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This research analyzed car-ownership in the US during and after the COVID-19 pandemic, by utilizing a nationally representative household survey spanning from January 2020 to March 2022. Using a multilevel Hurdle model with month and state random effects, vehicle ownership trends are separately modeled for car-owning and carless households within the same modeling framework, while accounting for endogeneity and unobserved heterogeneity. An increase in the total amount of economic stimulus funding received increased the odds of vehicle ownership, while high car prices, high levels of housing expenditures, living in multi-family dwelling, and being from minority and low-skilled worker families negatively affected car-ownership. Greater household labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected car-ownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure, living arrangements and employment-related characteristics. The analysis jointly considered how short-term pandemic-related influences as well as long-term demographic and occupational factors differently affect car-ownership for car-owning households and those without cars. Policy implications are drawn for consumer protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for public transportation which has continued to suffer from lower levels of use, and sustainability programs due to higher volumes of older used cars. • Increase in economic stimulus funding increased the odds of car-ownership. • High car prices and high housing costs negatively affected car-ownership. • Greater proportion of workers affected car-owning and carless households differently. • Stringency of Stay-at-Home levels and COVID caseloads also had different effects. • Pre-existing racial, occupational and dwelling effects persisted. [ABSTRACT FROM AUTHOR]

We show that search frictions in credit markets affect accepted interest rates and loan sizes and distort consumption. Using data on car loan applications and originations not intermediated by car dealers, we isolate quasi-exogenous variation in both the costs and benefits to searching for credit. After identifying lender-specific policies that price risk discontinuously, we study the differential response to offered interest rates by borrowers who face high and low search costs. High-search-cost borrowers are 10 |$\%$| more likely to accept loan offers with higher markups, consequently originating smaller loans and purchasing older and less expensive cars than lower-search-cost borrowers. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

COVID-19 altered travel patterns in the U.S. Studies have analyzed the effect of the pandemic on travel mode, including working from home, but few have focused on automobile ownership—a relationship with potentially long-term consequences for accessibility, household budgets and debt, and policy efforts to meet climate goals. To understand the association between the pandemic and automobile ownership, we rely on a unique credit panel dataset from Experian and examine three different automobile loan-related outcome measures: annualized growth rate of new automobile loan balances, average new loan size, and the number of new loans. We focus specifically on changes across loans in neighborhoods by race/ethnicity, hypothesizing larger increases in automobile debt in Black and Latino/a neighborhoods, where workers are less likely to be able to telework. The annualized growth rate of new automobile loans increased during the pandemic across all neighborhoods by race/ethnicity, increasing most rapidly in Latino/a neighborhoods. Controlling for other factors, loan size increased similarly across neighborhoods by race/ethnicity. The increase in automobile lending in Latino/a neighborhoods, therefore, likely was explained by a significant uptick in the number of new loans. The growth in automobile lending during the pandemic was potentially prompted by pandemic-induced changes in the need for automobiles and facilitated by an expanded social safety net. As the pandemic and its various forms of public financial assistance recede, the findings underscore the importance of ongoing assistance in enabling automobile ownership or shared access among households with limited means whose livelihoods depend on the access that vehicles provide. • Annualized growth rates of new automobile loans increased during the COVID-19 pandemic across all neighborhoods. • Annualized growth rates of new automobile loans increased most rapidly in Latino/a neighborhoods. • New automobile loan size increased similarly across neighborhoods by race/ethnicity. • The increase in lending in Latino/a neighborhoods likely was due to a significant uptick in the number of new loans. • Policies should enable automobile ownership or shared access among poor households whose livelihoods depend on this access. [ABSTRACT FROM AUTHOR]

In this paper, using data from indirect auto lending and a structural model of external sales representative (ESR) behaviour, we investigate (1) the role of commissions as a potential tool to influence ESRs' pricing decisions under limited authority, (2) the impact of optimized commissions on firm profitability, and (3) the implications for customer welfare. The results provide strong evidence for ESRs being strategic (vs. myopic) in their pricing and effort decisions; and in both cases, strategic behaviour is inversely proportional to customer risk. Moreover, once optimized, commissions are an effective tool for firms to bridge the profitability gap between centralized pricing and pricing delegation. Our analyses on social justice and fairness reveal that customer groups along the dimensions of customer risk, income class, and gender, which have been traditionally marginalized in society, suffer from inequities in the indirect-lending ecosystem. While, the optimization of commissions does not intensify these biases, we found females to be the exception, and that the inequities due to gender bias not only persist in the optimized regime, but also deepen. Through counterfactual simulations, we propose two policies for firms to minimize social inequity, which helps them balance immediate profit-maximizing goals with responsible AI initiatives. [ABSTRACT FROM AUTHOR]

Lower-income consumers who seek to increase their perceived social status or to emulate their wealthier peers may be motivated to purchase conspicuous luxury goods. Using a vehicle financing dataset, we find that non-prime consumers value vehicle prestige more than the average consumer. The stronger preferences for prestige lead non-prime consumers to purchase more expensive vehicles than they otherwise would. The preferences for prestige are driven both by status signaling and peer emulation motives. Furthermore, we show that larger vehicle purchases financed by auto loans lead to worse loan performance and credit standing for non-prime consumers. [ABSTRACT FROM AUTHOR]

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost "for free." That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) "Investissements d'Avenir" [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925. [ABSTRACT FROM AUTHOR]

