Tink Onboards Customers in a Couple of Days Using AWS

2020

Open-banking platform Tink builds, trains, and deploys machine learning models in hours and onboards customers in a couple of days using AWS. The company provides a platform to banks, fintechs, and startups, which allows them to offer personal-finance-management tools and other services. Tink runs its platform on AWS and has introduced Amazon SageMaker to improve the efficiency of using machine learning.

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Using Amazon SageMaker, Tink saves time training its ML models. In hours, engineers can build, train, and deploy ML models that reach millions of end users.”

Mattias Appelgren,
Technology Lead for Infrastructure, Tink

 

Enabling New Banking Services

Stockholm-based Tink is a leading European open-banking platform that enables banks, fintechs, and startups to develop data-driven financial services. Under the European Union’s second Payment Services Directive (PSD2), banks must allow third-party providers (TPPs), which include fintechs and startups, to access account holders’ transaction data through open application programing interfaces (APIs).

Through one API, Tink’s clients can access aggregated and enriched financial data, initiate payments, and build digital banking experiences, allowing them to gain new customers and retain existing ones.

Focusing on Customer Value

Tink’s platform runs on the Amazon Web Services (AWS) Cloud. Mattias Appelgren, technology lead for infrastructure at Tink, says, “We chose AWS because of its managed services, so that instead of hiring an entire infrastructure team to maintain our IT services, we could focus on delivering value to our customers.”

Onboarding Customers in a Couple of Days

Using AWS, Tink provides banks, fintechs, and startups with a dedicated environment on the Tink platform in a couple of days—a process that could take weeks or months with an on- premises environment. Appelgren says the speed comes from the ease of use of AWS, and he gives the example of working with the Amazon Relational Database Service (Amazon RDS). “You don’t need database expertise to use Amazon RDS. Our developers simply write code and connect to an Amazon RDS instance. Working this way streamlines onboarding and services development.”

To improve performance, Tink uses AWS Lambda, a serverless computing service, and has written Lambda functions to enforce policies for improving the utilization of its Amazon Elastic Compute Cloud (Amazon EC2) instances.

Building, Training, and Deploying ML Models in Hours

Tink uses Amazon SageMaker to train machine learning (ML) models that categorize the payment transactions that are aggregated from banks in Europe. SageMaker allows Tink to experiment with ML models that can reveal new patterns in bank transaction data to improve categorization services for customers.

Appelgren says, “Using Amazon SageMaker, Tink saves time training its ML models. In hours, engineers can build, train, and deploy ML models that reach millions of end users.”


About Tink

Tink is Europe’s leading open banking platform that enables banks, fintechs and startups to develop data-driven financial services. Through one API, Tink allows customers to access aggregated financial data, initiate payments, enrich transactions, verify account ownership and build personal finance management tools. Tink connects to more than 3,400 banks that reach over 250 million bank customers across Europe. Founded in 2012 in Stockholm, Tink’s 400 employees serve more than 300 banks and fintechs in 18 European markets, out of offices in 13
countries.

Benefits of AWS

  • Onboards customers in a couple of days
  • Builds, trains, and deploys ML models in hours
  • Avoids the need for database expertise

AWS Services Used

AWS Lambda

AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app.

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Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

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Amazon Relational Database Service (RDS)

Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching and backups.

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