akhtar Siddique, 2023-03-09 Financial models are an inescapable feature of modern financial markets. Yet it was over reliance on these models and the failure to test them properly that is now widely recognized as one of the main causes of the financial crisis of 2007–2011. Since this crisis, there has been an increase in the amount of scrutiny and testing applied to such models, and validation has become an essential part of model risk management at financial institutions. The book covers all of the major risk areas that a financial institution is exposed to and uses models for, including market risk, interest rate risk, retail credit risk, wholesale credit risk, compliance risk, and investment management. The book discusses current practices and pitfalls that model risk users need to be aware of and identifies areas where validation can be advanced in the future. This provides the first unified framework for validating risk management models. |
| supervisory guidance on model risk management: Machine Learning for High-Risk Applications Patrick Hall, James Curtis, Parul Pandey, 2023-04-17 The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab |
| supervisory guidance on model risk management: Third International Conference on Credit Analysis and Risk Management Joseph Callaghan, Hong Qian, 2015-09-04 Held at Oakland University, School of Business Administration, Department of Accounting and Finance. This book provides a summary of state-of-the-art methods and research in the analysis of credit. As such, it offers very useful insights into this vital area of finance, which has too often been under-researched and little-taught in academia. Including an overview of processes that are utilized for estimating the probability of default and the loss given default for a wide array of debts, the book will also be useful in evaluating individual loans and bonds, as well as managing entire portfolios of such assets. Each chapter is written by authors who presented and discussed their contemporary research and knowledge at the Third International Conference on Credit Analysis and Risk Management, held on August 21–22, 2014 at the Department of Accounting and Finance, School of Business administration, Oakland University. This collection of writings by these experts in the field is uniquely designed to enhance the understanding of credit analysis in a fashion that permits a broad perspective on the science and art of credit analysis. |
| supervisory guidance on model risk management: Modern Data Mining with Python Dushyant Singh Sengar, Vikash Chandra, 2024-02-26 Data miner’s survival kit for explainable, effective, and efficient algorithms enabling responsible decision-making KEY FEATURES ● Accessible, and case-based exploration of the most effective data mining techniques in Python. ● An indispensable guide for utilizing AI potential responsibly. ● Actionable insights on modeling techniques, deployment technologies, business needs, and the art of data science, for risk mitigation and better business outcomes. DESCRIPTION Modern Data Mining with Python is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards. After reading this book, readers will be equipped with the skills and knowledge necessary to use Python for data mining and analysis in an industry set-up. They will be able to analyze and implement algorithms on large structured and unstructured datasets. WHAT YOU WILL LEARN ● Explore the data mining spectrum ranging from data exploration and statistics. ● Gain hands-on experience applying modern algorithms to real-world problems in the financial industry. ● Develop an understanding of various risks associated with model usage in regulated industries. ● Gain knowledge about best practices and regulatory guidelines to mitigate model usage-related risk in key banking areas. ● Develop and deploy risk-mitigated algorithms on self-serve ModelOps platforms. WHO THIS BOOK IS FOR This book is for a wide range of early career professionals and students interested in data mining or data science with a financial services industry focus. Senior industry professionals, and educators, trying to implement data mining algorithms can benefit as well. TABLE OF CONTENTS 1. Understanding Data Mining in a Nutshell 2. Basic Statistics and Exploratory Data Analysis 3. Digging into Linear Regression 4. Exploring Logistic Regression 5. Decision Trees with Bagging and Boosting 6. Support Vector Machines and K-Nearest Neighbors 7. Putting Dimensionality Reduction into Action 8. Beginning with Unsupervised Models 9. Structured Data Classification using Artificial Neural Networks 10. Language Modeling with Recurrent Neural Networks 11. Image Processing with Convolutional Neural Networks 12. Understanding Model Risk Management for Data Mining Models 13. Adopting ModelOps to Manage Model Risk |
| supervisory guidance on model risk management: The Next Systemic Financial Crisis – Where Might it Come From? Andreas Dombret, Patrick Kenadjian, 2024-01-29 Where might the next systemic financial crisis come from? And how do we achieve financial stability in a poly crisis world? This book addresses macroeconomic factors, crypto assets, non-bank financial institutions and regulated financial service providers, keeping in mind that each sector can interact with the others to produce a cluster of risks with compounding effects. |