We document racial disparities in auto lending. Combining credit bureau records with borrower characteristics, we find that Black and Hispanic applicants' approval rates are 1.5 percentage points lower, even after controlling for creditworthiness. In aggregate, this effect crowds out 80,000 minority loans each year. Results are stronger where racial biases are more prevalent and lending competition is lower. Minority borrowers pay 70-basis-point higher interest rates, but default less ceteris paribus, consistent with racial bias rather than statistical discrimination. A major antidiscrimination enforcement policy initiated in 2013, but halted in 2018, reduced unexplained racial differences in interest rates by 60 |$\%$|⁠. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Residents in every state in the United States can lose their driver’s license or car registration because they owe debt to the state. At least eleven million people across the United States suffer these debt-based driving restrictions at any given time. Because Americans overwhelmingly rely on personal automobiles for transportation, states, by controlling access to driver’s licenses and vehicle registrations, use debt policy to control where and how people travel. And because these laws disproportionately affect people of color, primarily Black people, this kind of regulation props up racial segregation in both location and opportunity. This Article’s first contribution is empirical, descriptive, and prescriptive: it catalogs debt-based driving restrictions across multiple categories for all fifty states and the District of Columbia, provides a long overdue analysis of their harms, and proposes corrective legislative action. The driving suspension laws that animate this project are not the first instance where the government has turned to debt policy as a mechanism of racial control. These laws are not an aberration, but rather the latest iteration of a long tradition of government actors using debt policy to expand physical mobility for White people and simultaneously constrict physical mobility for Black people. Tracing this thread across American history provides the basis for this Article’s second contribution—a theoretical one—that expands the traditional view of credit and debt as a mechanism of social and financial mobility to include an analysis of credit and debt as a force that acts on physical mobility. When we expand the lens to include debt policy’s effects on physical mobility, we also see debt policy’s differential effects on White and Black people in the United States. Thus, this Article begins a new conversation about debt and debt policy, one that interrogates debt policy’s racialized effects on physical mobility, freedom, and personhood. [ABSTRACT FROM AUTHOR]

In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results. [ABSTRACT FROM AUTHOR]

We study the connection between financialization and consumerism, and claim that greater availability of consumer loans affects consumerist culture and promotes the institution of conspicuous consumption. We extend the ideas of Thorstein Veblen who coined the term of "conspicuous consumption" to illustrate our thinking with the empirical evidence on car purchases financed through bank loans in Russia's Rostov Region. We find that leveraged car buying has become embedded over the past 20 years. Social positioning drives people to seek more expensive cars, whereas getting a bank loan allows to buy an otherwise unaffordable car. We see a sign of conspicuousness in the fact that household income grows slower than the average price of a new car financed by a car loan, as well as the amount of loans. Our contribution is that a basic concept of institutional theory (conspicuous consumption) is combined with elements from other research approaches, namely the social significance of bank lending, functional differentiation of credit, and the effects of financialization. [ABSTRACT FROM AUTHOR]

Contextual dynamic pricing aims to set personalized prices based on sequential interactions with customers. At each time period, a customer who is interested in purchasing a product comes to the platform. The customer's valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise. The seller does not observe the customer's true valuation, but instead needs to learn the valuation by leveraging contextual information and historic binary purchase feedback. Existing models typically assume full or partial knowledge of the random noise distribution. In this paper, we consider contextual dynamic pricing with unknown random noise in the linear valuation model. Our distribution-free pricing policy learns both the contextual function and the market noise simultaneously. A key ingredient of our method is a novel perturbed linear bandit framework, in which a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing. We establish the regret upper bound and a matching lower bound of our policy in the perturbed linear bandit framework and prove a sublinear regret bound in the considered pricing problem. Finally, we demonstrate the superior performance of our policy on simulations and a real-life auto loan data set. Funding: Y. Liu and W.W. Sun acknowledge support from the National Science Foundation Division of Social and Economic Sciences [Grant NSF-SES 2217440]. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/moor.2023.1369. [ABSTRACT FROM AUTHOR]

We document the existence of gender and racial gaps in dealer markups of auto financing interest rates. Most car buyers are unaware that dealers can mark up interest rates for auto loans, the third most prevalent U.S. consumer debt category, beyond the rate dictated by lenders. Dealers can set their markups without considering buyer risk profiles, potentially leading to discrimination. Using individual transaction-level data from a 20% random sample of U.S. car dealers between 2004 and 2015, this paper shows that women and minorities pay a statistically significant 0.6% and 2.6% greater interest rate markup than men and nonminorities, respectively. The racial and gender gaps in dealer markups largely attenuate over time. Additionally, the premia for minorities and women are larger for those buyers in census block groups with lower education levels, those not trading in a car, and those in markets with fewer financial institutions. Although we find no heterogeneity in the racial gap by age and political inclination, the gender gap increases for women who are relatively older and is significant only in majority Republican-voting counties. Our analyses do not provide a precise estimate of the interaction effect between race and gender. Based on the buyer characteristics considered, the most vulnerable group of women (minorities), accounting for 4.7% (8.5%) of car buyers, pay 7.6% (27.9%) more compared with men (nonminorities). The findings inform policymakers and consumers of the gender and racial gaps in auto financing, their underlying mechanisms, and situations most susceptible to unfair lending. History: Catherine Tucker served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: O.C. Ozturk thanks the Darla Moore School of Business research grant program for support. P. K. Chintagunta thanks the Kits Center for Marketing at the University of Chicago for financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mksc.2022.0295. [ABSTRACT FROM AUTHOR]

