Technology

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

Tomas Sellden oktober 23, 2018

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

Tomas Sellden oktober 9, 2018

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

join-our-family-as-a-software-engineer

Tomas Sellden augusti 22, 2018

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 tomas.sellden@evispot.ai with your application and questions.

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

We are hiring – Machine Learning Engineer

Tomas Sellden november 27, 2017

WE

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.

WE + YOU


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.

YOU

– 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 tomas.sellden@evispot.ai 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

Tomas Sellden augusti 21, 2017

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:

Sebastian.Larsson@evispot.ai, 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

Tomas Sellden maj 22, 2017

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!

Tomas Sellden maj 12, 2017

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 – hello@evispot.ai 

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!

Credit Scoring on Anonymized Credit Data using Machine Learning

Tomas Sellden maj 10, 2017

The financial industry has a tradition of data-driven decisions, but uses only a fraction of the available data. Financial companies generate vast volumes of data in their credit operations, which is scarcely feeded back into the operations. From an international perspective, financial companies in Sweden have access to good quality external data. But, there is much to by exploring machine learning to create new insights from the data generated by the credit operations. 

 

2018 – The Year of Regulations 
Apart from recent advancements in the field of machine learning in credit scoring and rating, 2018 will be a year of major regulatory changes. These regulatory changes, in combination and alone, will become highly important for the implementation of machine learning algorithms in the financial industry. The first regulation, PSD II, will make financial data available through standardised APIs for third parties allowed access by the owner of the data. The second regulation, GDPR, will strengthen the control of the owner of the data (only for physical persons) have on their personal data. The combination of giving stronger control to data owners and opening up the traditionally closed systems expose many opportunities for new entrants. With GDPR the use of personal data in business will be stricter, making anonymisation of data more relevant than ever. In the setting of anonymised data machine learning will play a crucial role to find patterns and anomalies useful for credit assessments.

 

The Evispot AI Challenge 
Together with Bisnode we are exploring next generation credit decisions by using machine learning on anonymised transaction data. We took it one step further by organising the Evispot AI Challenge, where we asked students of Chalmers University of Technology to help us find innovative solutions.

Joachim Karlsson, Group Director of Innovation at Bisnode, on the collaboration:
”Industry leaders are choosing Bisnode to help them to work better and innovative, to create growth and to find new opportunities using smart data. Our collaboration with Evispot is a way to let innovators take part of Bisnode smart data. Historically, we have positive experience in similar collaborations and we believe there are many good ideas in Evispot’s network and we want to support that.”  

 

This case reflects one out of many opportunities in using the internal data in credit decisions. The case sets the ground to make way for identifying purchase and payment behaviours to be used in the credit assessment of granting new debts.

Want to take part of the results and insights?  
Fill in your contact details in the following link and we will get back to you:
Follow this link

Do you have any comments, thoughts or questions around this theme?
Don’t hesitate to contact us on hello@evispot.ai or at twitter.

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Tomas SelldenCredit Scoring on Anonymized Credit Data using Machine Learning

Extract Insights from Your Data

Tomas Sellden april 11, 2017

 

We are living in the data age. Every device around us is constantly assessing, calculating and interacting – resulting in enormous amounts of data constantly being created.

The term big data has been frequently used during the last decade. The term is used to describe the collection and analysis of data on a scale or of a complexity that makes the use of data challenging.  Extracting insights and value of these massive datasets is challenging, but when done properly you will find yourself increasingly efficient.

The UK house of commons wrote a study named The big data dilemma on the massive increase of data. The study highlights the importance of taking action and using the data to create valuable insights for your business. To add some perspective: in 2014 204 million emails were sent and every minute 4 million Google search queries were done. Even more astonishing is that 90% of the data currently in the world was created in the last two years and the amount of global data is predicted to grow 40% year on year for the next decade.

– So what can you do?                                     

The credit scoring giant Experian believes the role of big data in financial service is huge, naming fraud detection and general ledger data to gain previously impossible insights as some of many of possible products. Experian states that enabling  ‘real time’ data-decisions is the difference between winners and losers in the financial markets.

This data explosion offers a great opportunity of understanding your environment and peers on a deeper level. Properly exploited, this data should be transformative, increasing efficiency, unlocking new avenues in life-saving research and creating yet unimagined opportunities for innovation. However, the current datasets are nowhere near fully exploited despite research showing that data-driven companies being 10% more productive than those that do not operationalise on their data. Today an estimated 12% of all data analysed, meaning there is yet much to explore.

– Extracting insight and value from these massive data sets is difficult, but when done properly it can be the difference between winning and losing. Evispot was founded on the mission of being experts of insight-extraction in the data age.

Want to learn more about how your business can benefit from data?
 Check out the Evi-LAB – our environment for insight extraction.

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Tomas SelldenExtract Insights from Your Data

Lansering av Evi-Lab

Tomas Sellden april 11, 2017

Evispot.AI lanserar den intelligenta testmiljön Evi-LAB som hjälper finansbranschen att ta smartare och enklare beslut

Tomas Selldén, Isak Unfors och Sebastian Larsson

Evispot.AI, backat av Chalmers Ventures och Invativa AB tar sikte mot att utveckla nästa generations kreditbeslut. I utvecklingen av en AI-beslutsmotor för finansbranschen, lanserar de nu testmiljön Evi-LAB. Syftet är att minska tröskeln för att skapa smarta och enklare beslutssystem för framförallt kreditgivning och kredithantering genom initiala tester i en säker labbmiljö.

Till skillnad från dagens system så ska Evispots modeller optimeras och uppdateras i realtid. De stora volymer finansiell data som idag genereras vid kreditgivning ger upphov till en enorm komplexitet som varken människa eller konfigurerad mjukvara kan hantera, desto mindre i realtid. Grundarna av Evispot bestämde sig för att underlätta för finansbranschen genom att utvinna information som gömmer sig i de stora mängder data med AI och göra den tolkningsbar.

”Det är viktigt att jobba systematiskt när det kommer till att utveckla smarta och automatiserade beslutssystem. Evi-LAB handlar om att validera möjligheterna från faktiskt data. Det handlar om att bygga från evidens” – säger Sebastian Larsson, medgrundare och VD.

”Under 2018 kommer två lagar med stora implikationer för finansbranschen att implementeras, PSD II och dataskyddsförordningen. Detta kommer att förändra hur och vem som får användning av denna data. Vi positionerar Evispot långsiktigt i de snabba rörelserna, med nya samarbeten och den senaste teknologin för kreditbeslut” – säger Johan Kohlström, medgrundare och grundare av Invativa AB.

Gå in på www.evispot.ai för ett evidenstest i Evi-LAB eller spana in projekten som är på gång.

Kontakt:
Sebastian Larsson, Medgrundare och VD Evispot.AI
sebastian.larsson@evispot.ai
070-233 23 35

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Tomas SelldenLansering av Evi-Lab