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Household debt in Thailand has become a serious problem, particularly for the elderly, because they have less ability to pay debts compared to the working population. Therefore, better understanding of the elderly's debt and delinquency is crucial in policy formulation for the aged society. Previous works have focused on the elderly's debt, either at the macro or individual levels. Little is known about geographic differences at the regional level. Knowing where the elderly debt and delinquency tend to cluster could guide area-specific policies to tackle the elderly financial stability. This research aims to examine the spatio-temporal distribution of the debt and delinquency of the near-elderly population (age 50–59) and the elderly (age 60 and above) using data from the National Credit Bureau of Thailand from 2008 to 2019. The analysis focuses on various types of loans. Spatial clusters are identified by Moran's I and Local Geary statistics. The results show that the pattern of elderly debt and delinquency generally follows the life cycle hypothesis, except for credit card and business-related loans. There exist spatial clusters and spatial differences in different types of loans, suggesting regional heterogeneities that require area-specific policy responses. [ABSTRACT FROM AUTHOR]

The basic concept of credit scoring is to assess an individual`s payment ability as well as the specific individual`s credit default risk, hence determining an individual`s creditworthiness. Based on the credit score, financial institutions, insurance companies, telecommunication companies and other businesses decide whether consumers are eligible for a mortgage, credit card, auto loan, and other credit products. However, in many countries, potential tenants and insurance applicants also use credit scores extensively for screening. Accordingly, Credit Bureaus (CB) or Consumer Reporting Agencies (CRA) exert an essential gatekeeper function for important economic areas of consumers’ everyday life. However, when examining CBs globally, there are considerable differences in the use of data to calculate credit scores. Interestingly, the influence of CBs on credit rating receives little to no attention in academic research. This is particularly evident in the absence of a framework for classifying Credit Bureaus. Therefore, 24 traditional and non-traditional Credit Bureaus operating in 17 different countries are analyzed. First, the study identifies the different data types underlying credit reports and credit scores. Second, CBs are classified and clustered according to the type of information used for credit scoring. Furthermore, promising areas of research, in particular the ethical conflict between data protection and economic participation are highlighted.

This study explores the potential of utilizing alternative data sources to enhance the accuracy of credit scoring models, compared to relying solely on traditional data sources, such as credit bureau data. A comprehensive dataset from the Home Credit Group's home loan portfolio is analysed. The research examines the impact of incorporating alternative predictors that are typically overlooked, such as an applicant's social network default status, regional economic ratings, and local population characteristics. The modelling approach applies the model-X knockoffs framework for systematic variable selection. By including these alternative data sources, the credit scoring models demonstrate improved predictive performance, achieving an area under the curve metric of 0.79360 on the Kaggle Home Credit default risk competition dataset, outperforming models that relied solely on traditional data sources, such as credit bureau data. The findings highlight the significance of leveraging diverse, non-traditional data sources to augment credit risk assessment capabilities and overall model accuracy. [ABSTRACT FROM AUTHOR]

Digital credit that uses non-traditional scoring techniques has already expanded credit access to many new populations. A point of policy debate is whether digital lenders should be fully integrated into information-sharing systems, such as those offered by credit reference bureaus (CRBs). We study an example of a digital lender in Mexico adopting credit bureau scores into its screening process. Using unique administrative data, we estimate a regression discontinuity in time around the lender's integration of credit bureau scores. We find that the acquisition of credit scores reduces defaults, with the likelihood of borrowers' repayment increasing by 10–13 per cent. [ABSTRACT FROM AUTHOR]

The Equifax data breach in 2017 provides an interesting case for analyzing effective crisis communication. Unlike other data breaches, victims who were potentially damaged in this breach were not customers of Equifax. Benoit's (1995) Image Restoration Theory (IRT) and Coomb's (2007, 2015) Situational Crisis Communication Theory (SCCT) are used to analyze effective communication strategies in print media. Critical discourse analysis method is used to evaluate quotes from Equifax' spokespersons published in The New York Times, The Wall Street Journal, and The Washington Post from September 2017 through July 2019. The choice of well-known business news media outlets and a time frame with immediate temporal proximity to the data breach serves to enhance the reliability and the completeness of the quotes from Equifax spokespersons. Select financial data from Equifax is also analyzed as tangible evidence of the success (or failure) of strategic communication responses in this crisis. Equifax lacked consistency by using deny strategies with diminish or rebuilding strategies and the company failed to use rebuilding strategies throughout the crisis period. Equifax effectively used the adjusting information strategies. Although Equifax did not perform as well as a main competitor, its stock market performance was still higher than the Standard and Poors 500 (S&P 500) market average in the years following the crisis. Keywords: Image Restoration Theory (IRT), Situational Crisis Communication Theory (SCCT), strategic communication response, Equifax, data breach, In September 2017, Equifax publicly announced a data breach within their company that would eventually end up affecting approximately 147 million consumers (Equifax Data Breach Settlement, 2020). Hackers stole Social [...]