This article studies how receiving a bonus changes consumers' demand for auto loans and their risk of future delinquency. Unlike traditional consumer products, auto loans have a long-term impact on consumers' financial state because of the monthly payment obligation. Using a large consumer panel data set of credit and employment information, the authors find that receiving a bonus increases auto loan demand by 21%. These loans, however, are associated with higher risk, as the delinquency rate increases by 18.5%–31.4% depending on different measures. In contrast, an increase in consumers' base salary will increase their demand for auto loans but not their delinquency. By comparing consumers with bonuses with those without bonuses, the authors find that bonus payments lead to both demand expansion and demand shifting on auto loans. The empirical findings help shed light on how consumers make financial decisions and have important implications for financial institutions on when demand for auto loans and the associated risk arise. [ABSTRACT FROM AUTHOR]

We document three phenomena we jointly refer to as monthly payment targeting. First, using data from 500,000 used auto loans and discontinuities in contract terms offered by hundreds of lenders, we show that demand is more sensitive to maturity than to interest rate, consistent with consumers managing payment size when making debt decisions. Second, many consumers appear to employ segregated mental accounts, spending exogenous payment savings on larger loans. Third, consumers bunch at round number monthly payment amounts, consistent with heuristic budgeting. That these patterns hold in subsamples of likely constrained and unconstrained borrowers challenges liquidity constraints as a complete explanation. [ABSTRACT FROM AUTHOR]

We study dealer compensation in the indirect auto lending market that increases lender's market share and protects consumers from potential discrimination. We study dealer compensation in the indirect auto lending market, where most lenders give dealers the discretion to mark up interest rates and the markup constitutes a dealer's compensation. To protect consumers from potential discrimination by this dealer discretion, several banks adopted a policy that removes dealer discretion and compensates dealers by a fixed percentage of the loan amount. We document that this policy decreased (increased) the interest rates for low-credit (high-credit) consumers; however, the market share of these banks also decreased (increased) in low-credit (high-credit) segments—a reversal of the usual demand curve. This reversal highlights a significant influence of auto dealers on consumer choices. Accordingly, we develop an empirical model that features dealer-consumer bargaining. Our estimation results show systematically different levels of bargaining power across consumer groups. We use the model to explore alternative compensation schemes that remove dealer discretion. We find that a lump-sum compensation scheme obtains the most market share. In addition, the optimized lump-sum scheme improves consumer welfare compared with the adopted policy. Our study highlights the importance of accounting for the incentives and bargaining power of middlepersons. History: Puneet Manchanda served as the senior editor. Supplemental Material: The data files are available at https://doi.org/10.1287/mksc.2022.1418. [ABSTRACT FROM AUTHOR]

As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect. [ABSTRACT FROM AUTHOR]

Auto lenders were perhaps the biggest winners of the 2005 Bankruptcy Reform, as Chapter 13 bankruptcy filers can no longer "cramdown" the amount owed on recent auto loans. We estimate the causal effect of this anticramdown provision on the price and quantity of auto credit. Exploiting historical variation in states' usage of Chapter 13 bankruptcy, we find strong evidence that eliminating cramdowns decreased interest rates and some evidence that loan sizes increased among subprime borrowers. The decline in interest rates is persistent and is robust to a battery of sensitivity checks. We rule out other reform changes as possible causes. Received September 29, 2016; editorial decision January 15, 2019 by Editor Philip Strahan. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The article discusses developments regarding auto finance in the U.S. The Federal Trade Commission and the Napleton Automotive Group of dealerships entered into a consent order addressing the former's deceptive practices related to add-on products and services financed in connection with vehicle purchases and discrimination against African Americans. The Consumer Financial Protection Bureau warned lenders and servicers against unfair or deceptive vehicle collection and repossession practices.

In a major peer-to-peer (P.2.P.) lending market in China, we observe that some investors choose not to use auto-investment service and stick to time-consuming manual investment. By analysing over 200 million pieces of data, we find that the do-it-yourself (D.I.Y.) investors pursue 1.20% higher annual return and five to seven months shorter maturity than the auto-investment service can offer. Indeed, D.I.Y. investors obtained 1.25% higher return than auto-investors, but they also took excessive risk. These results are confirmed by dual investors sample, who switch between D.I.Y. and auto-investment services. We also show that the results are not due to algorithm priority. We suggest that financial institutions provide more personalized services and products to accommodate investors with various target returns and risk attitudes. [ABSTRACT FROM AUTHOR]

Nearly 40 percent of Americans approaching retirement felt heavily indebted. Understanding the role of secured and unsecured debt in retirement planning becomes an urgent concern for researchers and policymakers alike. Using data from the 2015 National Financial Capability Study (NFCS), the current study identified secured debt (mortgage and auto loan) and unsecured debt (medical debt and credit card debt) among a national sample of pre-retirees aged 51–61 years. Logit regression models were estimated to examine and compare each debt's relationship retirement planning among pre-retirees. We found a relatively large portion of the pre-retiree sample approached retirement in debt, and having debt was negatively associated with retirement planning. We also found that secured debt does not seem to facilitate retirement planning, and unsecured debt had a strong negative association with retirement planning. Our findings highlight differential impact that debt from different sources can have on retirement security, calling for closer examination on the role of debt in retirement security across income groups and those without retirement plans. Findings of this study yield policy implications on access to retirement accounts and financial education provision towards financial health and solvency of older Americans. [ABSTRACT FROM AUTHOR]

