Credit Scores Research Articles

A method for reducing credit scores’ sensitivity to economic conditions

La ciclicidad de los requisitos de capital de riesgo de crédito ha sido motivo de preocupación para los reguladores bancarios, los supervisores y la industria durante años. La sensibilidad a las condiciones económicas de los grados de probabilidad de impago (PD, por sus siglas en inglés) a los que se asignan las exposiciones crediticias es con frecuencia una de las fuentes más relevantes de dicha ciclicidad. Además, a menudo se supone que un método de asignación a grados con una elevada capacidad de diferenciación conduce inherentemente a una alta sensibilidad a las condiciones económicas. Con el fin de cuestionar esta hipótesis y promover análisis adicionales, pero sin intención de establecer ninguna expectativa o recomendación para las entidades financieras, este artículo explora una metodología destinada a limitar la sensibilidad a las condiciones económicas de un método de calificación preexistente, manteniendo al mismo tiempo su capacidad de diferenciación, mediante la inclusión de un módulo adicional en él. Este módulo resta una cantidad que refleja el efecto estimado de las condiciones económicas. Esto permite que las calificaciones originales y ajustadas coexistan y se utilicen para diferentes propósitos. Después de probar que la metodología funciona en un conjunto de datos sintéticos, su efectividad se confirma en un conjunto de datos reales obtenido de fuentes internas del Banco de España. Los resultados indican una reducción significativa en la variabilidad de la PD y las ponderaciones de riesgo al comparar una calibración de la PD de la calificación original con una calibración de la PD de la calificación ajustada.

PREDICTIVE MODELING FOR LOAN APPROVAL: A MACHINE LEARNING APPROACH

Exploring machine learning approaches to enhance the effectiveness and precision of procedures related to bank loan approval. This investigation encompasses various methods such as logistic regression, decision trees, linear regression, as well as GaussianNB, Random Forest, and SVM. Utilizing a substantial dataset containing past loan applications and diverse applicant attributes like demographics, credit scores, income levels, and employment histories. The research endeavors to evaluate the recall, accuracy, precision, and F1-score metrics of various algorithms. Additionally, it investigates the interpretability and transparency of machine learning models to offer further insight into the variables affecting decisions on loan acceptance. The study emphasizes the efficacy of logistic regression, which outperformed SVM (77%), GaussianNB (78%), random forests (78%), and decision trees (69%), achieving the highest accuracy of 80% in loan approval. By implementing this model, we can enhance ML-driven loan approval processes within the banking industry, thereby elevating decision-making standards and enhancing consumer satisfaction. KEYWORDS— Machine Learning Algorithms, Loan Approval, LogisticRegression, DecisionTree, Linear Regression, GaussianNB, RandomForest, SupportVectorMachine (SVM), Decision-Making,

Leveraging Machine Learning Techniques For Developing Robust Credit Scores For Peer-To-Peer Lending Platforms

Leveraging Machine Learning Techniques For Developing Robust Credit Scores For Peer-To-Peer Lending Platforms

Assessing the Relative Progressivity of the Biden Administration's Federal Student Loan Forgiveness Proposal

Abstract We quantify the total stock of balances eligible for the Biden Administration's 2022 student loan forgiveness proposal and examine which groups would have benefited most. Up to $442 billion in loans were eligible. Those who would have benefited most were younger, had lower credit scores, and lived in lower- and middle-income neighborhoods. We also find that Black and Hispanic borrowers would have disproportionately benefited from the proposal. We then compare the distribution of beneficiaries for the proposed policy to several alternative hypothetical forgiveness proposals and three existing tax credits. The additional forgiveness for Pell Grant recipients increased the progressivity of the policy at a cost of $129 billion. Reducing the income eligibility criterion in half from the proposal would have reduced the cost by nearly $100 billion and made the policy more progressive. Compared with existing tax credits, the announced forgiveness policy is less progressive than the Earned Income Tax Credit but more progressive than the 2019 Child Tax Credit and higher education tax credits. We conclude with a discussion of how each policy lever affects the progressivity of loan cancellation to help inform future policy.

A STUDY ON FACTORS AFFECTING THE HOME LOAN FINANCING IN SBI BANK

This study aims to identify and analyze the key factors affecting home loan financing in State Bank of India (SBI). By examining the loan application process and the decision-making criteria within the bank, the research seeks to provide insight into the elements that contribute to successful home loan approvals and rejections. Data were collected through a combination of interviews with SBI bank officers, a survey of home loan applicants, and analysis of loan approval records. Factors such as creditworthiness, income stability, loan-to-value ratio, property type, and customer relationship with the bank were examined. The findings suggest that while traditional criteria such as credit score and income remain important, other factors like customer loyalty and the bank's risk appetite play a significant role in determining home loan approval. The study concludes with recommendations for both the bank and future home loan applicants to optimize the loan financing process, including the development of a more nuanced risk assessment model and enhanced customer education initiatives.

