Previous research on calculative intermediaries shows how these effectively challenge, distort, and disrupt accounting practices in ways that policy‐makers might not anticipate. The promises of ...surveillance capitalism—with its attendant data architectures, datafication processes, and technological sophistication—are different, supposing more accurate ways of reading individuals and greater calculative certainty overall. Yet there is little empirical research to explore how surveillance capitalism manifests itself at the organizational level, either conceptually or operationally. As a result, it remains uncertain whether such specters of omniscience are as haunting in reality as they appear in theory. We explore these themes by way of an ethnographic study into credit scoring in China, showing how intermediary organizations developed a multiplicity of credit scoring models based on machine learning and big data that differed both from original expectations and from each other. These different “renditions” of credit scoring suggest that the data architectures of surveillance capitalism are just as much subject to challenge and adaptation by intermediary organizations as calculative practices, such as accounting, are in more analog environments.
Résumé
La montagne est haute et l’empereur lointain : l’évaluation du risque de crédit et l’infrastructure du capitalisme de surveillance en Chine
Les recherches antérieures sur les intermédiaires de calcul montrent comment ceux‐ci contestent, altèrent et bousculent les principes de la méthode comptable, et ce, de manière inattendue pour les décideurs. Les promesses du capitalisme de surveillance — incluant les architectures de données, les processus de datafication et la sophistication technologique qui l’accompagnent — sont tout autres, car elles tentent d’apporter une plus grande précision des méthodes utilisées pour étudier le comportement des individus et des calculs en général. Pourtant, il existe peu de recherches empiriques sur la manière dont le capitalisme de surveillance est pratiqué au niveau organisationnel, que ce soit sur le plan conceptuel ou opérationnel. Par conséquent, il n’est pas certain si ces manifestations d’omniscience sont aussi fréquentes dans la réalité qu’elles ne le sont en théorie. Les auteurs explorent ces thèmes au moyen d’une étude ethnographique sur l’évaluation du risque de crédit en Chine. Ils montrent comment les organisations intermédiaires ont élaboré une multiplicité de modèles d’évaluation du risque de crédit, basés sur l’apprentissage automatique et les mégadonnées qui diffèrent à la fois des attentes initiales et les uns des autres. Ces différents « rendus » de l’évaluation du risque de crédit suggèrent que les architectures de données du capitalisme de surveillance sont tout aussi sujettes à la contestation et à l’adaptation par les organisations intermédiaires que les pratiques de calcul telles que la comptabilité le sont dans des contextes plus analogiques.
STATUS GOODS Bursztyn, Leonardo; Ferman, Bruno; Fiorin, Stefano ...
The Quarterly journal of economics,
08/2018, Volume:
133, Issue:
3
Journal Article
Peer reviewed
Open access
This article provides field-experimental evidence on status goods. We work with an Indonesian bank that markets platinum credit cards to high-income customers. In a first experiment, we show that ...demand for the platinum card exceeds demand for a nondescript control product with identical benefits, suggesting demand for the pure status aspect of the card. Transaction data reveal that platinum cards are more likely to be used in social contexts, implying social image motivations. In a second experiment, we provide evidence of positional externalities from the consumption of these status goods. A final experiment provides suggestive evidence that increasing self-esteem causally reduces demand for status goods, indicating that social image might be a substitute for self-image.
This paper examines the impact of bank ownership on credit growth in developing countries before and during the 2008–2009 crisis. Using bank-level data for countries in Eastern Europe and Latin ...America, we analyze the growth of banks’ total gross loans as well as the growth of corporate, consumer, and residential mortgage loans. While domestic private banks in Eastern Europe and Latin America contracted their loan growth rates during the crisis, there are notable differences in foreign and government-owned bank credit growth across regions. In Eastern Europe, foreign bank total lending fell by more than domestic private bank credit. These results are primarily driven by reductions in corporate loans. Furthermore, government-owned banks in Eastern Europe did not act counter-cyclically. The opposite is true in Latin America, where the growth of government-owned banks’ corporate and consumer loans during the crisis exceeded that of domestic and foreign banks. Contrary to the case of foreign banks in Eastern Europe, those in Latin America did not fuel loan growth prior to the crisis. Also, there are less pronounced and robust differences in the behavior of foreign and domestic banks during the crisis in Latin America.