We analyze the effect of import competition on household balance sheets using individual data on consumer finances. We exploit variation in local industry exposure to foreign competition to study households' response to the income shock triggered by China's accession to the World Trade Organization. We show that household debt increases significantly in regions where manufacturing industries are more exposed to import competition. The effects are driven by home equity extraction and are concentrated in areas with strong house price growth. Our results highlight the role played by mortgage markets in absorbing displacement shocks triggered by globalization. [ABSTRACT FROM AUTHOR]

This paper provides a statistical estimate of the breakdown in race outcomes in Formula One races between the two most important inputs: driver skill and car technology. Financial data and racing results from the 2012–19 F1 seasons are used to estimate a combined driver and team fixed effects FGLS regression model for each season. Treating each season uniquely allows for the exclusion of weather and track specific variables common to other statistical studies of F1 racing. Our use of financial data provides an answer to the economic question of how should F1 teams allocate their scarce financial resources. The so-called "80-20" rule distinguishing team effects and driver effects is found to be a very rough approximation to the output shares for teams and drivers. A strong complementarity exists between driver skill and car technology that distorts the rule. The return to driver salaries and team budgets are both positive in term of race outcomes, but at diminishing rates. [ABSTRACT FROM AUTHOR]

This paper addresses the use of a customer character model as a determinant of used auto loan churn among a unique population of subprime borrowers. The customer character model (i.e restricted model) is compared to a full model consisting of the 4 Cs of capacity, collateral, credit, and character of churn prediction. The results reveal that there is a difference between the full model and the customer character model. Additionally, different supervised classification methods, such as logistic regression (LR), linear discriminant analysis (LDA), decision trees (DTs), and random forests (RFs), are applied and compared in terms of multiple predictive performance measures. The RF classification measures report the strongest performance. Additionally, different classification methods suggest the importance of different customer character variables. Therefore, from a practical perspective, effective borrower character screening is recommended to determine customer profiles more accurately for the purposes of target marketing and customer retention. This study also deepens understanding of subprime credit markets and reveals additional insights to credit screening using machine learning techniques. [ABSTRACT FROM AUTHOR]

United States households' consumption expenditures and car purchases collapsed during the Great Recession and more so than income changes would have predicted. Using CEX data, we show that both the extensive and the intensive car spending margins contracted sharply in the Great Recession. We also document significant cross‐cohort differences in the impact of the Great Recession including a stronger reduction in car spending by younger cohorts. We draw inference on the sources of the Great Recession by investigating which shocks can explain household choices in a 60 period life‐cycle model with idiosyncratic and aggregate shocks fitted to aggregate and life‐cycle moments. We find that the Great Recession was caused by a combination of large aggregate income and wealth shocks, while cross‐cohort adjustment patterns imply a role for life‐cycle income profile shocks. We also find a role for car loan premia shocks in accounting for car spending and car loans. [ABSTRACT FROM AUTHOR]

We examine whether different organizational forms influence agents' pricing decisions. We study a secondhand car loan setting consisting of independent agents and in-house agents. We consider independent agents to be more entrepreneurial than in-house agents and argue that entrepreneurship motivates agents to scan the environment for the opportunity to maximize their payoffs. We predict that entrepreneurial agents are more likely to increase prices when presented with the opportunity to do so. We expect this to be the case for opaque loans as these loans are less subject to competition than transparent loans. We find that opaque loans have higher prices than transparent loans for both types of agents, and the price difference is more pronounced for independent agents. Our evidence suggests that entrepreneurship enables agents to perform better in an environment where agents can profit from exploring opportunities, i.e., a less competitive environment. Data Availability: All data are proprietary. Confidentiality agreements prevent the authors from distributing the data. [ABSTRACT FROM AUTHOR]

Cities promote the distribution of more and better greenspaces across their urbanized areas. These spaces are not, however, well received in all quarters, and a particular category of greenspace, the public park, is often lamented for its relationship with crime.his has been found true of parks, but little has been done to disaggregate the impact of park types or their location within the city. This study conducts such an investigation to identify the effects of different types of parks and to identify any differences of those effects across space. After accounting for the unique socio-economic and demographic circumstances of different areas within a particular Canadian city, London, Ontario, it is found that only very few parks are connected with higher rates of property crime. [ABSTRACT FROM AUTHOR]

