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 Traditional credit checks are time-consuming and often based on outdated financial records, which has a negative impact on both your business and your potential customers.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.
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.
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.
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.
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