Racial disparities in debt collection

This paper documents that Black and Hispanic borrowers are 52% more likely to experience a debt collection judgment compared to their counterparts, controlling for socioeconomic variables including income, credit scores, delinquent debt balances, and other relevant credit characteristics. Statistical discrimination mechanisms such as spatially targeting collection efforts based on the likelihood that an attorney represents the defendant or that the defendant will contest the debt in court do not explain the racial gap in judgments. We find support that the judgment gap is partially driven by taste-based discrimination, as evidenced by minorities having less 90-day past due debt balances than non-minority borrowers when controlling for model covariates. Furthermore, the disparity primarily affects non-delinquent borrowers, indicating higher levels of creditor discretion in initiating judgment proceedings. A back-of-the-envelope calculation suggests that the racial wealth gap explains at most 48% of the racial disparity in judgments.

Personal Tax Changes and Financial Well-being: Evidence from the Tax Cuts and Jobs Act

We estimate the effects of personal income tax decreases on financial well-being, including qualitative subjective assessments and quantitative measures. A plausibly causal design shows that tax decreases in the Tax Cuts and Jobs Act made survey respondents more likely to say they were "living comfortably" financially, with null effects at lower levels of subjective financial well-being. Estimates from a similar design using credit bureau data show that people who had larger tax decreases were modestly more likely to open new accounts, and more likely to have higher consumer credit balances. Tax decreases had effects on credit scores that are indistinguishable from zero. Results suggest that larger tax decreases improve financial well-being in ways not fully proxied by typical administrative data.

Decentralized credit scoring: Black box 3.0

AbstractMuch like traditional credit scoring, decentralized credit scoring calculates a borrower's creditworthiness, but the fully automated process is executed on the blockchain by Decentralized Finance (DeFi) platforms. Originally, DeFi emerged as an alternative to the centralized traditional finance (TradFi) system; however, decentralized credit scoring combines DeFi data and traditional data that include a wide range of information sources, from traditional credit reports to social media information. Despite their fairness‐oriented narrative, an examination of the business models of the protocols and entities operating in this space reveals that these hybrid scores are subject to the same algorithmic distortions that have been observed in traditional and alternative credit scoring models. Moreover, decentralized credit scores present their own distinctive set of fairness issues. Particularly, both upgrade to smart contracts and their reliance on external algorithms, known as oracles, which feed outside data, introduce heightened potential for error and bias in the credit scoring process. These “black box 3.0” issues can result in opaque automation of biased processes and perpetuate social injustices, requiring regulatory intervention to strengthen the linkage points between DeFi and TradFi and better protect consumers from the black box 3.0 consequences of decentralized credit scores.

FICO Score

This case teaches students the importance of maintaining a strong FICO score by illustrating the consequences of paying bills late or not at all. The protagonist is David Molina, a waiter at a struggling Italian restaurant located down the block from where he lives. Money is tight for Molina right now—his limited income means he lives paycheck to paycheck. However, Molina knows things will be looking up for him soon because he recently accepted a job as a bank teller across town—his first desk job.Molina has been putting off paying two of his bills: a cable bill and his Bank of America credit card bill, both of which are late and have been issued, this time, in the form of threats to impact Molina's credit score if he doesn't pay them. He has just enough money to afford the minimum payments on each overdue bill. But then he receives a phone call from his friend, Jim Lindsey, reminding him about an invitation to go to Myrtle Beach for the upcoming weekend. Molina knows he cannot afford it, but a woman he's attracted to, Jessica, will be there too. Should Molina put off the bills yet again, and if so, how exactly will being late on them hurt his credit score?

Analisis Toxic Behavior Dalam Komunikasi Interpersonal Pada Pemain Game Online Mobile Legends: Bang-Bang