The availability of digitally verifiable employment data significantly expands credit access for economically disadvantaged customers, and at the same time, profitably expands lenders' loan portfolio. What is the value of verified employment data in consumer lending? We study this question using a data set covering all employment verification inquiries to Equifax. Using a difference-in-differences approach, we analyze the changes in applicants' loan outcomes after their employers join Equifax's digital verification system, which provides lenders with an efficient way of accessing the (employer-) verified employment data in auto loan applications. Holding the employment status constant, we find that the availability of digitally verified data significantly expands credit access: the loan origination rate increases by 35.5% on average, and is more significant among deep subprime (146%) and subprime consumers (44%). The interest rates charged on these loans rise only slightly. The expanded credit access also benefits lenders, with an estimated 19.6% increase in profit. This is because the benefit of the market expansion effect dominates the cost of a higher delinquency risk among the expanded loan portfolio. Our results suggest that, besides seeking new data sources, managers and policy makers should also consider ways to extract more value from existing data. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) has recently intensified in the global economy due to the great competence that it has demonstrated for analysis and modeling in many disciplines. This situation is accelerating the shift towards a more automated society, where these new techniques can be consolidated as a valid tool to face the difficult challenge of credit fraud detection (CFD). However, tight regulations do not make it easy for financial entities to comply with them while using modern techniques. From a methodological perspective, autoencoders have demonstrated their effectiveness in discovering nonlinear features across several problem domains. However, autoencoders are opaque and often seen as black boxes. In this work, we propose an interpretable and agnostic methodology for CFD. This type of approach allows a double advantage: on the one hand, it can be applied together with any machine learning (ML) technique, and on the other hand, it offers the necessary traceability between inputs and outputs, hence escaping from the black-box model. We first applied the state-of-the-art feature selection technique defined in the companion paper. Second, we proposed a novel technique, based on autoencoders, capable of evaluating the relationship among input and output of a sophisticated ML model for each and every one of the samples that are submitted to the analysis, through a single transaction-level explanation (STE) approach. This technique allows each instance to be analyzed individually by applying small fluctuations of the input space and evaluating how it is triggered in the output, thereby shedding light on the underlying dynamics of the model. Based on this, an individualized transaction ranking (ITR) can be formulated, leveraging on the contributions of each feature through STE. These rankings represent a close estimate of the most important features playing a role in the decision process. The results obtained in this work were consistent with previous published papers, and showed that certain features, such as living beyond means, lack or absence of transaction trail, and car loans, have strong influence on the model outcome. Additionally, this proposal using the latent space outperformed, in terms of accuracy, our previous results, which already improved prior published papers, by 5.5% and 1.5% for the datasets under study, from a baseline of 76% and 93%. The contribution of this paper is twofold, as far as a new outperforming CFD classification model is presented, and at the same time, we developed a novel methodology, applicable across classification techniques, that allows to breach black-box models, erasingthe dependencies and, eventually, undesirable biases. We conclude that it is possible to develop an effective, individualized, unbiased, and traceable ML technique, not only to comply with regulations, but also to be able to cope with transaction-level inquiries from clients and authorities. [ABSTRACT FROM AUTHOR]

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation in which the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon T) for N different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior (meta-exploration) with the need to leverage the estimated prior to achieve good performance (meta-exploitation) and (ii) accounting for uncertainty in the estimated prior by appropriately "widening" the estimated prior as a function of its estimation error. We introduce a novel prior alignment technique to analyze the regret of Thompson sampling with a misspecified prior, which may be of independent interest. Unlike prior-independent approaches, our algorithm's meta regret grows sublinearly in N, demonstrating that the price of an unknown prior in Thompson sampling can be negligible in experiment-rich environments (large N). Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared with prior-independent algorithms. This paper was accepted by George J. Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics. [ABSTRACT FROM AUTHOR]

Fraud detection is one of the core issues of loan risk control, which aims to detect fraudulent loan applications and safeguard the property of both individuals and organizations. Because of its close relevance to the security of financial operations, fraud detection has received widespread attention from industry. In recent years, with the rapid development of artificial intelligence technology, an automatic feature engineering method that can help to generate features has been applied to fraud detection with good results. However, in car loan fraud detection, the existing methods do not satisfy the requirements because of overreliance on behavioral features. To tackle this issue, this paper proposed an optimized deep feature synthesis (DFS) method in the automatic feature engineering scheme to improve the car loan fraud detection. Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. Experiments are developed based on actual car loan credit database to evaluate the performance of the proposed scheme. Compared with traditional automatic feature engineering methods, the number of features and training time are reduced by 92.5% and 54.3%, respectively, whereas accuracy is improved by 23%. The experiment demonstrates that our scheme effectively improved the existing automatic feature engineering car loan fraud detection methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the causal link between bank-based financial development and economic growth in Morocco between 2003 and 2018 using a vector autoregression framework. We test for causality between economic growth and four different measures of bank-based financial development in both the short and the long run. Our results show that bank-based financial development causes economic growth in the short and the long run. Moreover, our results show that economic growth only causes bank-based financial development in the long run. At last, we show that the Moroccan banking sector's integration with the international financial markets only affects the causal link between economic growth and bank-based financial development by providing short-run liquidity to Moroccan banks. Based on these results, barriers to the financial development of the Moroccan banking sector should be investigated and public policy should focus on designing appropriate policies and programs to alleviate these barriers in order to stimulate the growth of the Moroccan economy. [ABSTRACT FROM AUTHOR]

This study employs a behavioral technique known as asymmetric dominance used to nudge defaulted borrowers toward a repayment option that is in their best interest (lowest implied APR) while at the same time encouraging greater repayment, resulting in a win-win for both lenders and borrowers. We demonstrate the efficacy of this approach through an online experiment and then through a field experiment of 1st- and 2nd-lien actual defaulted mortgage pools. We address the generalization of asymmetric dominance by documenting success across multiple consumer asset classes – mortgages, auto loans, payday loans, student loans, health care debt and credit card debt. Finally, our results hold across 2-, 3-, and 4-digit monthly repayment amounts. [ABSTRACT FROM AUTHOR]

