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Your credit score : how to improve the 3-digit number that shapes your financial future
\"Improve your credit score, for real, with the #1 best-selling guide you can trust! Today, a good credit score is essential for getting credit, getting a job, even getting car insurance or a cellphone. Now, best selling journalist Liz Pulliam Weston has thoroughly updated her top-selling guide to credit scores, with crucial new information for protecting (or rebuilding) yours. Weston thoroughly covers brand-new laws and rules surrounding credit scoring - including some surprising good news and some frightening new risks.\" -- Publisher annotation.
Book
Debtor nation
Before the twentieth century, personal debt resided on the fringes of the American economy, the province of small-time criminals and struggling merchants. By the end of the century, however, the most profitable corporations and banks in the country lent money to millions of American debtors. How did this happen? The first book to follow the history of personal debt in modern America,Debtor Nationtraces the evolution of debt over the course of the twentieth century, following its transformation from fringe to mainstream--thanks to federal policy, financial innovation, and retail competition.
How did banks begin making personal loans to consumers during the Great Depression? Why did the government invent mortgage-backed securities? Why was all consumer credit, not just mortgages, tax deductible until 1986? Who invented the credit card? Examining the intersection of government and business in everyday life, Louis Hyman takes the reader behind the scenes of the institutions that made modern lending possible: the halls of Congress, the boardrooms of multinationals, and the back rooms of loan sharks. America's newfound indebtedness resulted not from a culture in decline, but from changes in the larger structure of American capitalism that were created, in part, by the choices of the powerful--choices that made lending money to facilitate consumption more profitable than lending to invest in expanded production.
From the origins of car financing to the creation of subprime lending,Debtor Nationpresents a nuanced history of consumer credit practices in the United States and shows how little loans became big business.
eBook
States of Credit
States of Creditprovides the first comprehensive look at the joint development of representative assemblies and public borrowing in Europe during the medieval and early modern eras. In this pioneering book, David Stasavage argues that unique advances in political representation allowed certain European states to gain early and advantageous access to credit, but the emergence of an active form of political representation itself depended on two underlying factors: compact geography and a strong mercantile presence.
Stasavage shows that active representative assemblies were more likely to be sustained in geographically small polities. These assemblies, dominated by mercantile groups that lent to governments, were in turn more likely to preserve access to credit. Given these conditions, smaller European city-states, such as Genoa and Cologne, had an advantage over larger territorial states, including France and Castile, because mercantile elites structured political institutions in order to effectively monitor public credit. While creditor oversight of public funds became an asset for city-states in need of finance, Stasavage suggests that the long-run implications were more ambiguous. City-states with the best access to credit often had the most closed and oligarchic systems of representation, hindering their ability to accept new economic innovations. This eventually transformed certain city-states from economic dynamos into rentier republics.
Exploring the links between representation and debt in medieval and early modern Europe,States of Creditcontributes to broad debates about state formation and Europe's economic rise.
eBook
Credit risk
Modelling credit risk accurately is central to the practice of mathematical finance. This volume of the Mastering Mathematical Finance series offers a comprehensive and accessible introduction to the subject tailored specially for master's students. The book focuses on the two mainstream modelling approaches to credit risk, namely structural models and reduced form models, and on pricing selected credit risk derivatives. Balancing rigorous theory with real-world examples from the post-credit crisis financial markets, it takes readers through a natural development of mathematical ideas and financial intuition. Students, practitioners and researchers alike will benefit from the compact presentation and detailed worked examples, exercises and solutions.
Book
From Wall Street to Main Street: The Impact of the Financial Crisis on Consumer Credit Supply
How did the collapse of the asset-backed securities (ABS) market during the 2007 to 2009 financial crisis affect the supply of credit to the broader economy? Using new data on the U.S. credit union industry, we find that ABS-related losses are associated with a large contraction in the supply of credit to consumers, especially among those credit unions that began the crisis with weaker capitalization. We also find that this credit supply shock restricted the availability of mortgage and automobile credit. These results show how movements in the prices of financial assets can affect the real economy.
Journal Article
REGULATING CONSUMER FINANCIAL PRODUCTS
We analyze the effectiveness of consumer financial regulation by considering the 2009 Credit Card Accountability Responsibility and Disclosure (CARD) Act. We use a panel data set covering 160 million credit card accounts and a difference-in-differences research design that compares changes in outcomes over time for consumer credit cards, which were subject to the regulations, to changes for small business credit cards, which the law did not cover. We estimate that regulatory limits on credit card fees reduced overall borrowing costs by an annualized 1.6% of average daily balances, with a decline of more than 5.3% for consumers with FICO scores below 660. We find no evidence of an offsetting increase in interest charges or a reduction in the volume of credit. Taken together, we estimate that the CARD Act saved consumers $11.9 billion a year. We also analyze a nudge that disclosed the interest savings from paying off balances in 36 months rather than making minimum payments. We detect a small increase in the share of accounts making the 36-month payment value but no evidence of a change in overall payments.
Journal Article
SHAP Stability in Credit Risk Management: A Case Study in Credit Card Default Model
The rapid growth of the consumer credit card market has introduced substantial regulatory and risk management challenges. To address these challenges, financial institutions increasingly adopt advanced machine learning models to improve default prediction and portfolio monitoring. However, the use of such models raises additional concerns regarding transparency and fairness for both institutions and regulators. In this study, we investigate the consistency of Shapley Additive Explanations (SHAPs), a widely used Explainable Artificial Intelligence (XAI) technique, through a case study on credit card probability-of-default modeling. Using the Default of Credit Card dataset containing 30,000 consumer credit accounts information, we train 100 Extreme Gradient Boosting (XGBoost) models with different random seeds to quantify the consistency of SHAP-based feature attributions. The results show that the feature SHAP stability is strongly associated with feature importance level. Features with high predictive power tend to yield consistent SHAP rankings (Kendall’s W = 0.93 for the top five features), while features with moderate contributions exhibit greater variability (Kendall’s W = 0.34 for six mid-importance features). Based on these findings, we recommend incorporating SHAP stability analysis into model validation procedures and avoiding the use of unstable features in regulatory or customer-facing explanations. We believe these recommendations can help enhance the reliability and accountability of explainable machine learning framework in credit risk management.
Journal Article