Mobile Legends Online Game: Bang Bang (MLBB) is an online game with the MOBA (Multiplayer Online Batlle Arena) genre. By presenting a competitive 5 vs 5 match so that this game is loved by many gamers. The online game Mobile Legends: Bang – Bang has chat and voice features to communicate with other players. With various easy to access communication features such as Chat and Voice, this is finally misused for several reasons. Mobile Legends is a team game, so cooperation and coordination between teammates is needed to achieve the goal of this game, which is to destroy the last tower of the enemy. The Chat and Voice features in Mobile Legends are used by players to coordinate with each other. This feature is not only useful as a means of coordination, but also has the potential to be a toxic tool, especially with the intensity of playing hours that are quite high by the players. Although in reality when playing the game Mobile Legends: Bang-Bang directly or face to face, there are also those who behave toxicly with their fellow teams. This research begins with the formulation of the problem of how toxic behavior in interpersonal communication in online game players Mobile Legends: Bang-Bang. The method in this study uses a qualitative approach with research informants totaling 10 players from each epic rank or tier in the Mobile Legends game: Bang-Bang. In the results of this study, there is an interaction between players in the online game Mobile Legends: Bang-Bang both virtual and in person. For virtual communication, the toxic perpetrator conveys a message to the toxic victim and this victim has the right to reply to the message or leave the message and both the victim and the toxic perpetrator have the same position to send and receive messages. As a result, toxic behavior occurs, such as retaliating with toxic cursing behavior, doing Trolls and Feeders, and even AFK. Toxic behavior in the Mobile Legends game is also included in the strategy to get victory by breaking the opponent's concentration to focus on chat. Meanwhile, toxic to teammates has the aim of motivating them to play games to be better than before and as entertainment motivation. While direct communication or face to face, toxic perpetrators convey messages to toxic victims who are friends verbally. Even though he received unpleasant treatment, the informant continued to play with his toxic friend, who valued friendship more than games. This is where the role of the toxic help center provided by Mobile Legends: Bang-Bang. Everyone who reports toxic actions will have their identity protected and those who commit toxic behavior will receive a penalty in the form of a reduced credit score according to what was done in the game

Loan Approval Prediction System Using Machina Learning

With the growth of the banking sector, the identification of reliable borrowers that must maintain the natural core income and asset accumulation becomes a key issue. Despite all security measures, reliability of customers remains an unclear question. To tackle this barrier, banking management which is directed towards customer loan repayment consistency is required. Credit approval is a significant aspect of economy since it determines the allocation of credit-linked funds. Today, machine learning is known for its power to automate and scale up the processing of application for loans. This project will begin with data collection which will consist of data on historical (regarding the past) loan applications and the borrower profiles. The dataset has features of the credit score, income, previous work experience, debt-to-income ratio, and loan repayment record. This way, the models learn through the strengths to find good features and the reasons for accepting a loan. They are the experts in these areas and can forecast the potential patterns and connections of the data. Within the scope of this work, the supervised algorithms used are logistic regression, decision trees, random forests, and support vector machines. These algorithms are applied to the dataset available to often produce results like binary classification and regression. The adoption of machine learning among financial institutions is intended for a faster processing of loans which is their benefit. What is credit scoring, it is a tool which automate manual loan application review thereby increases efficiency. The machine-learning algorithms that analyse applications for loans could cut down on the possibilities of human biases and mistakes which are an inherent part of the process. Also, ML uses the model to recognize borrowers who may default and subsequently lower the likelihood of default. Part of the task involve utilizing historical credit market data and implementing ML algorithms to develop a highly accurate and reliable loan approving system based on trained-data, random forests, the stream of loans and reliable clients.

Decoding CIBIL: Understanding the Structure and Strategies for Improvement

This research paper delves into the intricate dynamics of credit scoring systems, particularly focusing on understanding the structure of CIBIL scores and delineating strategies for improving creditworthiness. Drawing upon theoretical frameworks from financial economics and regulatory economics, the study elucidates key factors influencing credit scores, including payment history, credit utilization, credit type, and duration. Through a mixed-methods approach encompassing secondary data analysis from the official websites of TransUnion CIBIL India and Bajaj Finance Limited, as well as qualitative exploration through thematic analysis, the research uncovers actionable insights for individuals seeking to enhance their credit profiles.By bridging theoretical insights with empirical findings, this study contributes to the literature on credit management and equips individuals with knowledge essential for navigating the complexities of credit assessment and improving financial resilience

Housing, debt, and credit score classification situations: what the unique ‘situation’ of mortgage prisoners reveals about contemporary class inequality

ABSTRACT The expansion of debt-leveraged homeownership in Anglo-capitalist economies – including the US, UK, and Australia – has created powerful stratifying effects, which prevailing critical Marxist and asset-based stratification accounts assert are reconfiguring class inequalities. While the Marxist account contends that altered class relations are centred on bifurcations between creditors and debtors, the asset-based one constructs typologies of differing relationships to asset ownership. This paper challenges this for insufficiently acknowledging the role of classificatory credit scoring systems within credit markets in unequally shaping access to debt and subsequently, housing; and the interest-bearing consequences. Drawing on a qualitative case study of 28 interviews with ‘mortgage prisoners’, existing borrowers unable to remortgage even if up to date with payments, and proceeding in close conversation with Fourcade and Healy's notion of ‘classification situations’, I examine how mortgage prisoners’ credit score situation impacts their capacity to (re)access debt-leveraged housing. The results demonstrate: mortgage prisoners’ credit score situation dominates their experience of homeownership; many are hyperaware of their credit score and internalise its moralistic valuation; and some are resigned to or defeated by its apparent impermeability. Examining the mortgage prisoners’ case enhances critical Marxist and asset-based accounts by highlighting the complexity of financialised housing systems and how it impacts stratification and life chances.