| supervisory guidance on model risk management: Quantitative Enterprise Risk Management Mary R. Hardy, David Saunders, 2022-05-05 This relevant, readable text integrates quantitative and qualitative approaches, connecting key mathematical tools to real-world challenges. |
| supervisory guidance on model risk management: The XVA of Financial Derivatives: CVA, DVA and FVA Explained Dongsheng Lu, 2016-01-01 This latest addition to the Financial Engineering Explained series focuses on the new standards for derivatives valuation, namely, pricing and risk management taking into account counterparty risk, and the XVA's Credit, Funding and Debt value adjustments. |
| supervisory guidance on model risk management: Commercial Banking Risk Management Weidong Tian, 2016-12-08 This edited collection comprehensively addresses the widespread regulatory challenges uncovered and changes introduced in financial markets following the 2007-2008 crisis, suggesting strategies by which financial institutions can comply with stringent new regulations and adapt to the pressures of close supervision while responsibly managing risk. It covers all important commercial banking risk management topics, including market risk, counterparty credit risk, liquidity risk, operational risk, fair lending risk, model risk, stress test, and CCAR from practical aspects. It also covers major components of enterprise risk management, a modern capital requirement framework, and the data technology used to help manage risk. Each chapter is written by an authority who is actively engaged with large commercial banks, consulting firms, auditing firms, regulatory agencies, and universities. This collection will be a trusted resource for anyone working in or studying the commercial banking industry. |
| supervisory guidance on model risk management: PRACTICAL AND ADVANCED MACHINE LEARNING METHODS FOR MODEL RISK MANAGEMENT INDRA REDDY MALLELA NAGARJUNA PUTTA PROF.(DR.) AVNEESH KUMAR, 2024-12-22 In today’s fast-evolving landscape of artificial intelligence (AI) and machine learning (ML), organizations are increasingly relying on advanced models to drive decision-making and innovation across various sectors. As machine learning technologies grow in complexity and scale, managing the risks associated with these models becomes a critical concern. From biases in algorithms to the interpretability of predictions, the potential for errors and unintended consequences demands rigorous frameworks for assessing and mitigating risks. Practical and Advanced Machine Learning Methods for Model Risk Management explores these challenges in depth. It is designed to provide both foundational knowledge and advanced techniques for effectively managing model risks throughout their lifecycle—from development and deployment to monitoring and updating. This book caters to professionals working in data science, machine learning engineering, risk management, and governance, offering a comprehensive understanding of how to balance model performance with robust risk management practices. The book begins with a strong foundation in the principles of model risk management (MRM), exploring the core concepts of risk identification, assessment, and mitigation. From there, it dives into more advanced techniques for managing risks in complex ML models, including methods for ensuring model fairness, transparency, and interpretability, as well as strategies for dealing with adversarial attacks, data security concerns, and ethical considerations. Throughout, we emphasize the importance of collaboration between data scientists, risk professionals, and organizational leaders in creating a culture of responsible AI. This collaborative approach is crucial for building models that not only perform at the highest levels but also adhere to ethical standards and regulatory requirements. By the end of this book, readers will have a deep understanding of the critical role that risk management plays in AI and machine learning, as well as the practical tools and methods needed to implement a comprehensive MRM strategy. Whether you are just beginning your journey in model risk management or are seeking to refine your existing processes, this book serves as an essential resource for navigating the complexities of machine learning in today’s rapidly changing technological landscape. We hope this book equips you with the knowledge to effectively address the risks of ML models and apply these insights to improve both the performance and trustworthiness of your AI systems. Thank you for embarking on this journey with us. Authors |
| supervisory guidance on model risk management: The Validation of Risk Models S. Scandizzo, 2016-07-01 This book is a one-stop-shop reference for risk management practitioners involved in the validation of risk models. It is a comprehensive manual about the tools, techniques and processes to be followed, focused on all the models that are relevant in the capital requirements and supervisory review of large international banks. |