We investigate the effect of credit information sharing on cost of debt, with particular focus on the introduction of credit bureaus in developing countries. Using a large dataset of firms from 28 developing countries over the period 2004–2019, we find that firms' average cost of debt significantly declines following the introduction of credit bureaus. This finding is robust to an alternative measure of cost of debt, several firm‐ and country‐level controls and to firm‐ and year‐fixed effects. The reduction in cost of debt is more pronounced for less transparent firms and for firms domiciled in countries with weak institutional framework. [ABSTRACT FROM AUTHOR]

The article investigates how U.S. circuit courts have undermined the protections of the Fair Credit Reporting Act (FRCA). Topics discussed include the purpose, legislative history and provisions of the FRCA, basic framework for the dispute process to correct error in consumer report, the prevalence of inaccuracies in consumer reports, and wrong decisions by circuit courts in holding that credit reporting agencies are not required to conduct a reasonable reinvestigation of a disputed claim.

Credit card payments and revolving debt are important for consumer theory but a key data source—credit bureau records—does not distinguish between current charges and revolving debt. We develop a theory‐based econometric methodology using a hidden Markov model to estimate the likelihood a consumer is revolving debt each quarter. We validate our approach using a new survey linked to credit bureau data. We estimate that for likely revolvers: (i) 100% of an increase in credit becomes an increase in debt eventually; (ii) credit limit changes are half as salient as debt changes; and (iii) revolving status is persistent. [ABSTRACT FROM AUTHOR]

A large-scale microcredit expansion program—together with a credit bureau accessible to all lenders—can enable unbanked borrowers to build a credit history, facilitating their transition to commercial banks. Loan-level data from Rwanda show the program improved access to credit and reduced poverty. A sizable share of first-time borrowers switched to commercial banks, which cream-skim less risky borrowers and grant them larger, cheaper, and longer-maturity loans. Switchers have lower default risk than nonswitchers and are not riskier than other bank borrowers. Switchers also obtain better loan terms from banks compared with first-time bank borrowers without a credit history. [ABSTRACT FROM AUTHOR]

Conventional machine learning (ML) and deep learning approaches require sharing customers' sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a significant challenge for financial companies. To address this challenge, federated learning has emerged as a promising approach to protect data privacy. However, the high communication costs associated with federated systems, particularly for large neural networks, can be a bottleneck. To mitigate this issue, it is necessary to limit the number and size of communications for practical training of large neural structures. Gradient sparsification is a technique that has gained increasing attention as a method to reduce communication costs, as it updates only significant gradients and accumulates insignificant gradients locally. However, the secure aggregation framework cannot directly employ gradient sparsification. To overcome this limitation, this article proposes two sparsification methods for reducing the communication costs of federated learning. The first method is a time-varying hierarchical sparsification method for model parameter updates, which addresses the challenge of maintaining model accuracy after a high sparsity ratio. This method can significantly reduce the cost of a single communication. The second method is to apply sparsification to the secure aggregation framework. Specifically, the encryption mask matrix is sparsified to reduce communication costs while protecting privacy. Experiments demonstrate that our method can reduce the upload communication costs to approximately 2.9% to 18.9% of the conventional federated learning algorithm under different non-IID experiment settings when the sparsity rate is 0.01. [ABSTRACT FROM AUTHOR]

Using two natural field experiments, we examine how warning individual retail borrowers that their loan performance will be reported to a public credit registry before and after the loan take-up affects their borrowing behavior. We show that credit warnings reduce default rates by 3.7 to 7 percentage points and increase loan take-up rates by 4.1 percentage points, which suggests that credit warnings benefit both lenders and borrowers. The main drivers appear to be borrowers' anticipation of a reduction in lenders' informational rents and improved repayment incentives. Moreover, the reduction in default rates is comparable for borrowers who receive the credit warning before and after the loan take-up. As credit warnings received before but not after a loan take-up can affect the borrower pool, and thus the overall credit risk of the pool, the results suggest that credit warnings have little net effect on the pool's credit risk due to selection. JEL Classifications: G10; G21; G23. [ABSTRACT FROM AUTHOR]

