It’s increasingly difficult to achieve a reliable data set – and that could mean you can’t trust the information shared within your lending organisation. So, what’s the solution? In this blog, we explore how to gain high-quality data for credit risk programs, like AML, without it taking an abundance of your time.

Beyond satisfying regulatory governance, maintaining a flow of high-quality and harmonised data is essential for supporting credit risk programs. Most recently, the focus has been on feeding valid, relevant data into AML6 and additional fraud checks.

But, managing data is a tough challenge that can swallow an enormous amount of time as data flows in from a growing number of sources. How confident are you that you have access to the best data sources available? Often, we find clients aren’t necessarily using the wrong data sources, but they may not be the right ones for the business anymore (with the ICO continuing its investigation into data brokers, compliance with GDPR is another key factor to consider).

The biggest headache for many is planning. Finance teams spend way too much time on manual processes, collecting, consolidating, and validating data before they can even begin to analyse it. Furthermore, finance professionals and business managers often find budgeting, which is a key part of the planning process, to be burdensome and of little value in managing operations or executing corporate strategy.

🔍Top tip: To avoid getting caught not knowing something you should, be sure you’re not depending on incorrect, incomplete, or misleading data.

So is the answer throwing more time at the problem? There is a better way than wasting your time.

Navigating data blind spots

To get a full, accurate picture of your data without blind spots, you should deploy predictive models that score your data against intended outcomes. And, use decision optimisation that tells you, at the point of impact, just what to do.

Data quality checklist

To help manage your blind spots, there are four simple questions you should ask:

1.     Is any data missing?

2.     Is the data GDPR compliant?

3.     Is any of the information misleading?

4.     Is there pricing information available to ensure you are paying a fair price?

It’s critical that you can confidently answer these questions about the data that powers your decisions. And, move beyond acting on quick (and misleading) insights to cut through the uncertainty of “what happened” while getting deeper insights into the why. Once you’ve strengthened that foundation, then you can step forward and make advanced analytics attainable for everyday decision-makers.

🔍Top tip: The trick lies in finding not only the data you think is right, packaged as a pretty visualisation, but also the data that is right, and why. You can do this by identifying the root cause behind what happened, and be the first to discover the drivers that move the needle for your organisation before the competition beats you to it.

Why leading lenders use data benchmarking

Quality data holds the key to scorecard improvement and enables fair and transparent credit decisions. That’s why organisations rely on credit bureaux to provide accurate, high-quality data at a fair price. But there is a lack of objective, evidence-based insight into how each bureau measures up against each other, and the variations that different lenders pay. 

Data benchmarking addresses this significant gap in the credit community, by providing evidence-based benchmarks, and evaluation of data quality and accuracy – with complete pricing transparency.

Through comprehensive analysis, it enables you to see how you measure up and use that information to get a fair price and access to the right data for your needs. What’s more, working with an external partner can save valuable time and resources for the team, and enable more informed decision-making.

Price transparency: Going beyond data quality 

Going beyond basic data quality, data benchmarking has additional bonuses that all lenders need right now: cost sayings.

“With credit losses of $2.1 trillion for 2020 and 2021 spurred by the pandemic, reaching more than double the 2019 level - significant cost savings are a necessity.”

Alongside data transparency, comes pricing transparency.  That’s why leading organisations are supporting their procurement processes with new insight to provide complete transparency on data sources and pricing - saving on average 25-40% on costs.

Conclusion

With data benchmarking at your disposal, lenders are now able to eliminate unproductive activities like tracking down pricing and GDPR compliance.

Instead, you can spend their time using best practices such as driver-based planning and rolling forecasts to help them anticipate and respond to the disruptive forces driving the marketplace.

To build real confidence in your data, start by getting complete transparency on all your data sources. Be aware of the blind spots so you can be completely in control of all your contract negotiations.