| supervisory guidance on model risk management: Risk and EU law Hans-W. Micklitz, Takis Tridimas, 2015-09-25 Risk and EU Law considers the multiple reasons for the increase in the types and diversity of risks, as well as the potential magnitude of their undesirable effects. The book identifies such reasons as; the openness of liberal societies; market competition; the constant endeavour to innovate; as well as globalization and the impact of new technologies. It also explores topics surrounding the social epistemology of risk observation and management, the role of science in political and judicial decision-making and transnational risk regulation and contractual governance. |
| supervisory guidance on model risk management: Financial Institution Advantage and the Optimization of Information Processing Sean C. Keenan, 2015-03-02 A PROVEN APPROACH FOR CREATING and IMPLEMENTING EFFECTIVE GOVERNANCE for DATA and ANALYTICS Financial Institution Advantage and the Optimization of Information Processing offers a key resource for understanding and implementing effective data governance practices and data modeling within financial organizations. Sean Keenan—a noted expert on the topic—outlines the strategic core competencies, includes best practices, and suggests a set of mechanisms for self-evaluation. He shows what it takes for an institution to evaluate its information processing capability and how to take the practical steps toward improving it. Keenan outlines the strategies and tools needed for financial institutions to take charge and make the much-needed decisions to ensure that their firm's information processing assets are effectively designed, deployed, and utilized to meet the strict regulatory guidelines. This important resource is filled with practical observations about how information assets can be actively and effectively managed to create competitive advantage and improved financial results. Financial Institution Advantage and the Optimization of Information Processing also includes a survey of case studies that highlight both the positive and less positive results that have stemmed from institutions either recognizing or failing to recognize the strategic importance of information processing capabilities. |
| supervisory guidance on model risk management: Enterprise Compliance Risk Management Saloni Ramakrishna, 2015-09-04 The tools and information that build effective compliance programs Enterprise Compliance Risk Management: An Essential Toolkit for Banks and Financial Services is a comprehensive narrative on managing compliance and compliance risk that enables value creation for financial services firms. Compliance risk management, a young, evolving yet intricate discipline, is occupying center stage owing to the interplay between the ever increasing complexity of financial services and the environmental effort to rein it in. The book examines the various facets of this layered and nuanced subject. Enterprise Compliance Risk Management elevates the context of compliance from its current reactive stance to how a proactive strategy can create a clear differentiator in a largely undifferentiated market and become a powerful competitive weapon for organizations. It presents a strong case as to why it makes immense business sense to weave active compliance into business model and strategy through an objective view of the cost benefit analysis. Written from a real-world perspective, the book moves the conversation from mere evangelizing to the operationalizing a positive and active compliance management program in financial services. The book is relevant to the different stakeholders of the compliance universe - financial services firms, regulators, industry bodies, consultants, customers and compliance professionals owing to its coverage of the varied aspects of compliance. Enterprise Compliance Risk Management includes a direct examination of compliance risk, including identification, measurement, mitigation, monitoring, remediation, and regulatory dialogue. With unique hands-on tools including processes, templates, checklists, models, formats and scorecards, the book provides the essential toolkit required by the practitioners to jumpstart their compliance initiatives. Financial services professionals seeking a handle on this vital and growing discipline can find the information they need in Enterprise Compliance Risk Management. |
