Artificial Intelligence

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

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

Evispot helps Ativo take smarter decisions and decrease the number of manual decisions

In the beginning of 2018 Ativo Finans integrated their IT-system to Evispot’s credit decision platform. Since integration Ativo Finans has used the platform to take smarter credit decisions and decrease the number of manual decisions.


We would definitely recommend Evispot. From our very first interaction to onboarding, it’s always been a great experience. It’s an excellent credit decision platform and I’m glad we chose Evispot.” 

– Erik Finné, CCO/CIO at Ativo Finans


About Ativo Finans

Ativo Finans is a Swedish finance and credit company providing factoring services and loans to Swedish small and medium sized companies. They deliver high customer value through digitized, automated operations combined with personalized customer support. Ativo Finans also provides factoring services under the brand Factoringgruppen.


About Evispot

Evispot is a credit decision platform that helps you as a creditor to truly understand your customers. With the power of artificial intelligence, the platform reveals the complete picture of your customer behavior, enabling you to do more accurate credit decisions.

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Tomas SelldenEvispot helps Ativo take smarter decisions and decrease the number of manual decisions

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

Evispot Traits – Transparent and Explainable Artificial Intelligence

It is no secret that Artificial Intelligence (AI) improves credit underwriting. It allows creditors taking advantage of the vastly more data and switching from point-in-time solutions to change-over-time solutions – resulting in credit models with higher accuracy than ever before. With a traditional, point-in-time, model is the applicant’s number of historical payment remarks calculated and measured. With an AI, change-over-time, model is it possible to understand the trends and spot the differences between applicants who have gained payment remarks recently compared to the ones who got them several years ago. Hence, AI allows creditors to identify the difference between the applicants who are on an upgoing and downgoing trend in their creditworthiness.  Applicants who otherwise would have been seen as the same. This is only one example of how AI allows creditors to identify risk more accurately.  


So, why hasn’t AI been highly adopted in credit underwriting?
Due to a regulated industry and since AI-models has historically been black-box solutions.



Demystifying the Black Box

We at Evispot have decided to transform credit underwriting by using the power of AI combined with transparency and explainability. Our mission is to deliver solutions which elevate creditors to new heights – our customers should be able to meet the demands of transparency while still having the accuracy and performance of AI.

At Evispot we have demystified the black-box, through three steps: transparency, explainability and provability.

Figure 1: Breaking down the black box in three steps. 

Transparency, explainability and probability are our three technical key indicators helping us and our customers understand the full spectrum of how the model reasons, predicts, and – simply put – works. Transparency is about the AI-models high-level performance, the holistic view of its characteristics to help the creditors understand both the impact of the AI-model and the reasoning behind the model. Explainability is about helping the creditor understand each decision separately. Lastly, provability is used as a quality measurement of each prediction, to ensure the highest possible quality in each credit decision.



Introducing Evispot Traits

The solution we have developed is called Evispot Traits. When Evispot Traits sees an AI-model it returns a transparent and explainable description of the model. An AI-model is trained on a set amount of historical loans, good and bad ones with a set number of variables for each loan. The model has learnt itself which variables and combinations of variables that are significant for good and bad payers. Evispot Traits calculates how much a specific variable contributed to an actual historical event.  This allows us to create a score of each variable’s contribution to each historical loan.

This number is called Evispot Score, it varies between -1 to 1 and each historical application score is plotted as a dot (see figure 2 below). The horizontal position of the dot is the impact of that feature for that specific application. A dot skewed towards the left – a negative value –  increases the probability of default. While a dot skewed to the right – a positive value – decreases the probability of default. By calculating and plotting the  Evispot Score for each variable and for each loan it is possible to calculate and visualise the overall model performance. Evispot Traits makes it possible to granularly understand how the AI-model reasons and how it determines the risk of each loan application.


Figure 2: Illustrating three variables that the model is using for predicting. Please note that the models that a full overview of the model’s Evispot score is listed at the end of this post.


