Majority-Minority Boards of Directors and Decision Making: The Effects of Homophily on Lending Decisions

In this study, I examine the role racial minorities in the boardroom can play in reducing social injustice by promoting more equal access to mortgage credit to minority households. I develop a simple theoretical model that posits directors who are racial minorities provide the credit unions they govern with a perspective that shapes lenders’ trust of minority applicants. This trust is shaped by homophily and the tendency of individuals to prefer interactions with similar individuals. Using mortgage loan data from a cross-section of credit unions in the United States from 185,446 applications, I find that credit unions where the majority of board members are minorities are less likely to reject a similarly qualified minority applicant than their counterparts. Governance by minority directors significantly reduces the effects of discrimination faced by minority applicants. The board’s effect is strongest in minority neighborhoods and where the homophily is stronger between directors and applicants.

Trusting Your Bank in a Digitally Connected World: An Investigation into Perceptions of Privacy by Bank Customers

<p>People are placing more of their personal information online as the use of online social networking sites (OSNs) grows. Individuals often lack an awareness around the privacy implications of placing their personal information on these sites but still have an expectation of privacy about this information that may not entirely be justified. OSN data is often used for purposes other than those for which it was provided, but customer demand for ethical and compassionate use of their data is growing. Customers expect greater corporate social responsibility from companies, and especially banks, after the recent global financial crisis. Customers may perceive the use of OSN data by New Zealand banks to influence their lending decisions as a privacy violation. This study is intended to evaluate whether this use of OSN data would be perceived by customers to be a violation of their privacy. The research was carried out through a web-based survey and follow-up interviews with selected respondents. It was found that the less aware that respondents were about OSN privacy policies, the greater their expectation of privacy. The research also highlighted that even respondents who did not expect their data to remain private still had an expectation of privacy. A lack of perceived control was found to be associated with a greater expectation of a privacy invasion. Trust in respondents' banks was associated with a negative perception of those banks' use of OSN data for lending decisions. This study has revealed a high likelihood that a perception of betrayal coupled with a perceived privacy violation would take place should New Zealand Banks use OSN data in this manner.</p>

Trusting Your Bank in a Digitally Connected World: An Investigation into Perceptions of Privacy by Bank Customers

<p>People are placing more of their personal information online as the use of online social networking sites (OSNs) grows. Individuals often lack an awareness around the privacy implications of placing their personal information on these sites but still have an expectation of privacy about this information that may not entirely be justified. OSN data is often used for purposes other than those for which it was provided, but customer demand for ethical and compassionate use of their data is growing. Customers expect greater corporate social responsibility from companies, and especially banks, after the recent global financial crisis. Customers may perceive the use of OSN data by New Zealand banks to influence their lending decisions as a privacy violation. This study is intended to evaluate whether this use of OSN data would be perceived by customers to be a violation of their privacy. The research was carried out through a web-based survey and follow-up interviews with selected respondents. It was found that the less aware that respondents were about OSN privacy policies, the greater their expectation of privacy. The research also highlighted that even respondents who did not expect their data to remain private still had an expectation of privacy. A lack of perceived control was found to be associated with a greater expectation of a privacy invasion. Trust in respondents' banks was associated with a negative perception of those banks' use of OSN data for lending decisions. This study has revealed a high likelihood that a perception of betrayal coupled with a perceived privacy violation would take place should New Zealand Banks use OSN data in this manner.</p>

Non-financial variables related to governance and financial distress prediction in SMEs–evidence from Egypt

PurposeThis study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.Design/methodology/approachThis study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.FindingsThe results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .Research limitations/implicationsAlthough the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.Practical implicationsThe proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.Originality/valueThis study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.