Prior studies of interest rate differentials between credit unions and commercial banks suffer from selection bias since they rely on data at the level of the financial institution or branch which cannot account for demand‐side or loan‐level characteristics. We improve on these studies by using household‐ and loan‐level data from the Federal Reserve's Survey of Consumer Finances from 2001 to 2019. We find that, on average, households that receive auto loans from credit unions pay 0.70 percentage points less on interest rates for new vehicles—and 1.40 percentage points less on used vehicles—relative to observably similar households that receive auto loans from banks. The aggregated savings to credit union members is larger than the estimated value of the credit union corporate income tax exemption. Nonetheless, the benefit from lower auto loan rates is likely an underestimate of the true value to consumers of credit unions' presence in the market. [ABSTRACT FROM AUTHOR]

We develop a novel extension of the mover-stayer model to allow for time-dependent variables such as macroeconomic factors and apply it to the repayment process for car loans. The MS model postulates a simple form of population heterogeneity, which is particularly well suited to describing the repayment process: a proportion of borrowers always repay on time (stayers), and a complementary proportion evolves according to a discrete-time Markov chain (movers), with an absorbing default state. In contrast to the literatures focus on the determinants of defaults, our extension examines the determinants of creditworthy borrowers (stayers). We model the probability of borrowers being stayers as a logistic function of their time-fixed covariates as well as of macroeconomic variables. The car-loans data set, obtained from a Polish bank, contains a large number of characteristics for each borrower and their repayment histories. The MS models' estimation from these data indicates that annual GDP growth is the only macroeconomic variable exerting a substantial effect on the stayers' probability: as GDP increases, so does the proportion of stayers. Because stayers are the most desirable borrowers, the proposed model should be useful to institutional lenders. [ABSTRACT FROM AUTHOR]

Here we look at the varying states of the US consumer credit series. We employ a Markov-switching model to the consumer credit series to see if these exhibit different states of behavior. Our results suggest that these series are highly persistent. They also suggest that there is a cyclical component to one consumer credit series (revolving), but not the other. This indicates that when one of these series is in a high growth state it is not likely to switch to a different state and vice versa. This is very similar to the behavior of US GDP and US business cycles. Finally, given the different behavior of the two consumer credit series, it would be important to treat them as separate variable. In this study, we evaluate various consumer credit instruments to assess whether they are cyclical or not. Our analysis suggests that some instruments, like credit cards, are cyclical and persistent while other, like car loans, are not. [ABSTRACT FROM AUTHOR]

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers' characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand but learns this through sales observations over a selling horizon of T periods. We prove that the seller's expected regret, that is, the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s T under any admissible policy. We then design a near-optimal pricing policy for a semiclairvoyant seller (who knows which s of the d features are in the demand model) who achieves an expected regret of order s T log T. We extend this policy to a more realistic setting, where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s T (log d + log T) , which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods, such as myopic pricing and segment-then-optimize policies. Furthermore, our policy improves upon the loan company's historical pricing decisions by 47% in expected revenue over a six-month period. This paper was accepted by Noah Gans, stochastic models and simulation. [ABSTRACT FROM AUTHOR]

Before the COVID-19 pandemic, auto loans outstanding in the United States had soared to record highs. The boom in lending spanned new and used cars and traditional and subprime loans. With loan delinquencies also hitting new highs almost every quarter, predictions that the auto lending market could burst soon abounded. When the economy came to a grinding halt and unemployment skyrocketed in the wake of the pandemic, auto lenders knew they were facing a crisis. Throughout 2020, auto lenders granted more payment forbearances to consumers, while slashing interest rates on new loans. Auto manufacturers similarly made promises to buyers, such as the ability to return new cars for up to a year upon job loss. Combined with the CARES Act's relief rebates and moratoria, the bottom did not fall out of the auto loan market. These measures, however, are temporary. The pandemic alone will not reduce people's need for cars, but it will burst the auto loan bubble. The economic fallout will require interventions in the auto sale and loan markets, which presents a moment to transform America's car economy. This symposium Essay details a range of financial and related measures that can be implemented in the near future to shift auto financing away from promoting economically unequal and environmentally unfriendly use and access to automobiles, and, more broadly, to shift the United States away from prioritizing automobiles as the primary means of personal transportation. [ABSTRACT FROM AUTHOR]

Securitisation scheme enables credit institutions to refinance a set of loans, exposures or receivables, such as residential loans, auto loans or leases, consumer loans, credit cards or trade receivables. These loans, transferred to a special purpose vehicle, are transformed in tradable securities offered to investors. The lender organises loans into different risk categories for investors, thus giving them access to investments in loans and other exposures to which they normally would not have direct access. Returns to investors are originated in payments made by debtors of the underlying loans. Domestic law regarding securitisation (Law no 31/2006) does not meet the market expectations in order to generate securitisation schemes. Moreover, the domestic law came into conflict with European legislation (Regulation UE 2017/2402 laying down a general framework for securitisation and creating a specific framework for simple, transparent and standardised securitisation). Finally, domestic law has been repealed to leave room for application of the European regulation. [ABSTRACT FROM AUTHOR]