| supervisory guidance on model risk management: Quantitative Financial Risk Management Constantin Zopounidis, Emilios Galariotis, 2015-06-08 A Comprehensive Guide to Quantitative Financial Risk Management Written by an international team of experts in the field, Quantitative Financial Risk Management: Theory and Practice provides an invaluable guide to the most recent and innovative research on the topics of financial risk management, portfolio management, credit risk modeling, and worldwide financial markets. This comprehensive text reviews the tools and concepts of financial management that draw on the practices of economics, accounting, statistics, econometrics, mathematics, stochastic processes, and computer science and technology. Using the information found in Quantitative Financial Risk Management can help professionals to better manage, monitor, and measure risk, especially in today's uncertain world of globalization, market volatility, and geo-political crisis. Quantitative Financial Risk Management delivers the information, tools, techniques, and most current research in the critical field of risk management. This text offers an essential guide for quantitative analysts, financial professionals, and academic scholars. |
| supervisory guidance on model risk management: Financial Risk Management Steve L. Allen, 2012-12-19 A top risk management practitioner addresses the essentialaspects of modern financial risk management In the Second Edition of Financial Risk Management +Website, market risk expert Steve Allen offers an insider'sview of this discipline and covers the strategies, principles, andmeasurement techniques necessary to manage and measure financialrisk. Fully revised to reflect today's dynamic environment and thelessons to be learned from the 2008 global financial crisis, thisreliable resource provides a comprehensive overview of the entirefield of risk management. Allen explores real-world issues such as proper mark-to-marketvaluation of trading positions and determination of needed reservesagainst valuation uncertainty, the structuring of limits to controlrisk taking, and a review of mathematical models and how they cancontribute to risk control. Along the way, he shares valuablelessons that will help to develop an intuitive feel for market riskmeasurement and reporting. Presents key insights on how risks can be isolated, quantified,and managed from a top risk management practitioner Offers up-to-date examples of managing market and creditrisk Provides an overview and comparison of the various derivativeinstruments and their use in risk hedging Companion Website contains supplementary materials that allowyou to continue to learn in a hands-on fashion long after closingthe book Focusing on the management of those risks that can besuccessfully quantified, the Second Edition of FinancialRisk Management + Websiteis the definitive source for managingmarket and credit risk. |
| supervisory guidance on model risk management: Responsible AI Sray Agarwal, Shashin Mishra, 2021-09-13 This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination. The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter – providing the details that enable the business analysts and the data scientists to implement these fundamentals. AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it. |
| supervisory guidance on model risk management: The Law of Governance, Risk Management and Compliance Geoffrey P. Miller, 2014-03-17 The first casebook on the law of governance, risk management, and compliance. Author Geoffrey P. Miller, a highly respected professor of corporate and financial law, also brings real world experience to the book as a member of the board of directors and audit and risk committees of a significant banking institution. The book addresses issues of fundamental importance for any regulated organization (the $13 billion settlement between JPMorgan Chase and its regulators is only one of many examples). This book can be a cornerstone for courses on compliance, corporate governance, or on the role of attorneys in managing risk in organizational clients. Features: Addresses issues of enormous and growing importance that are not covered by other law school casebooks. Presents numerous cutting edge issues in a rapidly growing body of law and practice. Covers a subject matter that is a major employment opportunity for law school graduates. Professors who adopt this book participate in a new and burgeoning field of academic study and legal practice. Covers general issues as well as specific fields of compliance and risk management. Includes two sets of case studies--one on cases where compliance programs broke down (e.g., Enron, WorldComm, and JP Global), and one on cases where risk management broke down (e.g., UBS and the financial crisis, and JPMorgan Chase and the London whale). Features fewer cases and a higher ratio of author-written text and materials drawn from regulatory publications than in typical law school casebooks. Authored by a professor who is also an independent director of a financial institution. |
| supervisory guidance on model risk management: Self-Service Data Analytics and Governance for Managers Nathan E. Myers, Gregory Kogan, 2021-04-28 Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment. In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap. This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands. |
| supervisory guidance on model risk management: Artificial Intelligence and Credit Risk Rossella Locatelli, Giovanni Pepe, Fabio Salis, 2022-09-13 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. |