Psychiatric Influences on Hidradenitis Suppurativa: A Call for Help.

Background Hidradenitis suppurativa (HS) is associated with a high prevalence of psychiatric disorders. However, no studies examine how psychiatric disorders influence surgical and financial outcomes. This study aimed to assess impact of a psychiatric diagnosis on patients treated for HS. Methods Patients with HS were retrospectively identified at a single institution from 2010 to 2021. Cohorts were stratified by the presence of a psychiatric disorder. Demographics, comorbidities, and disease characteristics were collected. Outcomes assessed included the procedural interventions and emergency department (ED) visits. Financial distress was assessed via the COST-FACIT Version 2 survey. Results Out of 138 patients, 40 (29.0%) completed the survey of which 19 (47.5%) had a preexisting psychiatric diagnosis. No demographic differences were found between cohorts. Mean follow-up was 16.1 ± 11.0 months. The psychiatric cohort had a higher median number of surgeries received (7.0 vs. 1.5, p < 0.001), a higher median number of ED visits (1.0 vs. 0, p = 0.006), and a similar hospital length of stay ( p = 0.456). The mean COST-FACIT score of the overall study population was 19.2 ± 10.7 (grade 1 financial toxicity). The psych cohort had a lower mean COST-FACIT score (16.8 vs. 21.3, p = 0.092) and reported greater financial hardship (3.3 vs. 1.7, p < 0.001). On multivariate analysis, a psychiatric diagnosis was predictive of lower credit scores, more ED visits, and a higher number of surgeries. Conclusion Preexisting psychiatric conditions in patients with HS are associated with increased health care utilization and surgical intervention with substantial financial distress. Plastic surgeons should be cognizant of such comorbid disorders to facilitate holistic care addressing all patient needs.

Effects of credit rating changes on corporate capital structure in South Africa

Orientation: The changes in credit ratings for corporates have a great impact on corporate funding decisions, costs and capital structures.Research purpose: The study aimed to identify the relationship between credit ratings and capital structures in emerging economies such as South Africa.Motivation for study: Investors, financial managers, regulatory authorities and financial analysts focus on the credit quality of companies as measured by credit ratings in making financing and investing choices. The credit rating is a significant communication tool, and businesses consider it crucial when deciding on capital structure. An ideal capital structure of a company is one that reduces its relative cost of capital by striking a balance between the capital structure proportions to enhance value.Research approach, design and method: A systematic and quantitative approach using semi-annual data from 2011 to 2020 sourced from EquityRT and the JSE.Main findings: Credit ratings have a positive and material impact on the capital structure decisions of South Africa’s top 40 companies. Thus, a higher debt-to-asset ratio is encouraged when the credit score improves, and a downgrade is more likely to be followed by a capital reduction behaviour.Practical/managerial implications: Investors, managers and regulators can use the findings of this study for financial decision-making purposes, anticipating changes in future corporate capital structures and monitoring funding opportunities as well as balancing debt to equity in the capital construction of an organisation.Contribution/value-add: The evidence generated by the study presented that credit rating changes influence capital structure.

Privacy and the City: How Data Shapes City Identities

Abstract This article bridges comparative constitutional law with research inspired by city leadership and the opportunities that technology brings to the urban environment. It looks first to some of the causes of rapid urbanization and finds them in the pitfalls of antidiscrimination law in federations and quasi-federations such as the United States and the European Union. Short of achieving antidiscrimination based on nationality, the EU has experimented with data privacy as an identity clause that could bring social cohesion the same way purportedly freedom of speech has done in the US. In the City however, diversity replaces antidiscrimination, making cities attractive to migrants across various walks of life. The consequence for federalism is the obvious decline of top-down or vertical, state-based federalism and the rise of legal urbanism whereby cities establish loose networks of cooperation between themselves. These types of arrangements are not yet a threat to the State or the EU but might become such if cities are increasingly isolated from the political process (e.g., at the EU level) and lack legal means to assert themselves in court. City diversity and openness to different cultures in turn invites a connection to new technologies since unlike antidiscrimination that is usually strictly examined on a case-by-case level, diversity can be more readily computed. Finally, the article focuses on NYC and London initiatives to suggest a futuristic vision of city networks that instead of using social credit score like in China, deploy data trusts to populate their urban environments, shape city identities and exchange ideas for urban development..