Big Data methodologies are applied to understand subprime borrowers in the U.S. automobile space. The focus on the automobile market is essential as this subsegment is responsible for directly and indirectly employing over one million people and creating payrolls in excess of $100 billion annually in the U.S. It is found in this article that if a subprime borrower is a homeowner, the probability of repaying their auto loan increases by almost 4%. However, if the borrower is renting, the likelihood of repaying their auto loan increases by nearly 1.4%. Applying Big Data in making subprime auto loans can add 1000's of jobs and improve security of millions of dollars in payroll. [ABSTRACT FROM AUTHOR]

The improving sequence effect suggests that in choices between a rising earning and any other sequences, participants prefer the rising earning. Recent studies show that the improving sequence effect also exists in a loan context. As consumers have a strong preference for falling loan profiles, banks may consider to offer loans in which the loan repayments concentrate at the beginning of the loan term. In this paper, we examined the improving sequence effect in context of a car loan with three repayment plans expressed in temporally reframed prices (TRP). By regressing the evaluation of loan profiles on the perceived price attractiveness, price complexity, TRP and the interaction terms, we find that (1) the perceived price attractiveness and price complexity significantly predict the loan evaluation, and they also explain a significant proportion of variance in loan evaluation; (2) the TRP effect interacts with the improving sequence effect. Specifically, with the introduction of TRP, respondents prefer constant profiles over falling profiles. TRP may explain why level-payment loans are still popular in real world, though the improving sequence effect suggests otherwise. [ABSTRACT FROM AUTHOR]

I study the impact of changes to collateral value on borrowers' default decisions on auto loans using two natural experiments in Sri Lanka. Changes in vehicle import tax rates and loan-to-value ratio caps on auto loans generated plausibly exogenous variation in the resale value of vehicles already pledged as collateral. Using proprietary auto loan performance data, I estimate that a 10% drop in the collateral value corresponds to a 44% increase in the default rate. I also find that collateral value is more important for borrowers with higher outstanding loan balances. [ABSTRACT FROM AUTHOR]

We exploit a 2004 credit reform in Brazil that simplified the sale of repossessed cars used as collateral for auto loans. We show that the reform expanded credit to riskier, self-employed borrowers who purchased newer, more expensive cars. The legal change has led to larger loans with lower spreads and longer maturities. Although the credit reform improved riskier borrowers' access to credit, it also led to increased incidences of delinquency and default. Our results shed light on the consequences of a credit reform and highlight the crucial role that collateral and repossession play in the liberalization and democratization of credit. [ABSTRACT FROM PUBLISHER]

Purpose: This study investigates the relation of bank loan delinquencies to Fed Survey delinquency data from 2003 to 2017. Bank-generated loans have lower delinquencies than all Fed Survey loan types. Survey mortgage and auto loan delinquencies are positively related to bank loan delinquencies indicating complimentary delinquency decisions for borrowers. Conversely, student loans delinquencies are negatively related to bank loans, consistent with borrowers substituting student loan payments for bank debt for the entire sample period. Student loan delinquencies are negatively related to per-capita bankruptcy, and all other types of debt have a positive relation. The relation between Fed Survey loan delinquencies and bank-generated loan delinquencies is time varying and changed after the financial crisis in 2008. Design/methodology/approach: Seemingly Unrelated Regression is used to study delinquencies for three bank loan types and whether or not they are related to Fed Survey loan delinquencies. The sample is split into pre-financial crisis before 2008 and post-crisis after 2008. Findings: Seemingly Unrelated Regression (SUR) results show that bank delinquencies for second mortgages and "Other" loan types are consistently complementary to Fed Survey mortgage loan delinquencies. Fed Survey auto loans delinquencies are also consistent with a complimentary relation, and these results are largely driven by the relation after the financial crisis of 2008 since pre-crisis regression results are not significant for every dependent variable. Credit card loan delinquencies have a negative and substitute relation with bank-generated first mortgage loan delinquencies prior to the crisis in 2008, and with bank-generated second mortgages after the crisis. Conversely, student loan delinquencies from the Fed Survey are negatively and significantly related to bank mortgages for the entire sample period, but only with bank-generated first mortgages after 2008. The student loan delinquency results are consistent with income smoothing, on average, although this is not explicitly tested at the micro level since this study uses macro-level data and not borrower-specific data. These findings are also consistent with conventional wisdom that student loans provide "financial slack" and borrower flexibility. Research limitations/implications: A limiting factor is this study uses macro-level data and not borrower-specific data. Practical implications: Empirical findings are consistent with prior research that student loans provide income smoothing and "financial slack," and borrowers with payment challenges will pay other debt before student loans. Social implications: Borrowers in financial trouble tend to be delinquent for all debt, and more so for student debt. Originality/value: To investigate whether Fed Survey delinquencies of auto loans, first mortgages, student loans and credit card loans from all sources have complementary or substitution effects with bank debt at a macro level. The study investigates whether bank debt follows "market trends" as a complementary effect, or if bank debt has a negative relation to other debt indicating a substitution effect. [ABSTRACT FROM AUTHOR]

Using loan‐level data on millions of used‐car transactions across hundreds of lenders, we study the consumer response to exogenous variation in credit terms. Borrowers offered shorter maturity decrease expenditures enough to offset 60% to 90% of the monthly payment increase. Most of this is driven by shifting toward lower‐quality cars, but affected borrowers offset 20% to 30% of a monthly payment shock by negotiating lower prices for equivalent cars. Our results suggest that durable goods prices adjust to reflect credit terms even at the individual level, with one year of additional loan maturity increasing a car's price by 2.8%. [ABSTRACT FROM AUTHOR]

