Building loan analytics in-house makes sense mainly for large, analytically sophisticated lenders that have proprietary pricing models to protect and can afford dedicated database, back-end, front-end, design and data engineering teams. Those conditions do not exist for the overwhelming majority of credit unions, and building also carries key personnel risk.
Should a credit union buy a purpose-built loan analytics solution, or invest in building one internally? In certain circumstances, building proprietary loan analytics infrastructure is the right call for some institutions.
What are the benefits of building loan analytics in-house?
Total customization is possible
A self-built solution can be engineered to reflect the exact pricing models, risk frameworks, and reporting structures that a credit union uses internally. If an institution has developed a member scoring methodology, or prices loans based on a unique combination of behavioral and financial variables, a commercial off-the-shelf solution may not accommodate that logic without significant configuration — or at all.
Proprietary pricing models stay protected
The largest and most analytically sophisticated lenders — major banks and specialty finance companies — sometimes possess genuine, proprietary pricing models that they want to protect and operationalize in software. These institutions often can afford teams of engineers that structure and maintain the database, develop and maintain back-end code, design and update the user-interface, develop and maintain the front-end code, and data engineers to continually monitor and clean incoming data. With those labor intensive and costly resources, building ensures that the institution’s intellectual property is not inadvertently shared with a vendor who also serves competitors, and that the pricing engine can evolve precisely in step with the institution’s thinking and the feedback loop provided by the analytics solution.
AI and modern development tooling can help
The recent emergence of generative AI coding assistants, low-code platforms, and modern data stack tools (e.g., Power BI, Tableau, Looker, and even Excel) have in many instances lowered the cost and timeline of building internal software. A well-resourced engineering team today can accomplish in months what used to take years. This development has given new life to the internal build argument, and credit unions evaluating their options today should not dismiss it on the grounds of traditional development economics alone. (We look at this question in more depth in Does Generative AI Change the Buy vs. Build Calculus?)
What expertise does building loan analytics require?
However, these advantages still require the internal database, back-end, front-end and design expertise even if a firm may need fewer individuals to develop and maintain an effective web application. These conditions do not exist for the overwhelming majority of credit unions. And for those institutions, the build path carries risks that often go underappreciated in initial planning conversations or prototyping.
What is key personnel risk in internal software development?
Internal development carries material key personnel risk. Maintaining an unfamiliar code base is difficult for even the most talented and experienced engineering teams. As development professionals transition to new roles out of the firm, new people will be hired to maintain the analytics framework. This personnel churn is costly and frequently can result in static applications that cannot be updated at commercially reasonable expense.
For most credit unions, these risks tip the balance. Next in the series: Five reasons credit unions should buy, not build, loan analytics software.
Frequently asked questions
Can off-the-shelf loan analytics software handle a custom pricing model?
Not always. If a credit union prices loans based on a unique combination of behavioral and financial variables, or uses its own member scoring methodology, a commercial off-the-shelf solution may not accommodate that logic without significant configuration, or at all.
Why do large banks build their own loan analytics?
Major banks and specialty finance companies sometimes have proprietary pricing models they want to protect. Building ensures their intellectual property is not shared with a vendor who also serves competitors, and they can afford the engineering teams needed to build and maintain the platform.
What happens to an in-house analytics platform when developers leave?
New engineers must be hired to maintain an unfamiliar code base, which is difficult even for experienced teams. This churn is costly and frequently results in static applications that cannot be updated at commercially reasonable expense.
Related reading
- Buy vs. Build: A Strategic Framework for Loan Analytics Software at Credit Unions
- Why Is Loan Analytics So Hard for Credit Unions? Dirty Data, IRR at Scale, and Prepayment Analysis
- Should Credit Unions Buy or Build Loan Analytics Software? Five Reasons to Buy
- Does Generative AI Change the Buy vs. Build Calculus for Loan Analytics?
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