Can Facing the Truth Improve Outcomes? Effects of Information in Consumer Finance

2021 ◽  
Author(s):  
Jessica Fong ◽  
Megan Hunter
Keyword(s):  
Consumer Finance ◽  
Credit Score ◽  
Credit Scores

This article explores when consumers avoid learning information about their credit scores and how viewing one’s credit score impacts future credit scores.

scholarly journals Using Credit Scores to Predict and Analyze Areas of Gentrification and Urban Development in Toronto, Canada - a Geo-Spatial Approach

2021 ◽  
Author(s):  
Nicholas Pulsone ◽  
Brian Ceh
Keyword(s):  
Spatial Analysis ◽  
Urban Development ◽  
Well Being ◽  
Development Zone ◽  
Credit Score ◽  
Spatial Approach ◽  
Credit Scores ◽  
The City ◽  
Analytical Frameworks ◽  
Regent Park

This study examines the use of financial well-being indicators such as credit scores to identify gentrification. This study is a response to the redevelopment of neighbourhoods in the City of Toronto through gentrification. This study also explores both theoretical and analytical frameworks outlined in literature to identify correlations between financial wellbeing indicators and gentrification. Comparing the observations in this study to areas experience gentrification such as Regent Park revealed large implications that gentrification is largely associated with financial wellbeing. The study also found that the average credit scores in the City of Toronto seem to be increasing. The analysis determined that the credit score changes reflected the development in the Regent Park development zone. Key words: Gentrification, credit scores, spatial analysis, urban development

Consumer marketplace lending in Australia: Credit scores and loan funding success

2019 ◽  
Vol 45 (4) ◽  
pp. 607-623
Author(s):  
Andrew Grant ◽  
Luke Deer
Keyword(s):  
Decision Tree ◽  
Decision Process ◽  
Jel Classification ◽  
Third Party ◽  
Credit Score ◽  
Second Stage ◽  
Funding Success ◽  
Credit Scores ◽  
Unique Dataset

This article examines borrower acceptance in consumer marketplace lending using a unique dataset from the largest platform in Australia, Society One. Applications are initially filtered through an automated decision tree based on a third-party Veda (Equifax) credit score. At the second stage of assessment, loan applications are underwritten by the platform before being offered to sophisticated investors for purchase. The platform accepts around 11% of completed applications, with around 55% declined by an automated decision process and the remaining 34% by the manual underwriting process. More than 80% of purchased loans were made to borrowers with credit scores classed as ‘Good’, ‘Very Good’ or ‘Excellent’ (the threshold for ‘Good’ being a score of 622). However, underwriters decline around two-thirds of these higher credit score applicants, showing the importance of the underwriting process to the platform’s growth. JEL Classification: G21, G23, D14, D45, D82

scholarly journals Determining the Credit Score and Credit Rating of Firms using the Combination of KMV-Merton Model and Financial Ratios

2021 ◽  
Vol 6 (3) ◽  
pp. 105-112
Author(s):  
Norliza Muhamad Yusof ◽  
Iman Qamalia Alias ◽  
Ainee Jahirah Md Kassim ◽  
Farah Liyana Natasha Mohd Zaidi
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Credit Scoring ◽  
Financial Ratios ◽  
Credit Score ◽  
Merton Model ◽  
Medium Level ◽  
Credit Scores ◽  
Risk Managers

Credit risk management has become a must in this era due to the increase in the number of businesses defaulting. Building upon the legacy of Kealhofer, McQuown, and Vasicek (KMV), a mathematical model is introduced based on Merton model called KMV-Merton model to predict the credit risk of firms. The KMV-Merton model is commonly used in previous default studies but is said to be lacking in necessary detail. Hence, this study aims to combine the KMV-Merton model with the financial ratios to determine the firms’ credit scores and ratings. Based on the sample data of four firms, the KMV-Merton model is used to estimate the default probabilities. The data is also used to estimate the firms’ liquidity, solvency, indebtedness, return on asset (ROA), and interest coverage. According to the weightages established in this analysis, scores were assigned based on those estimates to calculate the total credit score. The firms were then given a rating based on their respective credit score. The credit ratings are compared to the real credit ratings rated by Malaysian Rating Corporation Berhad (MARC). According to the comparison, three of the four companies have credit scores that are comparable to MARC’s. Two A-rated firms and one D-rated firm have the same ratings. The other receives a C instead of a B. This shows that the credit scoring technique used can grade the low and the high credit risk firms, but not strictly for a firm with a medium level of credit risk. Although research on credit scoring have been done previously, the combination of KMV-Merton model and financial ratios in one credit scoring model based on the calculated weightages gives new branch to the current studies. In practice, this study aids risk managers, bankers, and investors in making wise decisions through a smooth and persuasive process of monitoring firms’ credit risk.

scholarly journals Financial Education versus Costly Counseling: How to Dissuade Borrowers from Choosing Risky Mortgages?