This paper explores the impact of automobile loan debt on US unemployment. Individuals with heterogeneous economic positions deem automobiles as important durable goods for unemployment exit and expected wage increases. The methodological approach makes use of an Autoregressive Distributed Lag (ARDL) Bound Testing modelling approach to document a negative and significant relationship between auto loans and unemployment. The results survive certain robustness tests, while they seem to confirm certain theoretical arguments posed in the literature, such as that the credit mechanism that dominates the transmission mechanism of monetary policy (credit shocks have a profound significant link with unemployment), while they seem to mitigate the role of alternative theories (where levered households suffer from a 'debt overhang' problem that distorts their preferences, making them demand high wages, and the 'vacancy-posting' effect) which imply that loans lead to high unemployment. The findings seem to provide significant recommendations to monetary policy makers on strengthening the banking services industry, providing an alternative to monetary policy for labour market intervention. [ABSTRACT FROM AUTHOR]

Promoting innovation requires efficient financial regulations ensuring well-functioning financial markets that play critical roles in reducing financing costs, allocating scarce resources, evaluating innovative projects, and managing risks. The author indicated that rigorous empirical studies that link financial regulation and innovation development are sparse. Thus, this study aims to provide some empirical evidence on linking government interventions, particularly by banking regulations and supervision, and a country's innovative growth from the perspective of the mediating role of financial development. Specifically, this paper demonstrates that the development of financial markets and financial institutions mediates the path between financial regulation and innovation development in Azerbaijan. The structural equation modelling technique using the statistical package PATH additionally to confirmatory factor analysis in STATISTICA was applied to analyze the data. Contrary to expectations, this study did not find a significant direct impact of changes in regulatory benchmarks related to total CAR and FX loans to total loans on Azerbaijan's rank in the Global Innovation Index and the volumes of high-technology exports. One of the more significant findings to emerge from this study is that the government regulatory and supervisory interventions in the banking sphere are changing the imprudent financial institutions' and markets' behaviour. Thereby it contributes to establishing a better developed and sound financial system in terms of their access, depth, and efficiency. Meanwhile, financial institutions' and markets' development contributes to the country's innovative development. This combination of findings provides some support for the conceptual premise that reduction or elimination of government power in the financial markets and institutions leads to exacerbating systemic risk and destabilization of the financial system that could not build extensive innovation capacities to foster growth. Ефективне фінансове регулювання забезпечує стрімкий розвиток інновацій та сприяє належному функціонуванню фінансових ринків. При цьому ефективний механізм функціонування фінансових ринків відіграє важливу роль при зменшенны витрат на фінансування, розподілі обмежених ресурсів, оцінці інноваційних проєктів та управлінні ризиками. У статті наголошено на обмеженості емпіричних досліджень щодо питань фінансового регулювання та інноваційного розвитку. Метою статті є емпіричне підтвердження взаємозв'язку між державним втручанням (зокрема за допомогою банківських норм та нагляду) та інноваційним зростанням країни з точки зору посередницької ролі фінансового розвитку. У роботі зазначено, що розвиток фінансових ринків та установ опосередковує шлях між фінансовим регулюванням та інноваційним розвитком в Азербайджані. Для реалізації практичної частини дослідження використанано метод моделювання структурних рівнянь (SEM). Аналіз панельних даних здійснено за допомогою статистичного пакету PATH у програмному забезпеченні STATISTICA. За результатами проведеного дослідження не виявлено суттєвого прямого впливу змін у нормативних показниках банківської системи та відношенні валютних позик до загальної суми позик на позицію Азербайджану в Глобальному інноваційному індексі та обсяги експорту високотехнологічної продукції. Результати дослідження дають підстави стверджувати, що державні регуляторні та наглядові втручання у банківській сфері змінюють поведінку фінансових установ та ринків, сприяючи створенню більш розвиненої та надійної фінансової системи з точки зору її доступності, глибини та ефективності. При цьому встановлено, що розвиток фінансових установ та ринків сприяє інноваційному розвитку країни. Отримані результати дають підстави стверджувати, що зменшення або ліквідація державного впливу на діяльність фінансових установ та ринків призводить до посилення системного ризику та дестабілізації фінансової системи. Таким чином, це перешкоджає розвитку інноваційного потенціалу. [ABSTRACT FROM AUTHOR]

The article discusses consumer abuses in the car loan market in the U.S. like discriminatory lending, inflated pricing, scams, and frauds, as well as how to prevent them through penalty default waiting period and regulatory oversight.

Logistic regression is a widely used machine-learning model for predicting categorical outcomes. It uses a number of explanatory variables to predict the values of the target variable that can be either binomial or multinomial. It is used in a number of fields such as cancer detection problems, risk modeling and any subject that requires computing the probability of an event occurrence. In this paper, we propose to apply this supervised machine-learning model to study prepayment risk and its determinants concerning car loans based on a number of characteristics. [ABSTRACT FROM AUTHOR]

The article discusses the actions by U.S. federal and state authorities to regulate auto finance lenders. Also cited are the consent orders given by the Consumer Financial Protection Bureau (CFPB) to lenders due to deceptive marketing practices of Guaranteed Asset Protection (GAP) products, consumer credit reports, and finance contract extensions, as well as the Department of Justice's (DOJ) settlement of violations of the Servicemembers Civil Relief Act (SCRA).