Accelerating Data Access and Adoption for a New Growth Horizon

Highlights: 

• Resilient lending partnerships are built on strategic collaboration between lenders, data providers, and technology partners to overcome challenges like process automation, compliance, and slow speed to market. This includes leveraging partners' core competencies and offering configurable data 'menus' instead of static solutions.

• The focus has shifted from mere "depth of coverage" to the depth and relevance of data. Lenders seek to convert unused data into predictive insights, focusing on data that can solve specific problems, predict financial risk, and uncover market opportunities (e.g., leveraging merchant transaction data).

Lenders, data providers, and technology partners are bringing their A-game to the table— strategically collaborating to create and support customer-first financial products and services. Yet, simple questions often lurk beneath the surface of these partnerships.  

What opportunities are we missing? Given our vast collective data, how can we deepen our understanding of customers and detect hidden signals—for their benefit and ours? Can we deliver smarter, faster solutions without compromising compliance?  

Overcoming Challenges Through Partnership Innovation

Whether it’s a Neobank launching a new credit card or a traditional lender adapting its risk model to evolving economic conditions, lender challenges persist, especially around process automation, compliance, and speed to market. 

Today’s partners are addressing these and other issues by reimagining how data and technology are used. Here are some examples:  

• Go beyond the decisioning engine. It’s crucial to have robust automation tools such as decisioning engines. Still, it’s equally, if not more, important to ensure lenders have access to the right data and credit products tailored to their target audience, performance goals, and compliance needs. The engine is only as good as the data fueling it. 

• Data access and process automation go hand in hand. Knowing this, offer a “menu” of data—instead of static solutions—that enables faster, configurable automation tailored to a lender’s unique size, business needs, compliance requirements, and operational processes across the customer lifecycle.  

• Lean into partner strengths. Instead of reproducing or reinventing what other companies do, lean into partners who bring those core competencies, proprietary algorithms, user experiences, and user interfaces to the table. This optimizes performance, facilitating better outcomes for all parties and opening the door to more opportunities. 

• Meet lenders where they are. By using a select mix of trusted providers, a partnership is better positioned to align with a lender’s current data and technology maturity and thoughtfully move them forward with tailored solutions that get to market faster. 

In essence, it’s less about saying “here’s what we can do” and more about “let’s explore what’s possible, together.”

Rethinking Data Depth and Value 

“Depth of coverage” alone is no longer the holy grail. While expansive data coverage is still valuable, lenders now focus on the depth and relevance of that data—whether it can help them problem-solve, reveal unique customer behaviors, identify risk, and signal financial stress or opportunity earlier. They want to know, “Can we convert these millions of rows of unused data into velocity counts, metric counts, and index counts, and build a score off that?” 

Also, they’re looking for ways to get more value from existing data sources. Leveraging the merchant transaction data to provide powerful new insights. Today, this data can be mapped against our legacy data to draw strong correlations and predictive insights, such as the likelihood of a business succeeding or failing within the next 12 months. 

Best Practices for Evaluating New Data Assets 

Apply the classic “what,” “so what,” “now what” formula to evaluate new data or alternative uses for existing data for the good of the partnership.

• What? Assess the data source, audience coverage, and all available data fields. 

• So What? Beyond standard applications, brainstorm as a team how the data can be creatively used to solve specific problems, such as better understanding the customer, predicting financial risk, streamlining compliance, detecting fraud signals, and uncovering market opportunities. 

• Now What? Identify the partner (or partners) best positioned to integrate and operationalize the data quickly and within regulatory bounds.  

Compliance and Due Diligence: The Foundation of Trust

In the highly regulated consumer finance industry, understanding compliance is essential to moving forward. For example, partners should know whether data is compliant with the Fair Credit Reporting Act and permissible for use in credit decisioning models. Also consider how it might support compliance-related tasks such as portfolio diversity reporting, as well as other mandates and benchmarks. 

Due diligence is also key. Gather use cases and success stories from peers and other partners and ask pointed questions: 

• What success have you had with this data and/or solution?

• What problem(s) did you bring to the partner or provider? 

• What mix of tools and resources did they offer you? 

• How quickly was it integrated into your decisioning waterfalls and workflows? 

Also consider key metrics: 

• Time to market: Can the data be quickly ingested into decisioning engines? 

• Portfolio performance: Will it increase approvals, reduce defaults and losses, or strengthen compliance reporting? 

• Decisioning speed: Can it be automated within a waterfall process to drive faster, more consistent customer decisions, benefiting all parties? 

Looking Ahead: Rethinking the Limits of Conventional Data

While credit bureau data captures “willingness to pay” signals, other sources capture “ability to pay,” and still others fill important data gaps to create more holistic views. In a tightening economy, having more complete and comprehensive insights is invaluable for proactive lender decisioning. 

Leading partners are “back testing” existing lender data, augmenting it with differentiated sources to experiment and demonstrate stronger approvals, lower delinquencies, and other improved outcomes across the account lifecycle. 

They’re also creating seamless processes for data access, flow, and contractual agreements that allow for easier integration wherever it makes sense within the lender’s analytic workflows, automated waterfalls, and overall decision engineering. 

Finally, emerging data such as Buy Now, Pay Later (BNPL) and peer-to-peer payment insights will play a growing role in decisioning—once they can be standardized and validated. As these and other data types emerge, lenders and their partners must determine how they affect creditworthiness and whether they expand access to financial services or obscure risk. 

Amid today’s economic uncertainty, the most resilient lending partnerships are those that collaborate boldly and think differently about data. By combining collective partner expertise with differentiated data and analytic insights, lenders can build a much deeper, more nuanced understanding of customers, accelerate innovation, and convert shared insights into smarter solutions that shape the future of the lending industry. 

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