Bank Lending : Principles and Practice by
This book is for students and professionals working, or intending to work, in a lending-related role. The text provides comprehensive, up-to-date coverage of bank lending, its principles and practices. Bank lending performs a key role within global and national economies. Individuals and enterprises look primarily to banks and other financial institutions to finance their personal and business requirements. Good lending practice is therefore a core skill required within the financial services industry. This book will give lending staff the detailed knowledge and understanding of the financial and legal aspects of their roles they need to be able to fulfil employer as well as customer expectations. Topics include: lending principles the legal and regulatory framework types of borrower purposes of financing collateral security the lending cycle Islamic finance impact of lending and social responsibility The book provides students and practitioners of bank lending with an excellent understanding of lending practices as well as the principles that underpin these practices.
Broken Bargain by
A history of major financial crises--and how taxpayers have been left with the bill In the 1930s, battered and humbled by the Great Depression, the U.S. financial sector struck a grand bargain with the federal government. Bankers gained a safety net in exchange for certain curbs on their freedom: transparency rules, record-keeping and antifraud measures, and fiduciary responsibilities. Despite subsequent periodic changes in these regulations, the underlying bargain played a major role in preserving the stability of the financial markets as well as the larger economy. By the free-market era of the 1980s and 90s, however, Wall Street argued that rules embodied in New Deal-era regulations to protect consumers and ultimately taxpayers were no longer needed--and government agreed. This engaging history documents the country's financial crises, focusing on those of the 1920s, the 1980s, and the 2000s, and reveals how the two more recent crises arose from the neglect of this fundamental bargain, and how taxpayers have been left with the bill.
Machine Learning for Finance by
A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine learning and how to put them to work in financial industries Clear explanation and expert discussion of how machine learning works, with an emphasis on financial applications Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learning Book Description Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas' experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learn Apply machine learning to structured data, natural language, photographs, and written text How machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Dig deep into neural networks, examine uses of GANs and reinforcement learning Debug machine learning applications and prepare them for launch Address bias and privacy concerns in machine learning Who this book is for This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.
The Oxford Handbook of Banking by
(EBSCO eBook) The Oxford Handbook of Banking provides an overview and analysis of state-of-the-art research in banking written by leading researchers in the field.
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