Toby Hobson

Case Study: Kreditech

Kreditech is a fintech business, operating primarily in emerging markets. They use advanced analytics and machine learning to model affordability and credit risks.

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Kreditech

Kreditech is a fintech business, operating primarily in emerging markets. They use advanced analytics and machine learning to model affordability and credit risks.

Key Features

  • Machine learning used to build credit profiles
  • 30+ micro services
  • Deployed in multiple markets

My Role

I was responsible for the risk and credit scoring element of the platform. We employed machine learning to make affordability and credit worthiness decisions in the absence of traditional credit rating reports. The data scientists produced models in R, and I was responsible for bringing them to production. I worked closely with the product managers to understand the business drivers and KPIs.

The Challenges

Kreditech operate in many markets. The regulations vary between countries. I needed to build a platform which could be quickly adapted to new markets.

The Solution

I went for a micro services approach as this allowed me to quickly swap out services if needed. We also performed split A/B testing to understand which models performed best. The machine learning models themselves are implemented in R and deployed using RServe. Other services are built in Scala, mostly using the Akka framework.

  • R/RServe
  • Scala (Akka stack)
  • Docker & Kubernetes

What did I learn

People are more important than processes. In contrast to many Agile environments we adopted a very lightweight process. We held one team meeting per week, we didn’t follow Scrum or Kanban, and we didn’t write user stories.

Conventional wisdom would say this is a recipe for a disaster, but we were successful. Why? because we had a very close relationship between the product managers and technical teams. I fully understood the direction the business was heading in and was able to make technical decisions to support this.

We also adopted a federated team structure. In essence, the sales team had a group of developers to support them; the risk team had the same. The platform as a whole worked well because each team operated independently and offered a “service” to other business units. The teams were highly focussed and understood the business goals and technical implementations well.

This structure also allowed the business to scale up quickly. When we wanted to introduce a new product we could pick from a range of in house services. We had confidence that the service we were using were solid, and the people behind them understood them well.

The Results

Revenue
€181m
Loans issued
Products
3 new
financial products launched
Markets
4 new
markets served

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