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Questions tagged [credit-scoring]

In finances, a credit score is a number representing the creditworthiness of a person.

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Formal Definition of Over-Predictive Model

I am looking for a formal definition or criterion to determine whether or not a model is over predictive. My understanding of a model being over-predictive, is a parametric model whose parameters are ...
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27 views

Credit Scoring WoE Calculation

I'm creating credit scoring model and stuck with WoE calculation. I know the formula and I know how to compute WoE for train sample. Should I use train sample WoE for test sample or I should compute ...
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11 views

Using posterior variable in credit risk model

I am rebuilding a credit risk model using logistic regression (either ridge penalty or elasticnet) to predict first payment default. Historically, the company approves an applicant for a loan to ...
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12 views

Adding Bad Events from the past to the risk default model to avoid Down/Up sampling techniques

We have been trying to build a classification model for credit default prediction using two different models one being Random forest and another being the Logistic regression based scorecard model. ...
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36 views

How do unsupervised credit scoring models that don't consider historical financial data work?

There seems to be a number of startups (Zest Finance, Credolab etc.) that provide credit scoring schemes that rely exclusively on alternative data without considering users historical financial data ...
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1answer
33 views

Hypothesis testing two sided tail test

I have a bank customer loan dataset with columns loan amount, funded amount, interest rate(high, medium, low), annual income of customer, loan status as (default and fully paid). Could I use two ...
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44 views

Calculating the long run average default rate when the portfolio changes during the year

The following question was asked on QSE and even with a bounty there was only one answer given that proposed a simulation approach. I wonder whether there is nore to say about this in a statistical ...
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1answer
99 views

Behavioral credit scoring: problems

I would like to create a behavioral credit scoring model to score the applications for which transaction data is available. There's an obvious problem mentioned in Thomas et al. Credit Scoring and Its ...
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384 views

Using bayes theorem to calculate credit risk given prior knowledge and predicted probability

How can one combine: a priori knowledge of the default proability of a certain loan type based on historical data the default probability of an individual loan as predicted by a machine learning ...
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1answer
177 views

Comparing coefficient across datasets - Cox Proportional Hazard model

I am doing a study of credit risk in europe for the period of 2006 - 2016 by using the Cox Proportional Hazard Model (time costant edition) in R (coxph). I have succesfully implemented the model for ...
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1answer
297 views

Optimal classifier or optimal threshold for scoring

In practice, there can be a classifier that gives far better performance at a specific acceptable threshold than an "optimal" classifier with better average performance across range of thresholds (...
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63 views

Missing Values and Model Scoring

How do you deal with missing values when scoring a model? Can I use multiple imputation when building the model?
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1answer
58 views

How to build Predictive models with insufficient historical/performance data

I'm building a auto loan probability of default model where the loan term could be 3 to 7 years and hence default can happen anytime in that interval. But we are a start-up and have only 3 years of ...
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56 views

How do loan companies set interest rate tiers?

What statistical or machine learning methods do companies like Lending Club use to segment their customer base into loan grades A1-G5? What would a reasonable partitioning method look like after ...
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59 views

What is the benefit of developing different scores for LGD modelling?

In the LGD Model flow presented in the figure 4.13 in the book "Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Application" which is partially available on the web: ...
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1answer
102 views

Find abnormal credit transactions based on historical data

I have a dataset of customer transactions (multiple customers,multiple transactions) and based on the historical data, I want to know when a new credit(+ve) transaction arrives if its unusual for that ...
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1answer
147 views

Using non-significant variables in model

I am trying to build a credit scoring model and have discovered and interesting approach for feature selection. I am looping through all features and removing them one by one (using variable ...
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56 views

What technique do I use to predict number of calls based on credit score?

I'm new to stats, but have been given this project: There is a call center which calls up leads and tries to get them to buy one of our products. (These are people who came to our website and filled ...
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204 views

Bayesian logit model in Psychometric or Behavioural Testing for Credit Scoring in Developing Countries

A lot of parameters in one title, I know. So there's credit scoring but not using credit history. Then there's using a Bayesian logit model. Then there's doing so in a developing country such as Haiti ...
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2answers
118 views

Creating heterogeneous risk score groups (risk based groups on the score)

I just built a credit risk score model (using logistic regression). Now that I have all estimates and resulting score per observation I would like to create risk groups, e.g.: 10 risk groups where 1 ...
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2answers
808 views

How to calculate probability for new person from existing logistic regression model?

I need to create a credit scorecard model. Once I ran a logistic regression to find out the probability of default of a customer, how do I calculate scores of new customers? I have variables like age, ...
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3answers
182 views

Improving quality of logistic regression estimation

I'm working on a credit scoring model (logistic regression), and I have divided my dataset (5082 obs with 580 negatives) in two samples: 75% training set and 25% test set. The result of the estimation ...
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1answer
106 views

optimal down payment estimation in credit scoring

Knowing I can estimate the risk of default, via logistic regression, of a consumer on a small loan... what would be the best way to estimate the optimal down-payment amount to ask for in order to ...
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983 views

Probability of Default

I'm doing a project to predict probability of delinquent for individual loans. Seems the model I fit is not good and I want to improve the model. However, I'm confused by the results I got and don't ...
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2k views

German credit data: neural network, svm, logistic regression : input variables

I'm using the following data set on some credit scoring models: https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data) My teacher told me that it's best to use the same data set for all ...
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Creating a model to interpret numerical scores

Good morning/afternoon everyone, first of all thanks to all of you for the valuable insights provided. I will be oulining here my current challenge, trying to provide as much detail as possible. ...
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982 views

Fitting survival/hazard model to probability of default

I will very grateful with some help on the following problem: I need to forecast probability of default for portfolio of retail loans, depending on several factors, that can be divided into three ...
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2k views

Good books/papers on credit scoring

I'm looking for recomendations of books on credit scoring. I'm interested in all aspects of this problem, but mostly in: 1) Good features. How to build them? Which have been proved to be good? 2) ...
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2answers
170 views

Nonparametric and parametric parts in semiparametric credit scoring

I am confused about "parametric" and "non-parametric": Our topic is nonparametric estimators for probability of default. So first of all, we consider the generalized linear models, as an example we ...
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1answer
345 views

Scorecard logistic regression — include or omit credit grade?

I am using logistic regression to create a credit scorecard from past loan data. We will not approve loans in the future if the applicant has an insufficient credit score (no credit or insufficient ...
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2answers
2k views

Best machine learning algorithm for loans dataset?

I have a dataset of about 75K samples with about 20 features per sample (12 of which are probably important) describing various credit profiles - credit score, late payments, income, etc. Some of the ...