Relying on a large-scale experiment in Peru, this study evaluates the effects of an in-class intervention on financial literacy and financial behaviour. As soon as the programme is over, treated students record significant financial literacy gains that do not hinder their academic performance. The programme also leads to immediate changes in downstream financial behaviour as measured by financial autonomy and financial savviness. Credit bureau records gathered three years later show that early improvements in financial literacy translate into limited, but positive long-lasting changes in financial behaviour. The treatment did not affect students' credit or repayment behaviour on the extensive margin, but, among those few with outstanding loans, it reduced arrears by 20%. [ABSTRACT FROM AUTHOR]

In the article, the authors discuss the credit scoring system and its effects on the socio-economy in the U.S. Other topics include how racial, ethnic and economic disparities in credit scores affect wealth gap and traditional credit reporting and scoring, and how prior credit decisions reflect the legacy of discrimination like redlining and marketing of subprime credit to people of color.

The mercantile expeditions organized by Lima's commercial and political elite who traded with the Philippines and China during times of notable silver production in Potosí are studied from the last two decades of the sixteenth century until 1604. This analysis emphasizes the roles in this trade of the viceroys of New Spain, Perú, and the governors of the Philippine islands such as Francisco Tello de Guzmán. Case studies are undertaken of the networks built by Limenian neighbors Juan de Segura and Diego Núñez de Campoverde. This paper ends with an analysis of the merchandise coming from the East destined for Lima, Potosí, Sucre, and La Paz. Research for this article has been conducted in the General Archive of the Indies (Sevilla, Spain), the Peruvian General Archive, México City's General Notary Archive, and the Bolivia's Nationals Archive and Library. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze how Czech car manufacturers have coped with the COVID-19 pandemic. Included are data on manufacturing companies taken from the Cribis database of the CRIF - Czech Credit Bureau a.s., specifically data on ŠKODA AUTO a.s., Hyundai Motor Manufacturing Czech s.r.o. and Toyota Motor Manufacturing Czech Republic s.r.o. Subsequently, the research comprises economic results, ROS (return on sales) and indebtedness calculated in relation to the aforementioned companies for the period of 2019-2020. The results show that the car manufacturers had greater losses during the COVID-19 pandemic than the year before. Additionally, they became more indebted in 2020 and had lower return on sales than in 2019. [ABSTRACT FROM AUTHOR]

The aim of this study was to ascertain what can be done by the informal finance sector to close the credit gap in order to improve access to finance by SMEs. SMEs are the backbone of many economies as a result of generating employment and improving GDP. Despite playing such a major role in African economies, SMEs have been excluded from the financial systems. The informal finance sector plays a vital role by providing finance to small businesses. The study employed a literature survey with a primary focus on empirical studies that have been conducted in the African context. The study found that, generally, there are two circumstances under which most small businesses depend on informal finance. Firstly, informal finance is used as a last resort by SMEs that fail to access credit from the formal finance sector, owing to, among other issues, information asymmetry, lack of collateral security and perceived high default rates. Further, low financial literacy and the absence of credit bureaus in developing countries also contribute to the failure to access finance from formal institutions. Secondly, some entrepreneurs opt for informal finance even if they are eligible for formal finance as a result of its flexibility, convenience and simple administrative procedures. Notwithstanding the above benefits of informal finance, informal lenders are regarded as exploiting the clients by charging high interest rates. In addition, this sector suffers from limited resources; hence, it fails to fully service SMEs that require larger funding and are not eligible for formal finance. Invariably, all the studies that have been carried out confirm that access to finance is a major obstacle to the growth and development of SMEs. The development and empowerment of SMEs cannot be ignored as an important driver of the developmental agenda of most economies globally. The main policy recommendations that flow from this study, based on the policy syndrome of improving access to finance (financial inclusion) by the SME sector, include (1) the establishment of a suitable regulatory framework which will nurture the informal finance sector while promoting consumer protection, and (2) linking the formal and informal sector. On the other hand, SMEs should improve their risk management practices and also embrace FinTech platforms in order to access credit. [ABSTRACT FROM AUTHOR]

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Copyright of OHM: Obradoiro de Historia Moderna is the property of Universidad de Santiago de Compostela, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)