Teknik

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

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

Credit Scoring on Anonymized Credit Data using Machine Learning

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

Kvar-att-leva-på-kalkylen (KALP) är pånyttfödd!

För drygt tre veckor sedan annonserades det att H&Ms dom 529-16 om bristande kreditprövningar för konsumenter, nu tas upp i högsta instans. Detta beslut hade tidigare hävts i Kammarrätten den 19 oktober 2016. Fallet har nu pågått i snart tre år, sedan Konsumentverket (KO) startade utredningen för att motarbeta överskuldsättning i samhället. Om Konsumentverket får rätt i sak, kommer stora förändringar att följa för hela kreditbranschen, men för fakturakrediter och kontokrediter i handel och ehandel i synnerhet. KALP har fått en pånyttfödelse. 

KO menar att H&Ms kreditprövning har allvarliga brister i att förstå en konsuments totala betalningsutrymme. De sopar undan H&Ms argument om låga kreditförluster då de säger att det bara betyder att H&Ms kunder betalar just H&M. En kreditprövningen som tar hänsyn till konsumentens betalningsutrymme måste ha färskare inkomstsiffror, samt kostnader och andra skulder, hävdar KO och föreslår att kreditgivare måste samla in dessa uppgifter.

H&M svarar att det skulle försämra kassaupplevelsen, men även deras reviderade kreditpolicy blev avfärdad av KO. Motivering var att H&M använt KOs egna beräkningar för hushållskostnader som används i andra sammanhang.

Hur ser Evispot på KALP-kalkylen?

Vi på Evispot följer utvecklingen gällande KALP och vakar efter nya uppdateringar. I takt med att mer information släpps arbetar vi för att förstå hur vår AI kan bistå. Ett frö för en teknisk lösning är sått i Evi-LAB. Lösningen handlar om att låta kunden bekräfta föreslagen information istället för att fylla i allt från grunden. Genom att minska antalet steg för ett köp, ökar konverteringen. Det är allmänt känt. Ambitionen är således att skapa en smidigare kassaupplevelse.

Har du egna tankar eller är du intresserad av denna lösning är du varmt välkommen att kontakta oss på hello@evispot.com

(Sorry english speakers, this is regarding a Swedish regulation)

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Tomas SelldenKvar-att-leva-på-kalkylen (KALP) är pånyttfödd!