Alternative Lending Data #3

Crawl-Walk-Run: How Finance is Managing the Shift to Alternative Lending Data

The emergence and integration of alternative lending data is not only reshaping how lenders assess and manage risk but also how they connect with a broader spectrum of borrowers, many of whom were previously underserved by traditional credit models. To better understand this shift, it's instructive to adopt a crawl-walk-run perspective, examining how financial service providers are progressively leveraging alternative lending data to revolutionize the lending process.

Crawl: Establishing the Foundation with Verification

In the crawling stage, lenders begin their journey into the realm of alternative lending data by using it for a fundamental yet crucial purpose: verifying the information provided in credit applications. This initial step is fast, efficient, and factual, serving as a foundational block for more advanced applications of alternative data. By ensuring that the application details match with reality, lenders can significantly reduce the incidence of fraud, a pervasive challenge in the industry.

This phase represents a cautious entry into alternative data utilization, where the primary goal is to enhance the integrity of the lending process without yet diving deep into the analytical potential these new data sets offer. Most institutions find themselves in this phase, recognizing the value of alternative data in strengthening their vetting processes and laying the groundwork for more sophisticated analyses.

Walk: Advancing to Cash Flow Analysis

As financial service providers grow more comfortable and adept at incorporating alternative data into their operations, they advance to the walking stage. Here, the focus shifts to analyzing borrowers' cash flows, utilizing alternative data to gain insights into how much money borrowers have left after their monthly expenses. This approach allows lenders to assess a borrower's ability to handle loan payments more accurately and dynamically than traditional credit scores might permit.

This stage represents a significant step forward, moving beyond mere verification to actively leveraging alternative data for enhanced decision-making. Institutions that reach this stage begin to see the transformative potential of alternative data, using it to develop a more nuanced understanding of borrowers' financial health and sustainability. It marks a transition from static, backward-looking assessments to more dynamic, forward-looking evaluations.

Run: Revolutionizing Risk Assessment

The running stage is where the utilization of alternative lending data truly hits its stride. Lenders at this level harness transaction data not just to verify information or assess cash flows, but to augment traditional risk scores or even build separate bank transaction models. These models can be deployed alongside traditional scores in a matrix fashion, offering a multidimensional view of a borrower's risk profile.

By analyzing transaction data in-depth, lenders can achieve greater separation in terms of risk prediction, identifying subtle nuances in borrowers' financial behaviors that might not be evident from traditional data alone. This sophisticated approach allows for more personalized, accurate risk assessment, potentially leading to better loan terms for borrowers and reduced risk for lenders.

This stage embodies the full potential of alternative lending data, leveraging it not just as a supplementary resource but as a core component of the risk assessment process. Institutions that reach this level are at the forefront of the industry, driving innovation and setting new standards for accuracy, efficiency, and inclusivity in lending.

The Road Ahead

The crawl-walk-run perspective highlights a progressive journey towards the full integration and utilization of alternative lending data within the financial services industry. As institutions move from verification to cash flow analysis, and ultimately to revolutionizing risk assessment, they unlock new possibilities for both lenders and borrowers alike. This evolution is not just about adopting new technologies or data sets; it's about rethinking the very fundamentals of lending, making it more accessible, accurate, and fair.

The journey is ongoing, with many institutions still navigating the early stages. However, as more organizations advance to the running stage, the impact on the industry and on financial inclusion will likely be profound. The future of lending lies in the effective use of alternative data, and the crawl-walk-run model provides a roadmap for institutions aiming to lead the way.

To learn more about the use of alternative data in lending, check out our in-depth paper, How alternative data will shift the credit landscape as we know it.