| supervisory guidance on model risk management: Quantitative Finance And Risk Management: A Physicist's Approach (2nd Edition) Jan W Dash, 2016-05-10 Written by a physicist with extensive experience as a risk/finance quant, this book treats a wide variety of topics. Presenting the theory and practice of quantitative finance and risk, it delves into the 'how to' and 'what it's like' aspects not covered in textbooks or papers. A 'Technical Index' indicates the mathematical level for each chapter.This second edition includes some new, expanded, and wide-ranging considerations for risk management: Climate Change and its long-term systemic risk; Markets in Crisis and the Reggeon Field Theory; 'Smart Monte Carlo' and American Monte Carlo; Trend Risk — time scales and risk, the Macro-Micro model, singular spectrum analysis; credit risk: counterparty risk and issuer risk; stressed correlations — new techniques; and Psychology and option models.Solid risk management topics from the first edition and valid today are included: standard/advanced theory and practice in fixed income, equities, and FX; quantitative finance and risk management — traditional/exotic derivatives, fat tails, advanced stressed VAR, model risk, numerical techniques, deals/portfolios, systems, data, economic capital, and a function toolkit; risk lab — the nuts and bolts of risk management from the desk to the enterprise; case studies of deals; Feynman path integrals, Green functions, and options; and 'Life as a Quant' — communication issues, sociology, stories, and advice. |
| supervisory guidance on model risk management: Climate Change Risk Management in Banks Saloni P. Ramakrishna, 2023-12-04 Banks, like other businesses, endeavor to drive revenue and growth, while deftly managing the risks. Dubbed the next frontier in risk management for financial services, climate related risks are the newest and potentially the most challenging set of risks that banks are encountering. On the one hand, banks must show their commitment to becoming net zero and, on the other, help their customers transition to more sustainable operations, all this while managing climate-related financial risks. It is a paradigm shift from how the banking industry has traditionally managed risks as climate change risks are complex. They are multilayered, multidimensional with uncertain climate pathways that impact real economy which in turn influences the financial ecosystem in myriad ways. Climate Change Risk Management in Banks weaves the complete lifecycle of climate risk management from strategy to disclosures, a must-read for academics, banking professionals and other stakeholders interested in understanding and managing climate change risk. It provides much-needed insights, enabling organizations to respond well to these new risks, protect their businesses, mitigate losses and enhance brand value. Saloni Ramakrishna, an acknowledged financial industry practitioner, argues that given the uncertain and volatile climate paths, complex geopolitical patterns, and sustainability challenges, banks and business professionals will benefit from a wholistic approach to managing climate change risks. The book provides a blueprint and a cohesive framework for embracing and maintaining such an approach, in a simple and structured format. |
| supervisory guidance on model risk management: The Machine Learning Solutions Architect Handbook David Ping, 2022-01-21 Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook. |
| supervisory guidance on model risk management: Federal Register , 2013-08 |
| supervisory guidance on model risk management: Smart Financial Market: AI and the Future of Banking Pritam Mehta, Dr. K Syamala, Dipendu Das, Priya Kumari, Saumya Raj, 2024-08-25 Smart Financial Market: AI and the Future of Banking offers a comprehensive exploration of how artificial intelligence is transforming the financial industry. This essential read covers critical topics such as FinTech innovations, robo-advising, and evolving payment methods. The book is a collaboration of experts, including engineers, professors, law students, and bank managers, ensuring that the content is both authoritative and up-to-date with the current landscape. Delving into the intersection of technology and finance, this book provides readers with insights into the latest AI-driven solutions that are reshaping banking services. From the rise of FinTech startups disrupting traditional banking models to the advent of robo-advisors offering personalized financial guidance, this book examines how AI is creating new opportunities and challenges within the financial sector. |
| supervisory guidance on model risk management: XVA Desks - A New Era for Risk Management I. Ruiz, 2015-04-27 Written by a practitioner with years working in CVA, FVA and DVA this is a thorough, practical guide to a topic at the very core of the derivatives industry. It takes readers through all aspects of counterparty credit risk management and the business cycle of CVA, DVA and FVA, focusing on risk management, pricing considerations and implementation. |