By looking closer to the variable Annual income, it can be seen that the AI-model has seen four distinguished intervals. Missing value, below 34800, between 34800 – 218000 and above 218000. By looking at the Evispot score it can be clearly seen that applicants with missing value or less annual income than 34800 are defined as the riskiest group, while above 34800 is seen as a less risky group. By plotting Evispot Score it is possible to visually identify the models characteristic. By using this method it is possible to understand how age, salary or other variables correlate with risk. It helps us to better understand and trust the model performance and robustness.



Understanding Each Decision

When a new applicant enters, we calculate the Evispot score for this applicant and thereby give you as a creditor the possibility to truly understand the model and the reasoning behind each decision.

Figure 3: Illustrating how the model has reasoned when predicting a new application (red dots), which helps us understand why the model predicted the way it did.

By using the same example as used in figure 2, a comparison between the incoming applicant with the historical applicants can be made. By plotting the new application with red dots it can be seen that this applicant’s Annual income is skewed towards high risk. The applicant’s  Account Balance, as well as Delinquencies, are skewed to low risk. The Evispot score of every variable gives us a complete picture of how and why the model predicts as it does. All in all, the benefits of Evispot Traits allows you as a creditor to gain the benefits of AI in your credit decision scoring while still meeting the demands of transparency and explainability.


Interested in learning more about Evispot Traits?
Please contact or call +46(0)70-2332335

 The Model’s Characteristics and Evispot Score for all Variables

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Tomas SelldenEvispot Traits – Transparent and Explainable Artificial Intelligence

We are hiring – Machine Learning Engineer


Evispot provides the financial industry with the decisions they need in the future – today. We do it by exploring artificial intelligence – because no human, neither configured software can handle the complexity of data that must be evaluated.


With 15 years of experience in the credit industry and a strong domain technology interest, we realized the opportunities AI entails in credit decisions. Traditional decisions models are based on a defined amount of parameters. A defined amount of parameters entail limited answers. The possibility to identify patterns, anomalies and multidimensional relationship has not been possible before – until now. The era of traditional credit models is over. New, constantly optimizing, models are the future.


Evispot is looking for a machine learning engineer to join the team, to take part in the product- and technology development and creating excellent products for the financial industry. You will be a part of disrupting the financial industry and creating next generation credit decisions.

– Take part of Evispot current technology- and product development.
– Develop solutions for real world, large scale problems.
– Hands-on prototyping new algorithms, evaluate with experiments.
– Also, productionize solutions at scale or/and plan for scaling.
– Help drive optimization, testing and tooling to improve data quality.
– Take part of an active start-up who constantly strives to create excellent products.
– Work closely and collaborate with Evispot’s advisors with over 10 years of AI experience.
– Evispot’s office is located in Gothenburg, Sweden.
– Flexible hours and all other startup-related clichés you might desire.


– Background and experience in machine learning or a related field.
Experience of feature engineering and prototyping machine learning applications, that have been deployed to production
– You are well-versed in programming and scripting (not only R and Matlab)
– Experience implementing machine learning systems in Python and R.
– You care about agile software processes, data-driven development, reliability, and disciplined experimentation
– Grit, and a true problem-solving mindset
– You are excited about joining a startup environment where anything & everything is happening at once.


Please contact Tomas Sellden at with your application and questions.

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Tomas SelldenWe are hiring – Machine Learning Engineer

Evispot Articles: Case #1 – Detecting ​​Default ​​Payments ​​of ​​Credit ​​Card ​​Clients using Machine Learning

Hope you have had a great summer! While many have been lying on the beach and enjoyed vacation, Evispot has worked on interesting machine learning cases. 

During the upcoming weeks we will share our experiences in a serie of articles. The idea is to give you insights and a deeper understanding of machine learning in credit scoring. The articles follow Evispot´s development process going from raw data to actionable insights – and our hope is that you will share our view: 

Machine learning is the future in credit scoring

Case#1 –  Detecting ​​Default ​​Payments ​​of ​​Credit ​​Card ​​Clients using Machine Learning

The first article is about predicting default payments of credit card clients, using demographic data, credit data and history of payment bill statements of credit card clients. In this example, we use logistic regression to briefly demonstrate a classification problem, and how classification can be more useful than just predicting the outcome variable.

