To Report or Not to Report: A Story of Modern Credit Data Furnishment
Penned by Christian Widhalm, CEO & Matt Keleshian, COO
In consumer finance, the credit score remains the most critical yardstick for measuring a person’s ability to pay, or “creditworthiness”. This score, which underpins nearly every economic opportunity available to consumers, is calculated using models developed by major players such as FICO and VantageScore. The problem is that these models exclude a huge swath of credit-invisible consumers in the US. Why? Because the models are only as effective as the data they use.
This brings us to the core of a modern dilemma in credit reporting: the data furnishment process.
The Challenge of Modern Credit Risk Modeling
The development of credit scoring models, such as those by FICO and VantageScore, is constrained by the availability and type of consumer data they can access. These models rely heavily on historical data provided to credit bureaus to assess creditworthiness. The scope of data included in these models directly affects their accuracy and fairness.
Here’s the thing: the data collection process is fundamentally flawed. Credit bureaus traditionally focus on collecting data on loan repayments, credit card usage, and similar financial activities. But this conventional data pool overlooks other reliable indicators of financial responsibility like rent and utility payments. Most bureaus don’t capture these types of alternative data, so the data doesn’t get integrated into the scoring models. This limits the comprehensiveness of credit evaluations and affects consumers when the traditional data types do not accurately represent their financial activities.
Essentially, it creates a fundamental “chicken and egg” problem. Data scientists need access to diverse and extensive consumer datasets to develop and refine predictive models. Without innovative models that can incorporate new types of data, data scientists can’t tap into these potential data sources. Conversely, without clear demand and proven utility from updated models, bureaus and data furnishers have little incentive to collect and provide new forms of data. In other words, without the data, there can be no model to evaluate such data, and without the model, the data remains irrelevant.
Consumer concerns about privacy and data security add another layer of complexity — concerns highlighted by numerous litigations, including those leading up to the enactment of the Equal Credit Opportunity Act (ECOA) and high-profile data breaches like the one at Equifax. The Equifax incident heightened consumer apprehension towards sharing personal information. It also resulted in more stringent regulations and a cautious approach from data furnishers and credit bureaus, hindering the adoption of new data practices.
Finally, the transition to newer models is sluggish; the majority of financial institutions still rely on older versions of FICO scores (like FICO 8, despite the availability of FICO 10). This is largely due to the extensive work required to validate and adopt newer models and recalibrate existing credit policies. This resistance to change ensures that newer, potentially more equitable, and comprehensive models are slow to be adopted, perpetuating the cycle of outdated data and methodologies.
Between data limitations, regulatory challenges, and institutional inertia, the modern credit industry faces significant challenges. Addressing these challenges requires a concerted effort from all stakeholders to expand data inclusivity, enhance model accuracy, and ultimately provide a more fair credit evaluation process for consumers.
The Fragmentation of Credit Reporting
In addition to the limited data and models that dictate credit scores, current credit reporting practices are riddled with gaps, particularly with how data is reported to the three major credit bureaus. The reality is that not all data furnishers report to all three bureaus. Some may choose to report to only one or two, often due to cost considerations or contractual relationships. This selective reporting results in each bureau having a potentially different set of data for the same consumer. Consequently, a consumer’s credit score can vary significantly across bureaus, complicating financial applications where lenders might pull reports from more than one bureau.
The regulatory framework adds another layer of complexity to unifying credit reporting practices. While the Fair Credit Reporting Act (FCRA) insists that all information in credit reports must be accurate, complete, and verifiable, it doesn’t require data furnishers to report to all three credit bureaus or specify what types of data need to be reported. This results in varying information across different bureaus, which naturally leads to inconsistencies in credit data.
On the operational side, integrating new kinds of alternative data into credit reports presents significant challenges. Credit bureaus need robust systems in place to securely gather, store, and handle this data. They also need to develop precise methodologies for folding this data into existing credit scoring models. This isn’t just a technical task — it involves deep statistical analysis and rigorous testing to ensure everything meets regulatory standards.
Even incremental steps toward including alternative data, such as Experian Boost, result in data silos where consumer-permissioned data benefits only one bureau, leaving gaps in the others. This fragmented approach to credit scoring does not incentivize — or require — providers of alternative data to furnish this information across all bureaus. This has also limited alternative data’s use and adoption by lenders from becoming more mainstream.
For consumers, this fragmentation creates a tricky landscape to navigate. Their financial activities might not be fully acknowledged across all credit-assessing platforms. This discrepancy can lead to uneven credit opportunities and borrowing costs, depending on which bureau’s report a lender reviews. Such inconsistency can disadvantage consumers who might benefit from a more comprehensive view of their financial behaviors, impacting their access to credit and the terms they receive.
What’s it Going to Take?
To overcome these significant challenges, coordinated action from all stakeholders is essential. Credit bureaus must lead by investing in robust systems to securely gather, store, and integrate alternative data, while standardizing reporting practices to ensure consistency across the board. Lenders and financial institutions should adopt and validate newer, more inclusive credit scoring models, offering consumers the ability to report non-traditional financial activities such as rent and utility payments. Credit scoring companies, including FICO and VantageScore, need to continuously refine their models to incorporate a broader range of financial behaviors. Regulators play a critical role by implementing policies that encourage the use of alternative data while safeguarding consumer privacy and security. Consumers must also embrace these opportunities by opting into programs that enhance their credit profiles with alternative data. By taking these steps, we can create a more inclusive, accurate, and fair credit evaluation system, ultimately benefiting consumers and financial institutions alike.
Consumer Challenges and Solutions
Consumers bound by traditional credit scoring models often face an uphill battle, resorting to tedious strategies to potentially improve their credit standing. Alternative credit scoring offers an emerging solution by leveraging consumer behavior data. Still, this approach introduces its own set of challenges. For instance, the Consumer Financial Protection Bureau (CFPB) mandates that alternative data must be treated with the same protections under the Fair Credit Reporting Act (FCRA) as traditional data. Moreover, Credit Reporting Agencies (CRAs) are not always equipped to handle this new type of data, and it can be difficult for furnishers to report it.
Consumer-permissioned data addresses these issues by allowing consumers to control what data is shared and where it flows. Consumers are incentivized to “turn the data faucets all the way on,” thereby enhancing the flow of credit data for all parties involved — bureaus, lenders, financial institutions, and most importantly, consumers themselves.
The key is having the right plumbing. Bloom’s consumer-permissioned data offering empowers consumers by enabling them to opt-in to report demand deposit account (DDA) information to bureaus. Traditional financial institutions (FIs) can offer this opt-in option to their customers using Bloom’s low- and no-code options. It’s an easy, compliant way for FIs to help their customers build better credit. More importantly, it gives consumers control over their DDA data, allowing them to include more good data in their credit profiles, improving their ability to access credit and lower rates. It also works hand-in-hand with Bloom’s furnishment platform, which simplifies the complexity of data reporting and makes it seamless to navigate complex multi-bureau approval compliantly and accurately.
The modern landscape of credit data furnishment is marked by significant challenges but also presents opportunities for innovation. By embracing alternative data and enhancing the way it is integrated and utilized, the financial industry can create a more inclusive and accurate system of credit evaluation. This not only benefits consumers by providing a more comprehensive view of their creditworthiness but also enables lenders to make better-informed decisions.
