Lifting the lid on the tech that powers enhanced credit scoring

4 min read|Published March 12, 2020

In part one and two of this series, we looked at the ‘what’ and the ‘why’ of enhanced credit scoring, discussing the benefits to consumers and businesses. In this third and final piece we dive into the ‘how’ – showcasing the tech behind the solution.

An enhanced credit scoring solution enables you to make more informed credit decisions, which leads to consumers getting a better deal. But where do you start if you want to build it into your flow? Quality account aggregation and data enrichment services are the foundations of a standout credit scoring model. Here we explain how.

Understanding your would-be customers’ true financial picture is crucial to making more informed, more timely and ultimately better credit decisions. But it’s something that many businesses struggle to do. Traditional credit checks are time-consuming and often based on outdated financial records, which has a negative impact on both your business and your potential customers.

Enhanced credit scoring gives you the opportunity to do things differently, and any model worth its salt has to begin with quality account aggregation. This is the service that gathers your customers’ real-time financial data in a split second through one API, laying the foundation for the rest of the solution to create its magic on top of.

Aggregating the data

It works like this. Your customer logs in to the credit scoring app or website, and selects the providers where they have accounts. With Tink, account aggregation can span over 2,500 financial institutions across 13 European countries, covering at least 95% of the population in each market.

Once authentication is complete, the single API delivers a bigger picture of a user’s financial situation than a credit check normally delivers, straight into your system, including data on checking account transactions, and in some cases, savings accounts, credit cards, investments, loans and mortgages can be included.

Making it meaningful

But once you have gathered this wealth of data, then what? You need to be able to use it in a way that’s meaningful for enhanced credit scoring. Which is where data enrichment comes in.

It’s the process of refining and enriching the raw data, to understand and recognise patterns in people’s financial history. The first step is categorising a user’s transactions. The data is cleaned, removing duplications, resolving discrepancies, and filtering transactions into categories, so you can see how and with who your potential customers are spending money.

From this, Tink can calculate someone’s income, identify their recurring transactions and understand their spending patterns.

Quality data enrichment is made possible by a scalable self-learning machine model, that is continually fed transactions, learning how to classify them accurately. It doesn’t matter how many merchants enter the marketplace, the system simply keeps pace. In the past eight years Tink’s machine learning model has enriched over 5 billion transactions, with each one making it smarter and more efficient, to ensure the best user experience. All in an instant.

Real-time decision making

This cleaned up, enriched data can then be fed to your credit scoring risk model, allowing you to use quality, real-time data to make better risk assessments and informed credit decisions, helping you to improve acceptance rates and risk accuracy.

Then you can present your customer with a competitive and well grounded offer right away, and even make it actionable, allowing your customers to sign up for your service or loan, and transfer funds into their account immediately. With the right technology in the background, the whole process from start to finish is completed seamlessly, and more accurately, in a matter of minutes.

Want to learn more? Check our enhanced credit-scoring page and find out how to start building your solution.

CategoryProduct

More in Product

2024-07-17

2 min read

Banking on engagement

This Tink white paper introduces new consumer and retail banking executive research from key European markets, setting the scene for banks to take the next step with Personal Finance Management (PFM).

This image depicts a tablescape from above with a slice of cake, fork, drinking glass and mobile phone, with the phone screen displaying Tink's Variable Spend feature in action.

2024-05-30

5 min read

Meet Variable Spend: our latest feature for intelligent expense tracking

Expense tracking can be detailed as well as intuitive – say hello to Variable Spend, a new feature of Tink Data Enrichment designed to help banks offer their customers more insight.

This image depicts an abstract illustration to convey the concept of a categorisation model that extracts value from account and transactional data, with the aim of resulting in a cleaned data set.

2024-05-28

12 min read

Risk decisioning essentials: our latest categorisation model updates to help you get ahead

Confident decisions, top quality reports and going to market faster are just some of the benefits Tink’s new categorisation model has for lenders – deep dive into the world of generative AI, multilingual models, and more with this guide.