Follow the link to get to the article:
Case #1 – Detecting ​​Default ​​Payments ​​of ​​Credit ​​Card ​​Clients using Machine Learning 

If you have any questions or data you want to explore feel free to contact:, CEO & Co-founder Evispot


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Tomas SelldenEvispot Articles: Case #1 – Detecting ​​Default ​​Payments ​​of ​​Credit ​​Card ​​Clients using Machine Learning

… and the winners are! Evispot AI Challenge

Most Accurate Solution 

1. EZW (70,3%)
By focusing on the core problem EZW was able to developed a superior solution compared to the competition. EZW had a structured pre-processing and used random forest algorithm which resulted in the winning solution.
You now have the right to brag about your machine learning skills! 

A big congrats, well deserved! You are now going to tell Bisnode how they should implement machine learning.

2. Real Human Beings (69,1%)

Well done, Real Human Beings! A well deserved second place! Congratz. You will get azure passes!

3. Klubb Bubbel (64,7%)
Congratz! You will also get azure passes!


Most Innovative Solution 

Deep Engima
Many of the solutions followed a similar processes in terms of pre-processing and choice of algorithm – but Deep Enigma stood out in terms of application area. Deep Engima is winner of the most innovative solution – by proposing a suitable & creative application area of your solution! Congratz!

You are winning a night of beers with Evispot – let’s talk about how to develop your proposed solution!

Once again, a big thanks to Mikael Kågebäck, Christian Lauritzen and David Fendrich in the jury and to our partners! 

Congratulations to the winners! 

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Tomas Sellden… and the winners are! Evispot AI Challenge

Evispot AI Challenge: Powered by Bisnode – has now started!

Yesterday, Evispot kicked off the Evispot AI Challenge, a five day long machine learning competition, co-organized  with Bisnode and Microsoft. With just over 20 teams participating in the challenge Evispot invited the teams to a night of inspiring talks, food & drinks and introduction to the challenge itself. Besides Evispot, we listened to talks from David Fendrich of Crawlica under the theme “Why can we predict anything at all?” and Robert Quinn of IBM under the theme “Did you say Let’s eat Grandpa or Let’s eat, Granpa?”. Now the participants have until Tuesday to complete the case – we are looking forward for the results!


Photo: Fabian Wennerbeck 
Joachim Karlsson, Group Director of Innovation, Bisnode

Joachim introduced Bisnode and their view of the Evispot AI Challenge as well as highlighted the importance of machine learning in the future of credit decisions.



Photo: Fabian Wennerbeck 
”Why can we predict anything at all?” by David Fendrich, CTO Crawlica

David started off by saying that he would give a technical speech, not to talk about business at all. And he certainly did, David speech focused around the Solomonoff induction, Kolmogorov Complexity
and gave some valuable tips of how to tackle AI problems. You can find David’s slide here. 

Photo: Fabian Wennerbeck
“Did you say Let’s eat Grandpa or Let’s eat, Grandpa?” – by Robert Quinn, Senior Software Engineer, IBM

Robert talked about how IBM Watson cognitive journey started with playing Jeopardy. By building a Jeopardy AI-robot, with the two requirements, it should answer within one second and at least be 85% sure to answer the correct answer.

Photo: Fabian Wennerbeck 
Isak, CFO & Co-Founder, Evispot
Isak highlighted the importance of the challenge due to the upcoming regulatory changes in the financial industry.

Photo: Fabian Wennerbeck 
Tomas, CTO & Co-founder,  Evispot
Tomas introduced the challenge in details and declared the challenge’s start. 


Below are more pictures from the evening.

Photo: Fabian Wennerbeck 

Photo: Fabian Wennerbeck 

Photo: Fabian Wennerbeck 

A big thanks to everyone who showed up and had a great evening with us! If you have any questions or comments don’t hesitate to contact us – 

Now we are looking forward for the results!

A special thanks to Fabian Wennerbeck who took the pictures!

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Tomas SelldenEvispot AI Challenge: Powered by Bisnode – has now started!