Solving the Consumer-Permissioned Data Puzzle

Penned by Christian Widhalm, CEO & Mike Esler, CTO

If you watched any of the NFL playoff games this season, you surely saw at least one ad featuring Travis Kelce pitching Experian’s new debit card. The pitch for this relatively new product offering is similar to their Boost product: build credit by sharing data on your bill payment activity. And there’s a major need for this type of service, as over 100 million US consumers currently don’t have access to mainstream credit products or rates, split nearly 50/50 between those consumers being subprime or thin/no-file with the major credit bureaus.

It’s such an eye-opening problem that it piqued the interest of investors, banks, and credit unions at FinovateSpring who voted for our Bloom+ product, which was awarded Best of Show out of 60 participants. They can likely feel the storm coming as new credit trends underscore the massive changes financial institutions must make to better serve the next generation of customers. Traditional credit scoring models are falling short, and there’s a critical need for models to evolve and incorporate alternative forms of data.

The premise certainly makes sense. Your rent, utilities, and cell phone are all contractual obligations — promises to pay — and should be viewed in a light similar to other financial obligations by someone evaluating creditworthiness. Some lenders already do this, at least to a limited extent, by pulling banking transaction data from aggregators like Finicity, Plaid, MX, etc. But generally, the context here is evaluating one’s ability to pay. Consumers don’t benefit from a long history of on-time payments.

Interestingly, both the Vantage and FICO score models already have support for this type of data. Rent payments — if provided as input — will impact your credit score in Vantage 3 and 4 and FICO 9 and 10. As will utilities. Yet very few consumers are seeing these benefits today. The issue in a nutshell is that payment data is not flowing nearly enough from landlords, property management companies, utilities, and telcos to the credit bureaus.

There’s a multitude of reasons why this data isn’t furnished (the industry lingo for submitting payment data to bureaus):

• Technical complexity: Property management, utility, and telco billing systems were not engineered to produce Metro 2^® files, and bolting on this functionality is challenging due to the impedance mismatch between statement data and bureau information. Even worse, the vast majority of rental property is owned by small businesses and individuals whose accounting demands are so lightweight that they don’t need a traditional billing system.
• Misperceived low ROI for providing this data: Furnishing data and helping their customers improve their credit scores isn’t core to their mission. The primary benefit that they see as a result of furnishing data is an easier path to collection on past-due accounts.
• Industry inertia: If competitors aren’t furnishing, and no one has historically furnished, then what is the carrot or impetus to motivate someone to start?

And yet, there is a demand for this data. The solution to this problem that the credit reporting industry is converging on is what’s known as “consumer-permissioned data”. In short, all of these payment transactions exist in the history of consumer checking and DDA accounts. If the telcos, utilities, and landlords can’t or won’t provide the data, perhaps the consumer will… particularly since they have the most to gain from including the data in their credit reports.

Experian has been leading the charge here, working to establish direct relationships with consumers through a variety of different products and services, and we applaud their efforts. But this approach has some real limits. Most critically, all this data only goes into the consumer’s file at Experian. If their credit is being evaluated by someone who pulls a report from a different bureau, none of this data will be reflected. The consumer who thought they had been successfully building their credit and had a 725 credit score might find that they’re still a 660 based on the data held at a different bureau. This model also doesn’t benefit traditional FI’s, who will continue to see their DDA customers seek out third parties to provide them with credit-building opportunities as well as other banking products and services.

What’s required here is a more democratized solution that would enable consumers to get this important and relevant credit data flowing to all the bureaus from their existing DDA provider. The good news is that there’s already a mechanism for every bureau in the US to take in this data: the existing Metro 2^® format.

It’s also important to understand the key differences between consumer-permissioned data and how credit data is traditionally furnished. With the latter, financial institutions act as the data source, often storing consumer data in data warehouses, which poses its own unique risks. It also makes for an opaque view that limits how much visibility consumers have into how (and with whom) their data is shared. Consumer-permissioned data, on the other hand, can be much more precise in data transmission, only sharing what is needed with sophisticated controls, compliance guardrails, and verification processes in place.

Data from traditional furnishers is derived from the financial interactions consumers have with recognized financial institutions — things like account balances, payment histories, credit limits, and loan details. Consumer-permissioned data comes directly from the consumer, who grants third-party services permission to access their financial data held by various institutions. This can include bank account transactions, utility payments, rent payments, and other financial details not typically reported to credit bureaus by traditional furnishers.

