In today's financial landscape, fintechs, banks and other lenders need to make sound credit decisions in order to stay afloat. And one of the most important factors in making accurate credit decisions is having high-quality data.

But if your data is incomplete, outdated, or not compliant, you won’t be able to use it to make the right risk decisions.

In this article, we walk through the steps you can take to improve data quality – which helps to reduce risk and improve the bottom line.

The challenges of data quality

Let’s start with a quick recap of what’s been happening in the credit data market…

Essentially, without accuracy and reliability in data quality, credit practitioners cannot trust the data or make informed decisions. This can, in turn, increase costs and wreak havoc on downstream processes.

And in the highly regulated credit industry, bad data can result in fines for improper financial or regulatory compliance reporting. All of this makes high-quality data critical for credit teams.

Yet, while high-quality data is essential for making sound credit decisions, it can be difficult to obtain. This is because data is often incomplete, inaccurate, or outdated, as highlighted by the FCA’s credit information market review.

While the FCA wouldn’t expect the CRAs to hold identical information on all individuals, it found that there are significant differences in the credit information held by the 3 large CRAs; information that is particularly important to a lending decision.

The above shows that the 3 large CRAs hold consistent information on the number of defaults for only around 30% of consumers.

We also know, there are significant differences in how each bureau prices data, and the resulting data quality and accuracy. (More on this later.)

Factors highlighting the importance of data quality

A variety of factors highlight the importance of improving data quality:

●      The FCA’s focus on data

●      Strong risk management

●      The need to increase efficiency and reduce costs

In other words, risk management, regulatory compliance, and the bottom line are all impacted by the quality of data. And if customer data is incomplete or inaccurate, the results can severely affect decisions and scores. 

Whilst this may sound like doom and gloom, the good news is that there are several things you can do to improve data quality 👇

5 best practices for improving data quality

Improving data quality in financial services calls for a deeper understanding of data sources, data transparency, and the core dimensions of data quality. 

With this in mind, here are 5 best practices for improving data quality.

#1: Make the quality of data transparent 

Missing, incomplete and inconsistent data can cause massive problems for financial institutions, especially when it comes to risk decision-making. Banks depend on up-to-date, consistent data.

But when you have several data discrepancies between the major CRAs – it’s difficult to establish a complete, accurate and transparent data overview. What’s needed is a way to understand how data quality differs across all the data bureaux data to help you to meet quality standards. This can only be achieved by adopting a data benchmarking approach. 

#2: Ensure the key dimensions of data quality are sound

In addition to incomplete data, customer data can change and lose integrity over time. For example, BNPL applications may not get updated immediately and new data sources may not get reconciled correctly.

That’s why we recommend that you focus on the dimensions of:

●      Completeness.

●      Timeliness.

●      Accuracy.

●      Validity.

All of which indicates whether data in financial services is fit for purpose. Continuous monitoring of data is also essential.

#3: Create a data quality plan

One of the most important things is to have a data quality management plan in place. This plan should identify the steps that you will take to ensure that data is accurate, complete, timely, and relevant.

Key steps might include:

●      Set clearly defined metrics.

●      Ensure data quality by establishing data governance guidelines.

●      Create a process to detect any suspected poor data.

●      Leverage external support where needed to improve data transparency.

#4: Adopt trusted data benchmarking 

We’ve already briefly mentioned this, but it’s so important it deserves its own section. 

To put it bluntly, one of the most important things that lenders can do is to invest in data benchmarking. This approach can help lenders to gain evidence-based benchmarks and evaluate data quality and accuracy with complete pricing transparency.

In a nutshell, this provides key performance indicators for each of the credit bureaux so that lenders and other credit providers can choose the optimal solution – whether measured against industry peers or by an entirely different industry. And this information can then be used to identify performance gaps.

#5: Purchase high-quality data

Once you understand how your data benchmarks, you might choose to purchase new data. But, we've discussed one of the main criticisms of bureau data is that it can be hard to verify its accuracy.

So, what can lenders do?

There are, however, some ways to ensure quality. Choosing the right provider(s) goes a long way. Find out everything about the data before you purchase it. And see if you can determine whether it was modelled or validated.

There are so many factors to compare here. And so it’s clear to see why hundreds of credit providers seek expert support when making data quality purchases and contract renewal negotiations.