Questions tagged [credit-scoring]

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

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12 views

WOE of raw variable vs WOE of log of same variable | What can be the impact? [closed]

Should I expect different results if I transform "var 1" to WOE compared to "log(var 1)" to WOE? WOE transformation means "Weight of evidence" transformation
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How to prove variable is ordinal

I want to run a model that determines the probability of self curing for loan customers who entered arrears. The thing is that when a customer enters arrears there are 3 ways he could go: -not cure -...
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What happens with the significance of binned variables?

For this project I was required to create a credit risk scorecard witht the 4 most relevant variables, so I binned all variables and selected them by chi2 and IV. I ran the logistic and linear ...
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Getting scores from PCA?

I have performed PCA in my dataset. The first 3 components explain 90% variance of the data points. Can I add these principle components to get a score? ...
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46 views

How does probability of default evolve over time?

Say I have a probability of default of 0.02 (which is annual so over next year) for a certain client. Then say this client takes out a 180 day loan, how can I adjust my probability of default for this ...
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Anchor Point Calibration (Cyclicality, Central Tendency) - Probability of Default (PD) Question about assessment - Credit Risk

Let's assume we have a PD model that was calibrated with the following parameters: ...
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1answer
41 views

Target Score and Target Odds in Credit Scorecard development

I am working on building a Credit Scorecard model. Till now I have performed the following steps: Data preparation and cleansing Calculate WOE and IV values Model Fitting Now I working on ...
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17 views

Scaling process using point of double odds

I am trying to use point of double odds to compare probability between different models I know the point of double odds is the difference in score required for odds to double. I know that I can use ...
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44 views

High cross validation score but low model performance on test set

I'm doing a machine learning project and need to predict a user's credit default probability. I tried some simple automated feature engineering and got a good AUC score on training set using ...
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Scaling credit risk scorecard

I need to build a credit risk scorecard using logistic and linear regression. The variables using to predict are all dummies, where each dummy is a bin of some variable. Let's say the variable age, I ...
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15 views

How to train a new classification model based on the labels obtained from the existing model? [duplicate]

We have a credit scoring model based on the logistic regression that we currently use in production. Every person who wants to get a short term loan is being scored by our current model and is either ...
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38 views

Rating model calibration using Bayes formula

I tried asking this question in the Quant finance section of stack overflow, with no luck. Maybe someone here can help me out. The setting is as follows: There are two different estimates for the ...
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28 views

How to transform/convert likelihoods to scores?

I have the probability of loan default for a labeled dataset where the distribution of probabilities is heavily skewed. Labels are defined as "good/0" for no default and "bad/1" for defaults. My goal ...
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To determine variables to figure out the bad customers in credit risk modeling [closed]

I am developing a probability to default model on a data from landing firm. After running the GLM() model i have got the below message: ...
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38 views

Predicting the loan default probability for next t intervals by predictSurvProb()

I have a loan dataset that contains details of customers for different loan types. Every category of loan have different tenure(starting from 6Months to 60Months) We are defining "Good" or "Bad" to a ...
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49 views

How to replace categorical variables in the testset with their WOE calculated using a training set?

I have calculated the Weight of evidence of two high cardinality variables(Postal_Code_L and Managing_Sales_Office_Nbr) using ...
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Is the regression coefficient the same for all categories of a categorical variable?

Let's imagine I build a scorecard with a single binned variable that can only take two values. In the weight of reference framework I would replace the two possible values by their weight of evidence ...
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1answer
45 views

German credit dataset : interpretation of checking_status feature

I am struggling to understand the meaning of some features of the german credit dataset (https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)). I am particularly interested in, the ...
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1answer
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What is the relationship between the risk ratio and the weight of evidence? [duplicate]

I've been reading about risk ratios as typical measures in clinical settings. In the finance and credit literature, there is the weight of evidence (WoE) measure that is used to encode and study ...
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1answer
494 views

Credit scorecard using Logistic Regression on R

I tried developing a scorecard to assess creditworthiness using the "Scorecard" package on R. The problem I encountered is when I scale the card and calculate the points for each attribute of each ...
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82 views

Using categories instead of WoE values

As for as I can understand, the Weight of Evidence strategy is the following: For continuous independent variables : First, create bins (categories / groups) for a continuous independent variable and ...
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106 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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13 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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220 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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45 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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1answer
156 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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575 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
247 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
552 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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137 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
82 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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61 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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66 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
146 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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2answers
267 views

Choosing a better model and dealing with missing data?

I am trying to create a logistic regression model to predict whether a customer given a loan will be a bad or a good customer: bad meaning missing a certain amount of payments and good meaning ...
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1answer
159 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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59 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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220 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
136 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
1k 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
231 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
119 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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1k 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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2answers
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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60 views

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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1k 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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3answers
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
201 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
383 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 ...