| supervisory guidance on model risk management: Managing Uncertainty, Mitigating Risk Nick Firoozye, Fauziah Ariff, 2016-04-20 Managing Uncertainty, Mitigating Risk proposes that financial risk management broaden its approach, maintaining quantification where possible, but incorporating uncertainty. The author shows that by using broad quantification techniques, and using reason as the guiding principle, practitioners can see a more holistic and complete picture. |
| supervisory guidance on model risk management: The New International Financial System Douglas D. E. T. Al EVANOFF, 2015-10-27 Ever since the Great Recession, the global financial regulatory system has undergone significant changes. But have these changes been sufficient? Have they created a new problem of over-regulation? Is the system currently in a better position than in the pre-Recession years, or have we not adequately addressed the basic causes of the financial crisis and resulting Great Recession?These were the questions and issues addressed in the seventeenth annual international banking conference held at the Federal Reserve Bank of Chicago in November 2014. In collaboration with the Bank of England, the theme of the conference was to examine the state of the new global financial system as it has evolved in response to significant market changes and regulatory reforms triggered by the global financial crisis. The papers from that conference are collected in this volume, with contributions from an international array of government officials, regulators, industry practitioners and academics. |
| supervisory guidance on model risk management: Driverless Finance Hilary J. Allen, 2022 Introduction -- The case for precaution -- Fintech and risk management -- Fintech and capital intermediation -- Fintech and payments -- Current approaches to fintech and financial stability regulation -- Precautionary regulation of fintech innovation -- The bigger picture. |
| supervisory guidance on model risk management: Credit Intelligence & Modelling Raymond A. Anderson, 2022 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. |
| supervisory guidance on model risk management: Multiple Perspectives in Risk and Risk Management Philip Linsley, Philip Shrives, Monika Wieczorek-Kosmala, 2019-04-16 This proceedings book presents a multidisciplinary perspective on risk and risk management. Featuring selected papers presented at the European Risk Research Network (ERRN) 8th European Risk Conference “Multiple Perspectives in Risk and Risk Management” held in Katowice, Poland, it explores topics such as risk management systems, risk behaviors, risk culture, big data and risk reporting and regulation. The contributors adopt a wide variety of theoretical approaches and either qualitative or quantitative methodologies. Contemporary companies operate in a highly dynamic environment, accompanied by the constant development of the information technology, making decision-making processes highly complex and increasing the risk related to company performance. The European Risk Research Network (ERRN) was established in 2006 with the aim of stimulating cross-disciplinary research in the area of risk management. The network includes academics and industry experts from the fields of accounting, auditing, financial economics and mathematical finance. To keep the network lively and fruitful, regular “European Risk Conferences” are organized to present papers from a broad spectrum of risk and risk management areas. Featuring contributions for Italy, South Africa, Germany and Poland, this proceedings book is a valuable reference resource for students, academics, and practitioners in risk and risk management |
| supervisory guidance on model risk management: Managing Country Risk in an Age of Globalization Michel Henry Bouchet, Charles A. Fishkin, Amaury Goguel, 2018-08-04 This book provides an up-to-date guide to managing Country Risk. It tackles its various and interlinked dimensions including sovereign risk, socio-political risk, and macroeconomic risk for foreign investors, creditors, and domestic residents. It shows how they are accentuated in the global economy together with new risks such as terrorism, systemic risk, environmental risk, and the rising trend of global volatility and contagion. The book also assesses the limited usefulness of traditional yardsticks of Country Risk, such as ratings and rankings, which at best reflect the market consensus without predictive value and at worst amplify risk aversion and generate crisis contamination. This book goes further than comparing a wide range of risk management methods in that it provides operational and forward-looking warning signs of Country Risk. The combination of the authors’ academic and market-based backgrounds makes the book a useful tool for scholars, analysts, and practitioners. |