An Exploratory Study on Role of Direct Selling Agent towards Bank and Customer with Specific Reference to Home Loan Services in Punjab

DSA (Direct Selling Agent) that agent who involves in the process of grants home loan from the bank or NBFC to the potential customer. DSA’s job outline is create leads, documentation, cross checking. This study explains role and responsibility of DSA towards bank and customers, awareness of DSA services of the different banks and NBFC among the potential customer, challenges faced by the customer for the approval of home loan and challenges faced by the DSA at the time of process home loan application. In this study the researcher has to implement both primary data and secondary data complete this paper. The primary data deals with raw data collected through questionnaire to the respondents related with their level of disbursement of housing loan. The secondary data consists of reports of existing customers from the banks and NBFC. Pass the housing loan by banks and NBFC provides a boost for the growth and development of house construction industries. Now a time lack of knowledge among individuals related with banking products of all types of banks at one time. In case home loan customer wants to take loan according to their requirements for example lower rate of interest, Type of banks, credit score for transparency and clarity of about loan terms. There are many problems discussed already in previous studies such as different cost of borrowing, collateral security, processing time, proof of income, documentation and margin money. These problems studied by the previous researcher described in the section of the literature review. In view of the above things, it is necessary to conduct a research study to fill these gaps that have been depicted as research problem. The respondents for this research who belong to upper class, middle class or lower-class groups and lived in Patiala city or zones of Patiala. Keywords satisfaction level of customer, Responsibility of Direct selling agent, Challenges faced by DSA

Student 360: Navigating Courses at your Fingertips with Student 360

Abstract: In colleges and Universities, faculty face a lot of problems collecting information from students about the completion and choosing of Open Electives and Professional electives at the beginning of the semester. Also, most of the students select the subjects which were already attained by them, not realizing until the final semester. Thus, resulting in not satisfying the required credit score and taking an extra semester wasting their valuable time. Student360 is a project that focuses on students completing their Open Electives and Professional electives, faculty managing students academic profiles and admins adding subjects elected by students and also regarding their academics. The project works as a reminder for students to check their credit score and wrap up the subjects which were not completed by them previously

Prevalence of and Factors Associated with Financial Toxicity After Pancreatectomy and Gastrectomy.

Financial toxicity (FT) refers to the adverse impact of cancer treatment costs on patients' experiences, potentially leading to poor adherence to treatment and outcomes. However, the prevalence of FT among patients undergoing major upper gastrointestinal cancer operations, as well as factors associated with FT, remain unclear. We conducted a cross-sectional study by sending the Comprehensive Score for financial Toxicity (COST) survey and Surgery-Q (a survey specifically developed for this study) to patients who underwent gastrectomy or pancreatectomy for malignant disease at our institution in 2019-2021. We sent the surveys to 627 patients and received responses from 101 (16%) patients. The FT prevalence (COST score <26) was 48 (48%). Patients likely to experience FT were younger than 50years of age, of non-White race, earned an annual income <$75,000, and had credit scores <740 (all p<0.05). Additionally, longer hospital stay (p=0.041), extended time off work for surgery (p=0.011), and extended time off work for caregivers (p=0.005) were associated with FT. Procedure type was not associated with FT; however, patients who underwent minimally invasive surgery (MIS) had a lower FT probability (p=0.042). In a multivariable analysis, age <50years (p=0.031) and credit score <740 (p<0.001) were associated with high FT risk, while MIS was associated with low FT risk (p=0.024). Patients with upper gastrointestinal cancer have a major risk of FT. In addition to predicting the FT risk before surgery, facilitating quicker functional recovery with the appropriate use of MIS is considered important to reducing the FT risk.

Fintech lenders and borrowers screening: Superior abilities or lax practices?

Fintech lenders target borrowers that commercial banks deem too risky. We hypothesize that such borrower selection is either due to superior screening abilities of Fintech lenders, or due to less rigorous screening practices from their part. We find evidence in favor of the “superior screening” hypothesis. Using data on U.S. conforming mortgage loans originated between 2012 and 2022, we show that Fintech mortgage lenders serve borrowers with lower credit scores compared to banks, while experiencing better mortgage performance. This result is particularly strong in the mortgage refinancing market. These findings suggest that Fintech mortgage lenders specialize in providing refinancing credit to ex-ante riskier mortgage applicants, to whom they successfully apply rigorous screening standards.