2020 ◽  
Vol 12 (1) ◽  
pp. 1-32
Author(s):  
Sumit Agarwal ◽  
Gene Amromin ◽  
Itzhak Ben-David ◽  
Souphala Chomsisengphet ◽  
Douglas D. Evanoff
Keyword(s):  
High Risk ◽  
Consumer Choice ◽  
Financial Education ◽  
Third Party ◽  
Credit Score ◽  
Contract Choice ◽  
Credit Scores

This paper explores the effects of mandatory third-party review of mortgage contracts on consumer choice. The study is based on a legislative pilot carried out in Illinois in 2006, under which mortgage counseling was triggered by applicant credit scores or by their choice of “risky mortgages.” Low-credit score applicants for whom counselor review was mandatory did not materially alter their contract choice. Conversely, higher credit score applicants who could avoid counseling by choosing nonrisky mortgages did so, decreasing their propensity for high-risk contracts between 10 and 40 percent. In the event, one of the key goals of the legislation—curtailment of high-risk mortgage products—was only achieved among the population that was not counseled. (JEL D14, D18, G21, R21)

scholarly journals Using Credit Scores to Predict and Analyze Areas of Gentrification and Urban Development in Toronto, Canada - a Geo-Spatial Approach

2021 ◽  
Author(s):  
Nicholas Pulsone ◽  
Brian Ceh
Keyword(s):  
Spatial Analysis ◽  
Urban Development ◽  
Well Being ◽  
Development Zone ◽  
Credit Score ◽  
Spatial Approach ◽  
Credit Scores ◽  
The City ◽  
Analytical Frameworks ◽  
Regent Park

This study examines the use of financial well-being indicators such as credit scores to identify gentrification. This study is a response to the redevelopment of neighbourhoods in the City of Toronto through gentrification. This study also explores both theoretical and analytical frameworks outlined in literature to identify correlations between financial wellbeing indicators and gentrification. Comparing the observations in this study to areas experience gentrification such as Regent Park revealed large implications that gentrification is largely associated with financial wellbeing. The study also found that the average credit scores in the City of Toronto seem to be increasing. The analysis determined that the credit score changes reflected the development in the Regent Park development zone. Key words: Gentrification, credit scores, spatial analysis, urban development

Decentralising Personal Credit Score

2022 ◽  
pp. 31-42
Author(s):  
Imbert Theadore ◽  
Paul Jek Sitoh
Keyword(s):  
Credit Score ◽  
Personal Credit ◽  
On Demand ◽  
Current Process ◽  
Credit Scores

The current process of securing a loan involves a cumbersome know-your-customer (KYC) process. The process also raises a question about the ownership of credit scores. In this chapter, the authors propose a solution based on a combination of decentralized identifier (DID) W3C blockchain and cryptographic wallet to make it possible to make credit scores possible. A decentralized identifier to enable a loan applicant to assert who he/she is without relying on a centralized identity issuer is key to enabling loan applicants to own his/her own credit score. The use of blockchain is to enable loan applicants to have his/her identity recorded immutably on a store that is trusted by all parties. Finally, the use of a cryptographic wallet is to enable loan applicants to assert identities on demand and prove his/her assertion.

scholarly journals Consumer credit, chronic disease and risk behaviours

2018 ◽  
Vol 73 (1) ◽  
pp. 73-78
Author(s):  
Lorraine T Dean ◽  
Emily A Knapp ◽  
Sevly Snguon ◽  
Yusuf Ransome ◽  
Dima M Qato ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Chronic Diseases ◽  
Salt Intake ◽  
Disease Risk ◽  
Consumer Credit ◽  
Health Behaviours ◽  
Credit Score ◽  
Increased Risk ◽  
Credit Scores ◽  
Zip Code

BackgroundCredit scores have been identified as a marker of disease burden. This study investigated credit scores’ association with chronic diseases and health behaviours that are associated with chronic diseases.MethodsThis cross-sectional analysis included data on 2083 residents of Philadelphia, Pennsylvania, USA in 2015. Nine-digit ZIP code level FICO credit scores were appended to individual self-reported chronic diseases (obesity, diabetes, hypertension) and related health behaviours (smoking, exercise, and salt intake and medication adherence among those with hypertension). Models adjusted for individual-level and area-level demographics and retail pharmacy accessibility.ResultsMedian ZIP code credit score was 665 (SD=58). In adjusted models, each 50-point increase in ZIP code credit score was significantly associated with: 8% lower chronic disease risk; 6% lower overweight/obesity risk, 19% lower diabetes risk; 9% lower hypertension risk and 14% lower smoking risk. Other health behaviours were not significantly associated. Compared with high prime credit, subprime credit score was significantly associated with a 15%–70% increased risk of chronic disease, following a dose–response pattern with a prime rating.ConclusionLower area level credit scores may be associated with greater chronic disease prevalence but not necessarily with related health behaviours. Area-level consumer credit may make a novel contribution to identifying chronic disease patterns.

