Credit underwriting

Three Reasons Why AI Enables You to Find Better Borrowers

It is no secret that Artificial Intelligence (AI) improves credit underwriting – however, we often receive the question why AI improves credit underwriting. This blog post includes three reasons why AI outperform traditional credit underwriting (e.g scorecard) development.

1: Linear vs Non-Linear Models

When using AI, you use non-linear models compared to linear models which today are the most common models used in underwriting.

Above you can see two pictures both including bad loans (blue points) and good loans (green points). When using a linear model, a straight line is used to differentiate a good/bad loan. As can be seen in the left picture is it impossible to draw a straight line capturing all good loans on one side and all bad loans on the other side. Which is more easily done with the non-linear model.

To be more concrete, let’s say we have the variable age and we can see that people over 35 years old are in general better payers compared to ones below 35. This information can be used in a linear model. However with a non-linear model we can go deeper into the data. A non-linear model enables us understand that not all under 35 are necessary bad payers –  by combining age with where you live, it could actually be positive to be below 35 compared to being over 35. By using a non-linear model we can capture these rare cases by looking at variables in combination and thereby find better borrowers.

2: Change-over-time vs Point-in-Time

Most credit underwriting models use a point-in-time solution, meaning that you analyse questions such as: How many payment defaults have you had the last 3 years? or How many bankruptcies have you been involved within the last 2 years? These answers and predictors are very useful but limited, since we don’t have the ability to understand trends over time. By using change-over-time solution, you can also analyse when the payment defaults occured or when the bankruptcies occurred.

If you think about it who would you prefer to grant a loan to?

A borrower who missed a few payments three years ago but has had a perfect record ever since or a borrower who has never missed a payment until the past few months, and missed a bunch in a row?

3: Vastly More Data

Artificial intelligence is an extremely good technology for analysing huge amounts of data and understanding variables from different sources and with different distributions. In order to concretise how vastly more data enables higher accuracy will we use an example below. In the example will we build a simple model that will determine if a person is a man or a women.

The first variable we choose is height, since men (in general) are taller than women. This is not true for everyone since it exist short men and tall women. Therefore we choose our next variable as weight, since men in general are heavier than women. The problem is that now our model thinks that all kids are women, which of course not is true. Therefore the third variable will be age, and our model become quite accurate.

If I had asked you if age could be used for determining gender one minute ago, you would probably have said that we were going nuts. Since, only age is a terrible predictor when trying to understand a person’s gender. When you are using a linear model such as logistic regression, the model itself will interpret each variable separately and a linear model will give you an answer which is not correct when using variables that are dependent on each other. Today when developing scorecards, this problem is solved by calculating the importance of each variable and then you are able to use these variables together.

However if you would have used 200 variables it would be impossible to put a scorecard on top of all those variables unless you have a huge analytics team that works day and night for creating one scorecard.

Artificial intelligence enables you to get this correlation for free, the model itself understand that age is only a good variables when it is combined with weight and age. The best part is that AI can understand these relations when you are working with hundreds or thousands of variables – resulting in a credit decision model that truly understands payment behaviour in the details and enables you to find better borrowers compared to traditional techniques.

Transparency: The Real Challenge

Utilizing AI in your credit model is hard but manageable. The real challenge is that an credit model has to be understood so it can pass your existing risk management committee and compliance requirements. This requires you to dig deep into the complex math behind the AI models –  my recommendation is to find a partner who already has addressed these types of challenges before since it is really time-consuming, math-heavy and complex processes. At Evispot we have developed Evispot Traits, a solution customized to give creditors the transparency and explainability required to take an AI model into production.

If you’re ready to capture the benefits AI can give your credit risk models, let’s have a chat! Contact to learn more.

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Tomas SelldenThree Reasons Why AI Enables You to Find Better Borrowers

Two lessons learnt to take your AI from idea to production

Are you interested to start using artificial intelligence (AI) in your credit underwriting business? Here are two lessons that I have learnt, which might help you convince your organization to use AI.

Artificial Intelligence improves credit scoring, the question is how much the impact will be on your business?

It is no secret that AI improves credit underwriting. How much it will affect your business is impossible to answer before trying it –  since it depends highly on the AI technology and what data is used.

There exist several options to understand how much AI will improve your specific business. One classic method is to run a proof-of-concept. The proof-of-concept path involves building an AI model, comparing the AI model performance to your current model, and analyzing the incremental business impact that the AI model delivers. If you are experienced within AI you can try it out yourself, you can hire people that are experts within this field or sign-up for our pilot study. Either way, you will ultimately discover that AI underwriting will add significant value to your business.

Deploying the AI model into production is the hard challenge

There are several challenges to accomplish before deploying an AI model into production. Including IT-Integrations, regulatory and compliance issues. In my experience, we have found suitable, low-friction IT solutions that are agnostic to whether a lender prefers to run its solution on-premises or in the cloud.

The real challenge is model transparency, a credit underwriting model has to be understood so it can pass your existing risk management committee and compliance requirement. This requires you to dig deep into the complex math behind the AI model –  my recommendation is to find a partner who already has addressed these types of challenges before since it is really time-consuming, math-heavy and complex process. At Evispot we have developed “Evispot Traits”, a solution customized to give creditors the transparency and explainability required to take an AI model into production.

If you’re ready to capture the benefits of more accurate and transparent credit risk models, let’s have a chat! Contact to learn more or to sign up for a pilot study.


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Tomas SelldenTwo lessons learnt to take your AI from idea to production

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 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:

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

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Tomas SelldenHow AI improves credit underwriting