Algorithm Transparency through the Fair Credit Reporting Act (FCRA)

Racial discrimination in housing has long fueled disparities in homeownership and wealth in the United States. Now, automated algorithms play a dominant role in rental and lending decisions. Advocates of these technologies argue that mortgage lending algorithms reduce discrimination. However, “errors in background check reports persist and remain pervasive,” and algorithms are at risk for inheriting prejudices from society and reflect pre-existing patterns of inequality. Additionally, algorithmic discrimination is often challenging to identify and difficult to explain or prosecute in court. While the Federal Trade Commission (FTC) is responsible for prosecuting this type of discrimination under the Fair Credit Reporting Act (FCRA), their enforcement regime “has inadequately regulated industry at the federal and state level and failed to provide consumers access to justice at an individual level,” as evidenced by its mere eighty-seven enforcement actions in the past forty years. In comparison, 4,531 lawsuits have been brought under the FCRA by other groups in 2018 alone. Therefore, the FTC must update its policies to ensure it can identify, prosecute, and facilitate third-party lawsuits against a primary driver of housing discrimination in the 21st century: discrimination within algorithmic decision making. We recommend that the FTC issue a rule requiring companies to publish a data plan with all consumer reporting products. Currently, the FTC recommends that companies make an internal assessment of the components of the proposed data plan to ensure that they are not in violation of the FCRA. Therefore, requiring that these plans be published publicly does not place undue burden on companies and empowers consumers to advocate for themselves and report unfair practices to the FTC. Coupled together, these will reduce the costs of investigation and enforcement by the FTC and decrease the discriminatory impact of automated decision systems on marginalized communities.

Forward Guidance and Corporate Lending

We suggest that forward guidance, via publicly committing the central bank to future actions and creating associated expectations, fundamentally affects bank lending decisions independently of other forms of monetary policy. To test this hypothesis, we build a forward guidance measure based on the language used in the Federal Open Market Committee meetings and match this measure with syndicated loans. Our results show that expansionary forward guidance decreases corporate loan spreads and that this effect is stronger for well-capitalized banks lending to riskier firms. Forward guidance also affects nonprice lending terms, such as covenants, performance pricing provisions, and the loan syndicate structure. Additionally, banks tend to initiate new lending relationships with lower spreads after forward guidance issuance.

Economic Consequences and the Motive to Discriminate

Past research indicates that increasing the economic consequences of evaluations should theoretically discourage discrimination by making it more costly. I theorize that such consequences may also encourage discrimination in settings in which evaluators may be motivated by performance expectations, e.g., stereotypes. I explore this theory using data from an online lending platform whose loan guarantee policy reduced the potential economic consequences of using borrowers’ demographics during lending decisions. I find evidence that with the policy in place, lenders evaluated female borrowers less favorably than male borrowers. This finding is consistent with the theory that the policy discouraged performance-motivated discrimination (that driven by beliefs about performance abilities) and simultaneously encouraged consumption-motivated discrimination (that driven by a like or dislike of others because of their demographic traits). Because I theorize about underlying motives for discrimination, the insights developed here should apply to a wide range of types of discrimination that vary according to these motives, including classic taste-based discrimination, homophily-driven discrimination, statistical discrimination, and status-based discrimination. Economic consequences may therefore represent an important dynamic link between different types of discrimination.

The Relationship Between the Financial Performance of Banks and the Quality of Credit Scoring Models

Model risk in credit scoring can be understood as the bank’s losses associated with a model quality deterioration. Deterioration in model quality entails an incorrect assessment of the creditworthiness of borrowers and leads to an increase in potentially defaulting applications in the loan portfolio, as the bank relies on the model performance when making lending decisions. The relationship between model quality and financial performance is embedded in the confusion matrix, where the value of a type I error indicates the bank’s lost profit, and the value of a type II error is equivalent to losses in the event of a default. We propose estimating model risk based on the scenario forecast of model quality or the ranking ability of the Gini model over a given time interval. The result of the analysis is the assessment of the bank’s net present value for the current and modified models, depending on the approval level. The proposed approach allows us to solve the problem of the optimal choice of a Gini model and answer the question of how model quality affects financial performance.