Does the Age at Which a Consumer Gets Their First Credit Matter? Credit Bureau Entry Age and First Credit Type Effects on Credit Score

FEDS Notes ◽  
2021 ◽  
Vol 2021 (2918) ◽  
Author(s):  
Lucas Nathe ◽  
Keyword(s):  
Consumer Credit ◽  
Credit Market ◽  
Key Factors ◽  
Access To Credit ◽  
Credit Score ◽  
Credit Scores ◽  
Credit Bureau ◽  
Entry Age

The consumer credit market plays a prominent role in the financial life of U.S. households. Consumers' credit histories and, in particular their credit scores, are key factors that determine their access to credit and the price at which they borrow.

scholarly journals Credit Rating Model of Small Enterprises Based on Optimal Discriminant Ability and Its Empirical Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhanjiang Li ◽  
Lin Guo
Keyword(s):  
Loss Rate ◽  
Credit Rating ◽  
Recursive Algorithm ◽  
Credit Ratings ◽  
Small Enterprise ◽  
Small Enterprises ◽  
Credit Score ◽  
Discriminant Ability ◽  
Credit Scores ◽  
Rating Model

As an important part of the national economy, small enterprises are now facing the problem of financing difficulties, so a scientific and reasonable credit rating method for small enterprises is very important. This paper proposes a credit rating model of small enterprises based on optimal discriminant ability; the credit score gap of small enterprises within the same credit rating is the smallest, and the credit score gap of small enterprises between different credit ratings is the largest, which is the dividing principle of credit rating of small enterprises based on the optimal discriminant ability. Based on this principle, a nonlinear optimization model for credit rating division of small enterprises is built, and the approximate solution of the model is solved by a recursive algorithm with strong reproducibility and clear structure. The small enterprise credit rating division not only satisfies the principle that the higher the credit grade, the lower the default loss rate, but also satisfies the principle that the credit group of small enterprises matches the credit grade, with credit data of 3111 small enterprises from a commercial bank for empirical analysis. The innovation of this study is the maximum ratio of the sum of the dispersions of credit scores between different credit ratings and the sum of the dispersions of credit scores within the same credit rating as the objective function, as well as the default loss rate of the next credit grade strictly larger than the default loss rate of the previous credit grade as the inequality constraint; a nonlinear credit rating optimal partition model is constructed. It ensures that the small enterprises with small credit score gap are of the same credit grade, while the small enterprises with large credit score gap are of different credit grades, overcoming the disadvantages of the existing research that only considers the small enterprises with large credit score gap and ignores the small enterprises with small credit score gap. The empirical results show that the credit rating of small enterprises in this study not only matches the reasonable default loss rate but also matches the credit status of small enterprises. The test and comparative analysis with the existing research based on customer number distribution, K-means clustering, and default pyramid division show that the credit rating model in this study is reasonable and the distribution of credit score interval is more stable.

scholarly journals Credit Risk Management of Consumer Finance Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Huibo Wang
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Credit Risk ◽  
Consumer Finance ◽  
Credit Risk Management ◽  
Healthy Development ◽  
Risk Management Process ◽  
Core Concepts ◽  
Credit Scores ◽  
Definition Of

In recent years, China’s consumer finance has developed rapidly, but the foundation is unstable, and the industry has serious problems of violent competition, excessive credit, and fraud. Therefore, we should attach great importance to the healthy development of consumer finance, especially the management of its credit risk. The application of big data credit investigation can provide early warning of potential risks and prevent the risk of excessive credit investigation. This paper starts with the definition of basic core concepts, such as traditional credit investigation, big data credit investigation, and consumer finance, analyzes the performance and causes of consumer finance credit risk, and combs in detail the relevant theories of the application of big data credit investigation in consumer finance credit risk management. The application of big data credit investigation has optimized the risk management process of consumer financial institutions, deepened the concept of Internet consumer finance, improved the risk management system, created a diversified credit information system, and strengthened the innovation of Internet consumer finance products and services. For example, credit scores provide the most intuitive quantification of consumer credit risk. For consumers with different levels of credit scores, different credit approval processes can be matched. For customers with high scores, the work process can be simplified without affecting the work results. It can reduce the workload of employees by 20% and increase the accuracy of customer credit risk prediction by 16%.

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