Credit underwriting

4 tips for establishing an automated risk management process 

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?

Contact partnership@evispot.ai for more information

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Tomas Sellden4 tips for establishing an automated risk management process 

Evispot’s AI-platform has been further developed to ease the SME credit assessment

It is well-known that a lot of SMEs (small-medium enterprises) struggles to find financial help. To apply for a loan, a lot of administration work is required and the final result is either a rejected loan or a loan offer with huge interest rate. Meanwhile, it is a huge market, only in Sweden it exists about 1,5 million companies and 99% of them are less than 50 employees.

So how come such a big market has issues finding capital?

One reason is that it is very difficult to understand if a company is well-being or not. There are many factors to consider and a lot of data to go through and analyse. Since there are so many perspectives to consider, credit assessing companies has always required a lot of administration and manual work. The result is that many SMEs are having a hard time finding suitable financial help and the few companies that are granted loans have to pay huge interest rate, since the cost for manually assessing the loan application is high.

With access to more data and technology to analyse and understand data, there exist tools which can ease  the credit assessment for the creditors.

Do you want to know how the Evispot AI-platform can help you ease the credit assessment of company credits? Contact: partnership@evispot.ai

The Evispot AI-platform is based on a complex array of scoring algorithms developed on hundreds of thousands of data points from different sources such as accounting data, annual reports, industry specific trends and can include your bank´s historical data to ease the job for a creditor to understand if a company is good or not.

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Tomas SelldenEvispot’s AI-platform has been further developed to ease the SME credit assessment

How AI improves credit underwriting

How Artificial Intelligence (AI) Improves Credit Underwriting

How can you as a creditor increase your lending portfolio without adding risk?

The answer is simple: Find the good borrowers which traditional credit scoring techniques oversees and rejects. And they do exist –  traditional methods, such as logistic regression, has a hard time accurately classifying some populations, such as those with little credit history or people who have had past credit issues.

Traditional credit agencies usually categorize a applicant into a risk group between 1-10, which is a good indicator for many of the applicants. However each risk category will have some applicants who are misclassified –  which should be classified in a higher or lower risk class. In sense, just because a person has a low credit score from a credit agencies it does not necessarily mean that he or she is a bad payer. And it goes goes to other way around as well, high scores doesn’t  necessarily mean you are a good payer.

By utilizing the power of AI we can get a complete picture of your applicants. Using more data, and more importantly, by understanding and capturing the interactions between the data is it possible to understand who will pay back and not. Resulting in more accurate credit decisions for you.

As can be seen in the picture above, an AI model will swap some of the borrowers to a higher risk class and some to the lower risk class. The end result is a credit decision model which helps you to either more approvals without increasing risk, or less risk with the same approval rate.

Evispot’s AI-platform help financiers to make use of more profitable AI credit models. If you are ready to take the next step contact us at partnership@evispot.ai

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Tomas SelldenHow AI improves credit underwriting