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https://github.com/ketchbrookanalytics/autolend


https://github.com/ketchbrookanalytics/autolend

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README 

        
---

output: github_document

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```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>"

)

```



![](www/ka_logo.jpg)



# Dynamic Credit Risk Modeling



The goal of this repository is to serve as a resource for developing robust credit risk models. Ideally, this repository will include detailed documentation, resources, and examples (in `R`) related to building such models.



## The Current State of Credit Risk Models



There are many shortcomings with current approaches to modeling credit risk using traditional methods (e.g., logistic regression), particularly as it fits into the lending decision itself. More specifically, these methods lack the ability to:



* handle heterogeneity

* incorporate longitudinal history

* forecast risk over time

* dynamically update risk estimates



### Longitudinal History & Heterogeneity in the Data



Traditional frequentist statistical modeling approaches (logistic regression, linear regression, etc.) and machine learning algorithms (decision trees, neural networks, etc.) assume that each observation in the dataset is independent; in other words, no two observations should be from the same "group". This is severely limiting because it often causes us to have to aggregate our data "up" to a higher level, such that we have a single observation in our training data for each subject (depending on your methodology, the *subject* might be a loan, or a customer). In doing so, we lose a lot of valuable information related to the lifecycle of that subject, and we have less data available to train the model. This same concept can be explained through the mathematical principle of *homogeneity*, which requires that there are no "sub-groups" within the model training data. Conversely, *heterogeneity* refers to the presence of such "sub-group" in a population (e.g., having multiple observations of the same loan or customer across multiple time points).



We argue that it would be greatly beneficial to be able to include additional history about a *subject* when training a credit decision model, particularly models that are dependent on **default** as the outcome variable.



### Forecasting Risk Over Time



Another shortcoming of approaches like logistic regression is that the model output does not vary over a time horizon. This is unfortunate, because we would expect that it takes time for default to occur. As an example, a customer who is newly issued a 5-year term loan may have current balance sheet with strong capital reserves to make the first years' worth of payments, but future cash flow issues the customer faces may cause the business to default two or three years into the note. Like with any forecast, as we look further and further into the future, we should expect an increasing amount of uncertainty around our predictions. Such risk should be priced according to the level of uncertainty, and models that cannot estimate probability *over a forward-looking time horizon* make it impossible to do so.



### Making Risk Estimates Dynamic



When using "traditional" modeling methods, we are often limited to using independent variables that represent the *earliest* data point we have in our database for that subject. This is due to the initial aggregation of the data during model training (as discussed [above](#longitudinal-history--heterogeneity-in-the-data)). In practice, this means that all independent variables which are *random* (i.e, a measurement that should change over time, such as *debt coverage ratio* or *credit score*) -- as opposed to *fixed* (e.g., *industry*, *state / country*, etc.) -- end up representing that measurement at loan origination. Further, because these independent variables are origination-specific, the predictions for a single subject will be *static*; we do not have the ability to update our prediction for the subject, even as we receive new data about that subject over time. Ideally, we would like to be able to continuously, dynamically update our estimated risk for a single subject every time we receive new data (e.g., an updated *credit score*) about that subject. 



## Approaches for Building Better Credit Risk Models



### Aggregated Loss Curves



Though the goal is typically to be able to build models that score individual observations at the *subject*-level, sometimes it is not possible to do so due to the available data or nature of the dependent variable (e.g., the thing we are trying to predict). In this case, taking a more aggregated approach can be the most appropriate course of action. We suggest that building **loss curves** segmented by *product* or *industry*, for example, is one best-practice approach.



  Resources

    * [*Modelling Loss Curves in Insurance with RStan*](https://mc-stan.org/users/documentation/case-studies/losscurves_casestudy.html)



 

### Hierarchical Models



### Survival Models



  Resources

    * [*Bayesian Survival Analysis Using the rstanarm R Package*](https://arxiv.org/pdf/2002.09633.pdf)



#### Hierarchical Survival Models



### Joint Models



  Resources

    * [*Estimating Joint Models for Longitudinal and Time-to-Event Data with rstanarm*](https://cran.r-project.org/web/packages/rstanarm/vignettes/jm.html)