Meet CODE_n finalist Kreditech

Sebastian Diemer is the CEO of Kreditech

Kreditech uses Big Data and complex machine-learning algorithms to enable faster and better credit decisions. The technology developed by the German startup identifies and scores individuals online and decides over instantly paid out loans. All based on the processing of 10,000 data points in real-time.

In today’s interview, we talk to Sebastian Diemer, founder and CEO of Kreditech. He tells us how the startup uses technology to provide banking products to customers in emerging markets, allowing unbanked people with no credit bureau history to get credit. He also tells us how they are getting prepared to present their Big Data solutions at CODE_n and CeBIT.

Could you briefly explain what Big Data scoring technology is?

Our technology takes into consideration every piece of information that can be obtained about a person. We then analyze the results with statistical methodologies collected from our self-learning technology. Several thousand individual data points are merged into one big mosaic picture within seconds. It is not about single stones being red or green but the shape of red or green areas, illustrating the importance of coherence among several different data pieces.

What sets Kreditech apart from its competitors?

In comparison to other financial service providers and established credit bureaus, Kreditech only does live scoring. That means that we rate a customer’s creditworthiness by only looking at his or her current financial status instead of historical data. We gather information at the moment the application is submitted. Unlike FICO or Schufa, we neither retain the data afterwards, nor do we sell it. Our unique sophisticated credit scoring process uses the powers of machine learning algorithms and Big Data infrastructure to allow us to analyze more than 10,000 data points in less than one minute. That is what is unique about Kreditech.

What is the role of Big Data in the financial services sector?

That depends on your viewpoint. In the banks’ opinion, there seems to be no reason to focus on fast and sustainable scoring. We at Kreditech, on the other hand, are convinced that what matters is the customer’s current financial situation and not their past. We have the notion that fast and reliable credit scoring brings benefits for both sides. The customer receives his money within minutes and we as a service provider can minimalize our risk by only accepting solvent applicants. Therefore, basically, Big Data is the future of sustainable and reliable banking.

In which emerging markets do you see the most potential for your product?

Right now, we are operating in Mexico, Spain, Czech, Russia, and Poland and have recently launched into Australia. Poland is already profitable on a full-cost basis, which is great and shows to us the potential for future operations and market entries. The term “emerging markets” is not 100 percent correct, as we are focusing on consumer finance emerging markets. This means, that even in highly developed countries, the market situation for retail banking and consumer finances is still under-developed.

We are focusing right now with special interest on the LATAM markets, as well as different countries in Asia. Latin America is highly interesting and a very promising market for innovative financial services, such as those that we are planning to launch during the next months. Prepaid credit cards, eWallets, installments, and risk-based pricing—everything needed to provide sustainable and lasting financial services.

What are your expectations for CODE_n?

CODE_n and CeBIT itself are well known as big and very thriving gatherings for young, innovative, and open-minded companies and entrepreneurs. This will be, like the years before, an inspiring experience and a great opportunity for exchanging thoughts and views on what the future in different market sectors has to offer. I really looking forward to learning, sharing with others, and especially getting in touch with companies outside the financial sector.