What’s data enrichment, and why do you need it?

You can’t benefit from raw data unless it’s turned into insight, and unless you’ve got an in-house data science team and machine learning capabilities, there’s not much you can do with it. That’s when you need a data enrichment tool.

What is data enrichment?

Raw data, as the name suggests, returns a large dataset that can span years. You might use it to verify a customer’s identity, account details, transactions, or income. But raw data is messy and confusing with strings of text and numbers… and it gives little context.

Take this transaction as an example: TESCO PFS BASINGSTOKE 2020/07/30 3803. At first glance, it could mean anything. But once enriched, it reveals a customer spent £10 at their local Tesco petrol station in Basingstoke. Much easier to understand, right?

Put simply, data enrichment is the additional level of insight businesses can derive from raw data, like the example above. Getting these insights before would’ve needed an in-house data science team and costly technology. Now, data enrichment tools are ripe for the picking.

So, who needs data enrichment?

Data enrichment gives lenders, brokers, car finance providers and much more access to a comprehensive overview of potential buyers. For instance, let’s say you had a large one-off transaction on your statement. Data enrichment would automatically recognise this as a one-off rather than a recurring transaction. The result? Boosted affordability scores.

What about data categorisation?

While data enrichment turns data into insight, categorisation goes one step further by sorting data into categories such as shopping and bill transactions. Let’s say you’re trying to understand an applicant’s financial situation for a loan application. You’d want to know how much they get paid, when, and how they spend it, right? Data categorisation gives the full picture of their spending habits.

Data enrichment use cases

1. Affordability check

Affordability is key for lenders, whether it’s consumer or business lending. But, when only considering a small snapshot of a potential borrower’s financial position, it can be difficult to understand real affordability. Data enrichment enables lenders to analyse financial data for up to two years, providing a clear and accurate affordability profile.

2. Creditworthiness assessment

While affordability determines whether an applicant can afford to borrow, creditworthiness looks at how likely they are to pay it back. Credit scores are a fundamental deciding factor. But, those three-digit numbers disregard millions who are still improving their finances. Data enrichment allows lenders to access real-time insights, so they can make more informed decisions about who to lend to.

Want to see how Juni use open data enrichment and banking to improve their creditworthiness assessments for ecommerce businesses? Check out their case study here.

3. Income verification

Whether it’s lending or renting, proof of income is at the core of a decision. It’s a lengthy process… particularly for those who are self-employed. In fact, business owners are twice as likely to be rejected for a mortgage. But with data enrichment, income verification is instant. No payslips, no bank statements, and no tax returns.

4. Balance Prediction

Data enrichment enables businesses to predict their cash flow position based on prior balances and see balance trends across accounts. Lenders can also use it to determine whether a customer’s balance will go up, go down, or go into an overdraft (which also helps businesses and consumers better manage their money).

Get in touch to learn more about how Yapily’s data enrichment tool can help you view spending habits, predict balances, identify recurring transactions, and better understand affordability. Still exploring your options? Check out our Open Banking Buyer’s Guide to understand how to vet providers and choose the best partner.


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