This study investigates how financial education in high school, college, or in the workplace affects the short‐ and long‐term financial behaviors of adults using the 2015 National Financial ...Capability Study (NFCS) data. Financial education appears to have generally insignificant effects on short‐term behaviors for which there is regular feedback and penalties, and thus greater opportunity for learning by doing. If consumers do not pay off their credit card bill, they get a monthly statement showing interest charges and penalties. Financial education appears to have more positive and stronger effects on long‐term behaviors with less timely feedback, and for which the adverse consequences are not fully realized until later in life, so learning by doing may not work. Not saving enough money for retirement cannot be easily or quickly corrected, if at all. The benefits to financial education may differ based on the time horizon for the financial behaviors.
We explore the link between a firm's stock returns and credit risk using a simple insight from structural models following Merton (1974): risk premia on equity and credit instruments are related ...because all claims on assets must earn the same compensation per unit of risk. Consistent with theory, we find that firms' stock returns increase with credit risk premia estimated from CDS spreads. Credit risk premia contain information not captured by physical or risk-neutral default probabilities alone. This sheds new light on the "distress puzzle"—the lack of a positive relation between equity returns and default probabilities—reported in previous studies.
Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, ...in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learning approaches and human expert-based models is a prerequisite in further introducing novel methods. Therefore, our main goal in this study is to establish a new benchmark using real consumer data and to provide machine-learning approaches that can serve as a baseline on this benchmark. We performed an extensive comparison between the machine-learning approaches and a human expert-based model—FICO credit scoring system—by using a Survey of Consumer Finances (SCF) data. As the SCF data is non-synthetic and consists of a large number of real variables, we applied two variable-selection methods: the first method used hypothesis tests, correlation and random forest-based feature importance measures and the second method was only a random forest-based new approach (NAP), to select the best representative features for effective modelling and to compare them. We then built regression models based on various machine-learning algorithms ranging from logistic regression and support vector machines to an ensemble of gradient boosted trees and deep neural networks. Our results demonstrated that if lending institutions in the 2001s had used their own credit scoring model constructed by machine-learning methods explored in this study, their expected credit losses would have been lower, and they would be more sustainable. In addition, the deep neural networks and XGBoost algorithms trained on the subset selected by NAP achieve the highest area under the curve (AUC) and accuracy, respectively.
Credit operations are fundamental in the banks’ activities and provide a significant share of their income. Under an increased demand for credit resources, credit risks are growth. It keeps the ...importance of the problem of an increase in the efficiency of lending management processes in financial institutions. The aim of the work is the justification and development of new technology and models for the management of bank lending that reduce credit risks and increases lending efficiency. The research materials are statistical data from the Bank of Russia and Rosstat. The methods of system analysis, methods of control theory, methods of statistics, optimization methods and machine learning are used. The positive results of the implementation of the proposed technology and credit management models are of practical importance to ensure the profitability growth of credit organization and contribute to its competitiveness.
An increase in the household debt to GDP ratio predicts lower GDP growth and higher unemployment in the medium run for an unbalanced panel of 30 countries from 1960 to 2012. Low mortgage spreads are ...associated with an increase in the household debt to GDP ratio and a decline in subsequent GDP growth, highlighting the importance of credit supply shocks. Economic forecasters systematically over-predict GDP growth at the end of household debt booms, suggesting an important role of flawed expectations formation. The negative relation between the change in household debt to GDP and subsequent output growth is stronger for countries with less flexible exchange rate regimes. We also uncover a global household debt cycle that partly predicts the severity of the global growth slowdown after 2007. Countries with a household debt cycle more correlated with the global household debt cycle experience a sharper decline in growth after an increase in domestic household debt.
Effective and thorough credit-risk management is a key factor for lending institutions, as significant financial losses can arise from the borrowers’ default. Consequently, machine learning methods ...can measure and analyze credit risk objectively when at the same time they face increasingly attention. This study analyzes default payment data from a credit cards’ portfolio containing some 30,000 clients from Taiwan with twenty-three attributes and with no missing information. We compare prediction accuracy of seven classification methods used, i.e. KNN, Logistic Regression, Naïve Bayes, Decision Trees, Random Forest, SVC, and Linear SVC. The results indicate that only few out of most of the typical variables used can adequately analyze default characteristics in terms of lending decisions. The results provide effective feedback to credit evaluators, lending institutions and business analysts for in-depth analysis. Also, they mention to the importance of the precautionary borrowing techniques to be used to better understand credit-card borrowers’ behavior, along with specific accounting, historical and demographical characteristics.
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