| supervisory guidance on model risk management: Intelligent Credit Scoring Naeem Siddiqi, 2017-01-10 A better development and implementation framework for credit risk scorecards Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data. Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include: Following a clear step by step framework for development, implementation, and beyond Lots of real life tips and hints on how to detect and fix data issues How to realise bigger ROI from credit scoring using internal resources Explore new trends and advances to get more out of the scorecard Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results. |
Sound Practices for Model Risk Management: Supervisory …
Apr 4, 2011 · The Office of the Comptroller of the Currency (OCC) has adopted the attached Supervisory Guidance on Model Risk Management. This guidance, developed jointly with the …
SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT
model risk management. If at some banks the use of models is less pervasive and has less impact on their financial condition, then those banks may not need as complex an approach to …
Model Risk Management - Office of the Comptroller of the …
This booklet aligns with the principles laid out in the “Supervisory Guidance on Model Risk Management” conveyed by OCC Bulletin 2011-12, “Sound Practices for Model Risk …
Comptroller's Handbook: Model Risk Management | OCC
The booklet presents the concepts and general principles of model risk management, and aligns with the principles laid out in "Supervisory Guidance on Model Risk Management" conveyed …
Model Risk Management: New Comptroller's Handbook Booklet
Aug 18, 2021 · Please contact Caroline Stuart, Governance and Operational Risk Policy Analyst, Operational Risk Division, at (202) 649-6550. Grovetta N. Gardineer Senior Deputy …
GAA 2014-1, Supervisory Guidance for Data, Modeling, and …
banking organization’s overall operational risk exposure is computed as the 99.9th percentile from this estimated distribution. 6 See . OCC Bulletin 2011-21, “Interagency Guidance on the …
Interagency Statement on Model Risk Management for Bank …
Refer to the “Supervisory Guidance on Model Risk Management,” Federal Reserve SR Letter 11-7; OCC Bulletin 2011-12; and FDIC FIL 22-2017. 2. The term “bank” is used here as in Bank …
Bank Secrecy Act/Anti-Money Laundering: Interagency Statement …
Apr 12, 2021 · The statement addresses how the risk management principles described in the “Supervisory Guidance on Model Risk Management” (referred to as the model risk …
Guidance on Advanced Approaches 2014-1: Supervisory Guidance …
Jun 30, 2014 · The Guidance on Advanced Approaches (GAA Series) addresses technical matters relating to the implementation of the advanced approaches risk-based capital rule. …
18978 Federal Register /Vol. 86, No. 68/Monday, April 12, …
in the interagency Supervisory Guidance on Model Risk Management (referred to as the ‘‘model risk management guidance,’’ or MRMG) support compliance by banks with Bank Secrecy …
Sound Practices for Model Risk Management: Supervisory …
Apr 4, 2011 · The Office of the Comptroller of the Currency (OCC) has adopted the attached Supervisory Guidance on Model Risk Management. This guidance, developed jointly with the …
SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT
model risk management. If at some banks the use of models is less pervasive and has less impact on their financial condition, then those banks may not need as complex an approach to …
Model Risk Management - Office of the Comptroller of the …
This booklet aligns with the principles laid out in the “Supervisory Guidance on Model Risk Management” conveyed by OCC Bulletin 2011-12, “Sound Practices for Model Risk …
Comptroller's Handbook: Model Risk Management | OCC
The booklet presents the concepts and general principles of model risk management, and aligns with the principles laid out in "Supervisory Guidance on Model Risk Management" conveyed …
Model Risk Management: New Comptroller's Handbook Booklet
Aug 18, 2021 · Please contact Caroline Stuart, Governance and Operational Risk Policy Analyst, Operational Risk Division, at (202) 649-6550. Grovetta N. Gardineer Senior Deputy …
GAA 2014-1, Supervisory Guidance for Data, Modeling, and …
banking organization’s overall operational risk exposure is computed as the 99.9th percentile from this estimated distribution. 6 See . OCC Bulletin 2011-21, “Interagency Guidance on the …
Interagency Statement on Model Risk Management for Bank …
Refer to the “Supervisory Guidance on Model Risk Management,” Federal Reserve SR Letter 11-7; OCC Bulletin 2011-12; and FDIC FIL 22-2017. 2. The term “bank” is used here as in Bank …
Bank Secrecy Act/Anti-Money Laundering: Interagency Statement …
Apr 12, 2021 · The statement addresses how the risk management principles described in the “Supervisory Guidance on Model Risk Management” (referred to as the model risk …
Guidance on Advanced Approaches 2014-1: Supervisory Guidance …
Jun 30, 2014 · The Guidance on Advanced Approaches (GAA Series) addresses technical matters relating to the implementation of the advanced approaches risk-based capital rule. …
18978 Federal Register /Vol. 86, No. 68/Monday, April 12, …
in the interagency Supervisory Guidance on Model Risk Management (referred to as the ‘‘model risk management guidance,’’ or MRMG) support compliance by banks with Bank Secrecy …
