Latest News and Updates

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

The Reason Artificial Intelligence is the Future of Lending


Lending is built on trust

Lending has always been highly driven by personal relationships and the face-to-face meetings – where trust is key to understand if and how a consumer will pay back his/her debt. The trust is traditionally based on a personal relationship between the applicant and the bank clerk – they have gotten to know each other through several meetings at the local bank office. The digitalisation has truly changed how and when consumers are able to apply for a loan and thereby the requirements on the lenders – the consumers do not have the patience to fill paper forms or to wait days for approval. The consumers want a smooth application process and an answer within a matter of seconds. The lender needs to calculate and understand if the debt will be paid back – and thereby understanding if they can trust the borrower in a matter of a second. This blog post we discuss the transformation that lenders will need to go through in order to keep the pace with the consumers and still being confident that they lend to the right people.


Transforming the banking experience

Lenders who haven’t managed to transform into a fully digital and automated application process will have another problem – they will lose customers to the players who have. Based on a survey by Signicat this was obvious – more than one-third of the respondents (consumers) said that they abandoned their loan application as a result of the length and effort required to complete it. Where the requirement of physical papers and waiting-time was the major reason why they abandoned. When consumers come across a frustrating on-boarding will the low-risk and high-risk consumers behaviour differ. Low-risk consumers with good credit who are likely to repay their loans can easily go to another lender. While the high-risk consumers are more willing to jump through challenging on-boarding because they have no other options. Hence, a smooth application process is key for many reasons.

The digitalisation has resulted in several obvious changes when looking to lenders’ way of doing business. During the recent years has the way of marketing changed – going from traditional mails to email campaigns, affiliate networks, agents and social media. Applying for a loan has moved from face-to-face meetings at the bank to become online and mobile. Being able to follow the consumers and keep them satisfied will require the backend of the bank to equally transform. 

Figure 1: The transformation of marketing, application processes and credit underwriting in banking.

The financial companies won’t able to understand all possible customers using the traditional methods. By being able to use more sophisticated credit and risk models applied to more data – the data which you have thought about using but never have – is it possible to find more desirable customers. This can help to reach and understand the ones who were traditionally difficult to score, such as college students and millennials. And since model developed with Artificial Intelligence (AI)  come with automated updates is it possible for lenders to meet changes in the market conditions, new channels (such as mobile) or when the customer populations are reshaping.



Leveraging all data

Today are we acquiring data at a faster rate than ever. A deeper embrace of the AI and machine learning being used elsewhere at the bank, combining with the rich data being harvested, can meaningfully change a financial firm’s profits for good. 

Yet the widespread use of AI to more sophisticated predict credit risk has eluded the industry until today. The early adopters o AI in credit have measured financial performance benefits – through higher conversion rates or lower credit losses, or a blend of both.

Down below is an illustration of some of the effects going from traditional credit underwriting methods to more sophisticated ones.

Figure 2: The effects and benefits of going from traditional credit models to ones based on machine learning and artificial intelligence. 


Fewer resources and more relevance

It can take up to several months to develop, validate, and deploy a new scorecard model. That’s an eternity in the fast-paced digital industry we are living in today. Customer populations, economic conditions, and markets can change dramatically. This could potentially cause introducing errors or human bias into the original model – decreasing its effectiveness. Using AI allows lenders to put powerful models into production quickly and responsibly. Then it is possible to quickly refit new models to adapt to these changing conditions.


Full Explainability

AI algorithms are considered a “black box” where models are unable to explain the reasons for its outputs. We at Evispot has recently developed a software which allows us to fully understand the reasoning and decisions of the AI models. This enables lenders to understand the specifics. Explainable AI models help lenders know exactly why applicants are approved or denied, in alignment with their existing business processes and procedures. (A further introduction of explainable AI-models in credits will be featured in an upcoming blog post).


We think it is obvious the time for AI in credit decisions is now – do you agree?
Please reach out to to continue the discussion.

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Tomas SelldenThe Reason Artificial Intelligence is the Future of Lending


Join our family as a Software Engineer

As of today, we expect banks to provide all their services digitally, we expect to have the possibility to apply for a loan and know if we are granted a loan or not, within seconds, using our phones whenever it suits us. The traditional credit assessment processes won´t be efficient enough for this transformation, and that is why Evispot exists.

