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Assets and Liabilities management of financial institution is a complex matter. Following the crisis, regulators are more demanding and banks must put in place the best practices. This book presents the fundamentals for modeling accurately a commercial bank and managing its balance sheet.
March 1998 Differences in interest margins reflect differences in bank characteristics, macroeconomic conditions, existing financial structure and taxation, regulation, and other institutional factors. Using bank data for 80 countries for 1988-95, Demirgüç-Kunt and Huizinga show that differences in interest margins and bank profitability reflect various determinants: * Bank characteristics. * Macroeconomic conditions. * Explicit and implicit bank taxes. * Regulation of deposit insurance. * General financial structure. * Several underlying legal and institutional indicators. Controlling for differences in bank activity, leverage, and the macroeconomic environment, they find (among other things) that: * Banks in countries with a more competitive banking sector-where banking assets constitute a larger share of GDP-have smaller margins and are less profitable. The bank concentration ratio also affects bank profitability; larger banks tend to have higher margins. * Well-capitalized banks have higher net interest margins and are more profitable. This is consistent with the fact that banks with higher capital ratios have a lower cost of funding because of lower prospective bankruptcy costs. * Differences in a bank's activity mix affect spread and profitability. Banks with relatively high noninterest-earning assets are less profitable. Also, banks that rely largely on deposits for their funding are less profitable, as deposits require more branching and other expenses. Similarly, variations in overhead and other operating costs are reflected in variations in bank interest margins, as banks pass their operating costs (including the corporate tax burden) on to their depositors and lenders. * In developing countries foreign banks have greater margins and profits than domestic banks. In industrial countries, the opposite is true. * Macroeconomic factors also explain variation in interest margins. Inflation is associated with higher realized interest margins and greater profitability. Inflation brings higher costs-more transactions and generally more extensive branch networks-and also more income from bank float. Bank income increases more with inflation than bank costs do. * There is evidence that the corporate tax burden is fully passed on to bank customers in poor and rich countries alike. * Legal and institutional differences matter. Indicators of better contract enforcement, efficiency in the legal system, and lack of corruption are associated with lower realized interest margins and lower profitability. This paper-a product of the Development Research Group-is part of a larger effort in the group to study bank efficiency.
Is the fall in overall productivity growth in the United States and other developed countries related to the rising share of the service sectors in the economy? Since services represent well over half of the U.S. gross national product, it is also important to ask whether these sectors have had a slow rate of growth, as this would act as a major drag on the productivity growth of the overall economy and on its competitive performance. In this timely volume, leading experts from government and academia argue that faulty statistics have prevented a clear understanding of these issues.
For junior-senior/MBA-level courses in Commercial Banking, Commercial Bank Management, Management of Financial Institutions, Financial Institutions and Markets. Established as the market-leader for more than 12 years, this thoroughly revised text describes both the theory and practice of commercial banking from a financial-management perspective. Focusing on the dynamic and rapidly changing financial-services industry, it explores modern financial management decision-making and highlights the importance of adapting to change and creating value as the way for firms to succeed.
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.
In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, "In a world focused on using AI in new ways, we're focused on using it wisely and responsibly."
The objective of Off-Balance Sheet Activities is to gain insights into, and propose meaningful solutions to, those issues raised by the current proliferation of off-balance sheet transactions. The book has its origins in a New York University conference that focused on this topic. Jointly undertaken by the Vincent C. Ross Institute of Accounting Research and New York University's Salomon Center for the study of Financial Institutions at the Stern School of Business, the conference brought together academic researchers and practitioners in the field of accounting and finance to address the issues with the broad-mindedness requisite of a group whose approaches to solutions are as different from each other as their respectively theoretical and applied approaches to the disciplines of finance and accounting. The essays are divided into two sections. The first covers issues surrounding OBS activities and banking and begins with a brief introduction that places the essays into context. OBS activities and the underinvestment problem, whether loan sales are really OBS, and money demand and OBS liquidity are examined in detail. Section two, which also begins with a brief introduction, focuses on issues of securitized assets and financing. A report on recognition and measurement issues in accounting for securitized assets is followed by three separate discussion essays. Other subjects covered include contract theoretic analysis of OBS financing, the use of OBS financing to circumvent financial covenant restrictions, and debt contracting and financial contracting. The latter two contributions are also followed by discussion essays. This unique collection of papers will prove to be an interesting and valuable tool for accounting and finance professionals as well as for academics involved in these fields. It will also be an important addition to public, college, and university libraries.
The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.