Risk and Risk Management in the Credit Card Industry

Butaru, Florentin; Chen, Qingqing; Clark, Brian J.; Das, Sanmay; Lo, Andrew W.; Siddique, Akhtar R.

doi:10.3386/w21305

Cited by 47 publications

(65 citation statements)

References 8 publications

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“…There is an extensive literature on credit scoring, both for assessing private loans (Butaru et al, 2016;Chi and Hsu, 2012;Khandani et al, 2010;Sousa et al, 2016) and corporate loans (Jones et al, 2015;Ravi Kumar and Ravi, 2007). Some recent work include Chen et al (2017), Xia et al (2017), Abellán and Castellano (2017), and Barboza et al (2017).…”

Section: Introductionmentioning

confidence: 99%

“…Papers typically use information from credit bureaus, such as number of outstanding accounts, delinquent accounts, and balance on other loans; individual account characteristics, such as current balance of the individuals accounts and monthly income; and demographic data, such as age and marital status. Butaru et al (2016) also include macroeconomic variables, such as interest rates and unemployment statistics, as an attempt to make the delinquency model generalize better over longer periods of time.…”

Section: Introductionmentioning

confidence: 99%

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Predicting mortgage default using convolutional neural networks

Kvamme

Sellereite

Aas

et al. 2018

Expert Systems with Applications

122

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We predict mortgage default by applying convolutional neural networks to consumer transaction data. For each consumer we have the balances of the checking account, savings account, and the credit card, in addition to the daily number of transactions on the checking account, and amount transferred into the checking account. With no other information about each consumer we are able to achieve a ROC AUC of 0.918 for the networks, and 0.926 for the networks in combination with a random forests classifier.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting mortgage default using convolutional neural networks

Kvamme

Sellereite

Aas

et al. 2018

Expert Systems with Applications

122

View full text Add to dashboard Cite

show abstract

“…White (1989), Granger (1995), and Kuan & White (1994) study nonlinear or neural network modeling of financial time series. Khandani, Kim & Lo (2010) and Butaru, Chen, Clark, Das, Lo & Siddique (2016) examine other machine learning models of financial default. Recent applications of deep learning in financial economics include Sirignano (2016), who models limit order books and Dixon, Klabjan & Bang (2016), who model market movements.…”

Section: Related Literaturementioning

confidence: 99%

Deep Learning for Mortgage Risk

2018

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We develop a deep learning model of multi-period mortgage risk and use it to analyze an unprecedented dataset of origination and monthly performance records for over 120 million mortgages originated across the US between 1995 and 2014. Our estimators of term structures of conditional probabilities of prepayment, foreclosure and various states of delinquency incorporate the dynamics of a large number of loan-specific as well as macroeconomic variables down to the zip-code level. The estimators uncover the highly nonlinear nature of the relationship between the variables and borrower behavior, especially prepayment. They also highlight the effects of local economic conditions on borrower behavior. State unemployment has the greatest explanatory power among all variables, offering strong evidence of the tight connection between housing finance markets and the macroeconomy. The sensitivity of a borrower to changes in unemployment strongly depends upon current unemployment. It also significantly varies across the entire borrower population, which highlights the interaction of unemployment and many other variables. These findings have important implications for mortgage-backed security investors, rating agencies, and housing finance policymakers.

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“…Previous contributions either focus on particular types of default or use transaction data that is not admissible in conventional credit scoring models. The closest contributions to our work are Khandani, Kim, and Lo (2010), Butaru et al (2016) and Sirignano, Sadhwani, and Giesecke (2018). Khandani, Kim, and Lo (2010) apply a decision tree approach to forecast credit card delinquencies with data for [2005][2006][2007][2008][2009].…”

Section: Introductionmentioning

confidence: 99%

Predicting Consumer Default: A Deep Learning Approach

Albanesi¹,

Vamossy²

2019

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We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation. JEL Codes: C45; D14; E27; E44; G21; G24.in the United States. They are proprietary measures designed to rank borrowers based on their probability of future default. Specifically, they target the probability of a 90 days past due delinquency in the next 24 months. 1 Despite their ubiquitous use in the financial industry, there is very little information on credit scores, and emerging evidence suggests that as currently formulated credit scores have severe limitations. For example, Albanesi, De Giorgi, and Nosal (2017) show that during the 2007-2009 housing crisis there was a marked rise in mortgage delinquencies and foreclosures among high credit score borrowers, suggesting that credit scoring models at the time did not accurately reflect the probability of default for these borrowers. Additionally, it is well known that credit scores and indiscriminately low for young borrowers, and a substantial fraction of borrowers are unscored, which prevents them from accessing conventional forms of consumer credit.The Fair Credit Reporting Act, a legislation passed in 1970, and the Equal Opportunity in Credit Access Act of 1984 regulate credit scores and in particular determine which information can be included and must be excluded in credit scoring models. Such models can incorporate information in a borrower's credit report, except age and location. These restrictions are intended to prevent discrimination by age and factors related to location, such as race. 2 The law also mandates that entities that provide credit scores make public the four most important factors affecting scores. In marketing information, these are reported to be payment history, which is stated to explain about 35% of variation in credit scores, followed by amounts owed, length of credit history, new credit and credit mix, explaining 30%, 15%, 10% and 10% of the variation in credit scores respectively. Other than this, there is very little public information on credit scoring models, though several services are now available that allow consumers to simulate how various scenarios, such as paying off balances or taking out new loans, will affect their scores.The purpose of our analysis is to propose a model to predict consumer default that uses the same data as conventional credit scoring models, improves on their performance, benefiting both lenders and borrowers, and provides more transparency and accountability. To do so, we resort to deep learning, a type of machine l...

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Risk and Risk Management in the Credit Card Industry

Cited by 47 publications

References 8 publications

Predicting mortgage default using convolutional neural networks

Predicting mortgage default using convolutional neural networks

Deep Learning for Mortgage Risk

Predicting Consumer Default: A Deep Learning Approach

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