We are a group of technology interested people with competence and experience within credit assessment, finance and deep knowledge within machine learning. Our main goal is to ensure that the credit decisions of tomorrow is given in a transparent, ethical and fair manner without compromising the accuracy.

Work at Evispot

If you choose to get on board with us, you will become part of a growing family, where everyone has big responsibilities and the possibility to to make a big difference. It is our core belief that WE are stronger than I. Therefore, we constantly work with common team-goals, where we together find strategic solutions to build the best possible product to help our customers.

As a software engineer at Evispot, you will be part of building and implementing our AI-platform. Among other things you will be part of implementing solutions for the following challenges:

  • Build and design pipelines and software behind the data flows that exist and soon will come
  • Build data-pipelines which can process thousands of features every second
  • Process and structure unstructured data
  • Build integrations to customer through REST API
  • Work close to our machine learning team and together continuously develop our AI-platform

Who you are?

Most importantly, you are interested in technology and handle large amounts of data. You like to, together with other people, find solutions for new challenges. Furthermore, we appreciate if you previously have been involved in projects where data from different sources have been processed and made interpretable for analysis and model development.

Our primary tool is python, we use it for implementing REST API:s, data processing and model development. We host data at our own servers and use dockers and kubernetes for orchestration. It is great if you previously have been working with these technologies but definitely NOT a requirement.

If you think you would enjoy working at Evispot and have the possibility to work from Gothenburg –  it would be amazing to meet you for a cup of coffee to learn more about what you would like to do in the future.

For more information contact Tomas Sellden at with your application and questions.

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Tomas Selldenjoin-our-family-as-a-software-engineer

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

It’s a Platform BOOM! PSD II drives platform banking

Platform Boom: This is the second article in the series of articles on the topic ”Payment Service Directive II (PSD II)”

Platform, is it the buzzword of the century? Perhaps, and even most likely. However, no spark, no fire. A buzz does not emerge from nothing. Some goes as far as to say that in 30 years, 50% of total S&P 500 revenues’ will stem from platform businesses¹. One should always be reluctant to the reliability of such impressive numbers in the future. But, the same bold prognosticator states that over the past 10 years, S&P 500 platform companies have increased net earnings 20 times as much as the overall average of S&P 500. The number of platform businesses are also growing. There is for sure some truth in the prediction.

Exponential growth of platform businesses among the S&P 500 Source: Business Collective, written by the CEO of Applico

Exponential growth of platform businesses among the S&P 500 Source: Business Collective, written by the CEO of Applico

PSD II is essentially the ultimate platform business model enabler in the financial industry. It will decentralise the power of the dominant large banks by making data easily portable and many services, like making transactions, accessible for third parties. In light of the impressive numbers of the profitability of platform businesses, there will be an obvious battle of becoming the platform in the future. This shows through the initiatives of creating a standard API for the PSD II to come.

The classics:

  • Uber, the world’s largest taxi company, owns no vehicles.
  • Facebook, the world’s most popular media owner, creates no content.
  • Alibaba, the most valuable retailer, has no inventory.
  • And Airbnb, the world’s largest accommodation provider, owns no real estate.

Next up?

Will we also add “Company X, the world’s largest bank, provides no credit”? Because with PSD II it is not necessary that the banks are the platform owners.

In the latest years the discussion has been circling around complying to the minimum requirements of PSD II. But the banks should not be overlooked! There are several banks, who have understood the value of the platform business. These actors are rather aiming for providing an API of all kinds of services within the bank. However, the ones outside the scope PSD II not necessary free of charge.

We have previously written about The Open Bank Project, who provides an Open Source API for bank integration. They are essentially building an App store for financial services. It is no bank, they are only building the platform. These solutions will grow with PSD II, and network effects will play out. When put in the hands of the bank’s customers, either companies or persons, a completely new industry of financial service will emerge.

Different Platform Business Models

Now, lets try to concretize the concept of platforms businesses. The two gentlemen Evan & Davids² define it as businesses that act as an intermediary and tie together two sets of different (but often related) actors. The two groups need each other and rely on the platform to act as an intermediator and facilitate transactions between them. In some sense, these platforms enable an exchange of value-creation that otherwise wouldn’t take place.

The companies we used as examples above have gone through extreme growth-phases. However, these powerful, positive growth dynamics makes monetization a complicated matter. The platform guru, Parker, means that monetizing an offering too early can be the death sentence for the platform since it creates friction in the growth. In contrast, not having a thought through plan for pricing makes you set for an unprofitable disaster.

