Questions tagged [credit-scoring]

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

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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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559 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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60 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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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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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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20 views

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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28 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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27 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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77 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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179 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
80 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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64 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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217 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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1answer
116 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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1answer
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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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34 views

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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26 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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12 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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47 views

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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33 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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37 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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40 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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42 views

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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98 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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1answer
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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133 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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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. ...