When your organization transitions to machine learning credit underwriting technologies, it is very beneficial to use an automated risk management process. It will help your organization keep up with changes in the market, being able to explain your business to regulators and make your organization more efficient. So what should you think about when going for an automated risk management process?
1: Document the data
It all begins with data. It is important to track, analyse and measure all the data that has been used to create the model. Understanding why each feature is included in the model and how missing data is imputed. We also recommend to document every model you have tried during model development, along with its performance and results.
2: Understand model – technically
Before deploying your machine learning credit underwriting model into production, you must understand how your model operates. This includes understanding the global variable/feature importance and how the variables interact with each other. Is your model trained on data which includes specific policy rules? Then you have to know your model’s blindspot and weaknesses. At last what is your performance metric for scalability, how many loan application can you model handle in a live environment?
3: Understand model – businesswise
The economic impact of the model must be quantifiable too, and questions such as:
How does the model impact revenue?
Will the model change approval rate or default rate?
How does the model affect the distribution of scores?
Without having these answers, the model you put into production can behave very differently compared to the test results you have.
4 Monitor your model
A robust risk management process, will help you minimise the number of surprises a model put into production can have. However it cannot completely eliminate the unknown. Therefore it must exist a process where you can quickly validate and follow the model and have the possibility to quickly make changes if something goes wrong. At top of that, monitoring the model’s variable distribution over time can help you alert market changes, foresee the next macroeconomic shock and have a competitive advantage during rough environment
An Automated risk management process is a must for financial institutions when utilizing the benefits of artificial intelligence in credit underwriting. Following these best practices will help you ensure that your model is powerful, accurate and robust. Allowing you to to identify better borrowers, which artificial intelligence in credit underwriting brings.
Are you ready to use artificial intelligence in combination with a robust risk management process?
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