So, how are companies attacking this problem?

One common method to overcome this hurdle is the strategy often called users first, monetisation later. This method were used by Instagram and Facebook in their early days. Meaning that the business focus on building a user base to a critical mass before initiating a monetisation. Another common tactic is taking a transaction fee of each intermediated service, thus taking a cut of the “whole” price.  Examples of this tactic would be Uber and Airbnb. The monetisation strategies of these platforms are many and are highly dependent of the which kind of service of product the platform intermediating.

It will be interesting to see which tactics will be the “winning-one” for the fintech platforms when PSD2 incepts. Keeping the monetisation strategy top-of-mind is important in order to leverage rapid growth when the bell rings.

The company behind the Open Source API is making money on an annual commercial license fee, maintenance and support. The App store’s success depends on an interdependent function of how many banks are integrated with the API (thus potential customers) and the number of apps. More banks will result more apps, and more apps will result in more banks.

Evispot provides the financial industry with the decisions they need in the future. We are developing an AI platform for the financial industry. AI on demand. Easily integrated. Secure.
Get in touch, let’s have a chat!

² Evans, David S., Two-Sided Market Definition (2009). ABA Section of Antitrust Law, Market Definition in Antitrust: Theory and Case Studies, Forthcoming. Available at SSRN:

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Tomas SelldenIt’s a Platform BOOM! PSD II drives platform banking

Changes in the legal landscape catalyze transformation: PSD II

This is the first article in the series ”Payment Service Directive II (PSD II)”

No other industry is in such a rapid transformation as the financial industry. It is not only driven by new technology such as Artificial Intelligence (and all what that implies for credit risk and personalization of financial services), but also changes in the legal landscape such as the introduction of General Data Protection Regulation (GDPR) and Payment Service Directive II (PSD II) in 2018. In this series of articles we will shed light on the PSD II, the EU “Open Banking” initiative. Whereas the first PSD was induced by the rapid changes in the financial market, the second version, PSD II, is set to catalyze and force new rapid changes in the market.

There are many good articles of the implications of PSD II. Instead of providing one more article to the already crowded space, we simply recommend you to read one or both of the following articles: Banking Hub (short and easy) or Whitepaper by Deutsche Bank (detailed).


Here’s a two- word summary: Financial collaboration

Here’s a two-sentence summary: With PSD II banks are obliged to offer the possibility for third-party payment services providers (TTPs) to integrate to banks infrastructure and data through an open APIs. This implies (with consent) at minimum accessing account data and being able to make transactions on behalf of the person.

PSD II enables you to pick your own personalised basket of financial services

PSD II enables you to pick your own personalised basket of financial services

Progress in the field

It is now only six months ahead of the implementation of PSD II (January 2018). From January banks have two years to adjust. The implementation is expected to come in four phases: starting with a minimum viable product (MVP) throughout 2018, and completed in Q1 2019¹. However, several large banks have already begun. Nordea who is paving the way in the Nordics is releasing a pilot test of their open API during the Autumn of 2017. Even though there are many initiatives to harmonize the API such as the recent announcement from the Berlin Group² or the German Open Bank Project, one can expect it to be many different APIs battling to become the standard³. There is an obvious first-mover advantage to begin the development early, i.e. to being able to control the formalisation of the one, the chosen, the API to rule them all.

There are numerous companies that have made a business out of having done multiple integrations with banks. Fidor Bank, Plaid, Instantor, Railsbank and Tink are examples of companies before their time. Throughout time with more harmonized APIs it will become easier to connect to many banks, but it won’t happen instantly.

PSD II and GDPR: A love story

It is an interesting combination the PSD II and the GDPR. Private persons have always owned their own financial data, but for the first time this really matters. Private persons have obtained a control position with the GDPR and the ability to move data easily with PSD II. In other words, banks and financial service providers will need to become much more customer-centric. Because for the first time in history one can swiftly change service provider and pick a basket of financial services from different actors.

We are welcoming the open banking of the future and the tectonic shifts in the legal landscape making it possible. In the next article we are interviewing another company who are actively working with PSD II. Stick around!

In the meanwhile, send us an email or give us a call to hear more about our plans with PSD II and the classification of transactions.

³ See also the dynamic list of open APIs in the financial sector:

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Tomas SelldenChanges in the legal landscape catalyze transformation: PSD II

… 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