“When data is missing, lenders are unable to review a borrower's application efficiently. These applications don’t provide a true representation of the applicant’s financial state, and it’s difficult for advisors to understand what is missing, causing a waste of time and resources. We believe in bringing attention to that” - Brandon Wallace, Senior Product Manager for Assess.
And not only bringing attention to it… but delivering the solution.
In the financial services world, lending doesn’t work without comprehensive customer data. And the data reliability itself depends on various factors, like the sources, the transaction analysis techniques and, of course, data completeness.
In this piece, we sit down with Brandon and his colleague Alexandra-Catalina Luminariu, Assess Product Designer to explore why data completeness is essential in making the right lending decisions and how Bud’s new feature can identify gaps in customer data.
Over-access to credit has seen the idea of lending shift from associated with profit to a high-risk operation. Minimal efficiency within the process has generated poor decisions, customers who are late with or missing payments and overall low repayment rates.
If this is a familiar feeling, it’s likely that lenders have been relying on traditional processes to make their lending decisions, such as:
While this type of information sources can be helpful, most institutions rely on credit bureau data in order to facilitate them. Relying solely on credit bureau data alone has clear pitfalls, such as inaccuracy, missing values and a lack of data transparency.
“Traditional credit reference agency reports don’t take into consideration changes to a customer's financial position in the most recent couple of months”, Brandon says.
And, Alexandra explains, “An advisor could go through the information, and then later find out that there was no income, for example. And this is a huge, real blind spot for credit bureau at the moment, because they tend to just estimate income through a proxy - the account turnover rate. It’s not helpful enough - it means they would have to go to the customer and ask for the information, and get more data to corroborate it. There’s a lot of repetitiveness to this.”
It’s not the end of the world to go back-and-forth with a customer in order to get things right.
But, what if you could say, with absolute certainty and supported by insights, that the data your advisors receive is only delivered once it’s complete and thoroughly enriched?
“The most important factors in decision making during lending are assessing income and assessing expenses”, Alexandra notes from her extensive client research.
“By making sure that we check if the data is complete ahead of time, in the beginning of the journey, the advisor knows when to wait instead of going into analysis mode on a set of incomplete data. We’re saving the advisor time”, she adds. And in doing so, this allows the organization to operate lean, move efficiently and conserve working capital.
In order to manage this transformation within the assessment process, an alternative data set comes into play, including:
Each of these data elements can benefit organizations by creating automated insights, preventing hesitation and time-wasting within the lending process.
This can also be useful when monitoring credit over time. Brandon explains, “What are the recent changes? Has there been anything significant which might not be noted in the credit reference agency report, that we can highlight through this transaction data? With these insights built into the API, the key part is having it there for end-users to then use as they wish”.
The data reconciliation process, leading to complete open banking data, therefore has a larger role to play in modern lending.
Comprehensive data can highlight when important transactions are missing that could suggest a customer hasn’t connected all of their accounts through open banking, for example.
Alexandra explains, “We are allowing them to check beforehand, at a quick glimpse, whether or not they’ve got all the data. Because manually assessing applications doesn’t just take a lot of time but also financial resources.”
Open banking also provides access to a wider range of financial data, including transaction history, and comprehensive account information. This is useful for advisors to gain a holistic view of their borrower’s financial situation.
Alexandra notes that this is especially useful over the long-term, “just looking at the most recent credit bureau report, a customer could have acted in a certain way to make sure their finances looked extra positive because they had planned to take a loan in the following month.
But by giving advisors a 12-month view, it can highlight the ups and downs, showing when a customer has had an unusual rough patch five months ago, for example. Or alternatively, if they have consistent trouble in managing their finances. It’s a win for both customer and lender, because a more accurate picture of the futures means less chance of delinquency.”
Creditworthiness assessments typically rely on data accuracy, and therefore completeness. It’s obvious that missing or incomplete data can lead to inaccuracy with risk assessments, credit scoring and ultimately, lending decisions.
Bud’s new feature therefore works to identify transactions, categorize them accurately and identify when key information is missing.
Brandon shares how this could look in practice; “This feature builds on top of underlying enriched data to confidently highlight when something is missing. The platform automatically highlights this to the advisor, which means that they can speak to the customer in real-time to help them connect another account.”
Completeness and data validation are not only useful within credit assessments, but it can also help organizations prove their compliance to Consumer Duty regulations.
“In light of these requirements, it’s important that advisors are informed and can somewhat predict how a customer might act after being approved for a product. The fact that Assess can show the data over a twelve month period doesn’t just show a snapshot - but a more accurate trend over time.”, Alexandra explains.
As previously mentioned, one example of a common problem around assessing creditworthiness is income visibility. Here, a credit bureau report would not typically differentiate between data lineages, such as true income sources versus the applicant receiving external transfers from friends. But this impacts data integrity.
Brandon says, “this is a really hard problem to solve because, at the end of the day, customers have control over what banking data they choose to share. Even with open banking, advisors haven't necessarily known whether the customer is hiding stuff.”
This means that credit assessments could go one of two negative ways:
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Incomplete data - not recognized |
Incomplete data - recognized |
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Lending approved when it shouldn’t have been, leading to increased credit risk |
Forced to decline applications due to missing income data (therefore low data quality), when actually the customer would be approved if this was present |
Alexandra expands,”creditworthiness is heavily related to the net discretion of income value, which is income minus taxes, minus essentials. So within the Assess dashboard, taxes are already removed so that income is at the transactional level. Then, transaction categorization can automatically determine the essentials, making the net discretionary income clear.”
Bud’s new data completeness feature, within the Assess suite, works to provide that transparency. Brandon adds, “the data completeness feature highlights that there are key pieces of information that are missing, which could suggest that customers haven’t fully connected their accounts.”
This new feature makes sure that the Assess dashboard has the two major elements that advisors need for effective credit assessments. This, paired with the display of income over time generates efficiency, enabling advisors to assess applications quickly, and with a high level of confidence.
How can we expect advisors to make confident, informed and fair lending decisions if they don’t have the full picture? Complete data enables accurate assessments, and protects organizations from heightened financial risk.
Prioritizing data completeness also empowers institutions to serve their customers better, meet consumer duty regulations and compete in the evolving open banking landscape.
Automatically notify your advisors when data could be missing with Bud.