This encompasses a broader set of financial information, including alternative data that is not normally part of traditional credit reports. It provides a broader view of a consumer’s financial behavior, such as consistent payment histories on non-credit bills, which can be particularly beneficial for consumers with thin credit files or those rebuilding credit.

What’s more, it fosters a greater level of control over data for consumers, who currently cannot choose what data is shared. This model empowers consumers to decide what information is accessed and revoke permission at any time. This level of control can empower consumers, making them active participants in the credit reporting process.

The inclusion of non-traditional data can help provide a fuller, more accurate picture of a consumer’s true creditworthiness. It can be particularly advantageous for underserved groups, helping to level the playing field for those who might be disadvantaged by traditional metrics.

It’s incumbent on the financial institutions — the credit unions, community banks, regional banks, and major money centers — to enable this capability for their customers. Core banking providers ought to be working on solutions so that FIs can flip a switch and turn this feature on.

At Bloom, we’ve been actively working on just this:

• Banks and credit unions can offer Bloom’s product through both low-code and no-code options. In fact, no-code options could be live within 24 hours of the FI partnering with Bloom.
• Consumers are prompted about the available service and complete a very simple enrollment process.
• Historical recurring bank transaction data from each consumer’s DDA is then parsed and categorized and displayed as potential eligible tradelines for the consumer to opt-in for its reporting.
• Bloom captures and stores all consumer consents and then transforms the data into formats that are highly accurate and accepted by the credit bureaus.
• Every month Bloom re-pulls bank transaction data on each enrolled consumer and continues to report that data to the bureaus so long as it continues to be repaid.
• As consumers see improvements on their credit profile, especially those that were thin/no-file with the major bureaus, they’ll eventually be able to access more mainstream credit products through their existing FI.

Consumer-permissioned data does raise some interesting questions — and opportunities — in terms of how credit bureaus would approach the data. For example, credit bureaus will have to decide how to incorporate and weigh traditional versus alternative credit data. This could happen in a couple of ways. One approach could be to create separate sections in the credit report for alternative and traditional data, so users can distinguish between different types of data. Another approach could be to integrate the data into existing credit scoring models. In this model, the weight given to each type of data might differ depending on its perceived reliability and relevance.

The bureaus may also choose to create new, tailored credit products that serve more diverse consumer segments. Or they might offer more sophisticated analytical tools and services to lenders and financial institutions, enabling them to make more informed decisions.

The way this data might be used isn’t regulated, so bureaus and lenders may approach the issue in different ways. Yet, it’s clear that nearly every corner of the ecosystem will benefit from more robust data. One thing is certain: Leveraging Bloom as a translation layer — one that inherently adheres to the business processes and rules of the bureaus — removes the complexity and eliminates the margin of error for contributors.

While there’s no “silver bullet” to solving the critical credit data gap problem, consumer-permissioned data comes close. That said, a need for guardrails underpins the potential success of this model. Credit bureaus — like data furnishers — are beholden to FCRA rules. This makes fraud a top threat and primary concern, and rightfully so. Identity theft and synthetic profiles pose a real risk. Lapses in security or data accuracy validation on the part of bureaus could result in regulatory investigations and sanctions, fines for non-compliance, and class-action lawsuits from affected consumers seeking compensation for damages caused by errors or negligence in handling their credit information.

Those are the big, scary, obvious ramifications for imperfect handling of data. But the bureaus also face increased costs related to resolving disputes, correcting inaccuracies, and improving security measures. It’s a lot to swallow.

But, technology can help to address these challenges and mitigate the risk. In some cases, consumer provided data can be validated against existing trusted data sources. In others, human-in-the-loop AI can be developed and deployed to reduce the cost of manual review processes.

Financial institutions are at a crossroads. Consumers clearly want access to better solutions — and to be included in the mainstream credit market. This requires the inclusion of accurate, transparent traditional and non-traditional data for calculating a person’s creditworthiness. FIs have the power to facilitate this flow of data — with the right technology partner. Bloom sits at the intersection of consumer-permissioned data and a compliant means to furnish that data to bureaus. Partnering with Bloom to offer this type of credit-building service not only allows FIs that offer DDAs to retain sovereignty over their customers, it allows them to level the playing field when consumers need it most.