Make vs Zapier for Specialty Asset-Based Lenders

A straightforward asset-based facility, one collateral type, one advance rate, one approval tier, moves through underwriting on a single path: pull the credit file, calculate the borrowing base, route to the assigned underwriter. A single Zapier connection that triggers a credit pull the moment an application is submitted and drops the result into the loan file covers this case well, because there is only one path for the automation to model in the first place.

Lenders who price off a floating benchmark need their quoting workflow to pull a current rate before it calculates a term sheet, not a rate that was accurate when the workflow was first built. With the 10-year Treasury yield recently near 4.44 percent¹, a quote generated even a few weeks stale can already misprice a deal enough to matter. Make's ability to pull a live rate feed as one step in a longer branching workflow, rather than hardcoding a number someone has to remember to update, is the more durable answer once pricing depends on where the market sits that day.

A lender working exclusively in one collateral type, accounts receivable, say, can build one borrowing-base calculation and one document checklist and reuse them for every deal. A lender spanning receivables, inventory and equipment needs the workflow to branch by collateral type at intake, since each one pulls different verification data and carries a different advance rate; that branching is where Make's visual workflow holds up better than stacking several separate Zapier connections that all have to stay in sync by hand. Adding a fourth or fifth collateral type later is also easier inside one branching Make scenario than inside a growing pile of separate Zapier connections, each of which needs its own maintenance the moment underwriting policy changes for just one of them.

Once a facility closes, servicing means processing draw requests against a live borrowing base, tracking covenant compliance, and handling payoff requests, all of which touch a borrower's financial data directly. Automating the parts of this that are genuinely mechanical, logging a draw request and updating the borrowing base calculation, while keeping any step that releases funds or changes a covenant status behind a human approval, keeps the automation honest about where it should and should not act on its own. Say a borrower submits a draw request against a receivables-based facility: the workflow can log the request, pull the current aging report and calculate the available borrowing base automatically, but the actual release of funds should still wait on a person confirming the calculation looks right, not on the workflow deciding it does.

A specialty lender running several distinct loan products, each with its own underwriting path and servicing rules, on one shared loan management system is the case where Workato's IT-governed recipe management starts to earn its overhead: a change to one product's workflow can be scoped so it cannot accidentally alter another product's rules. A lender with a single loan product rarely needs this layer of governance yet, and adding centralized governance before you actually have more than one product to govern usually just slows down the team that is still building out the first one.

It is tempting to automate the fastest-looking step first, moving an application straight to an approval queue, but the verification step behind it, confirming financial statements, collateral value and existing liens, is where risk actually lives. Say a workflow routes every application straight to underwriter review the moment it is submitted, without first confirming the applicant's stated collateral value against a pulled report: the underwriter ends up redoing the verification manually anyway, and the automation has added a queue rather than removed one. Sequencing verification before routing, even when it takes longer to build, tends to save far more time than speeding up a step that was never the actual bottleneck.

Sequence the automation with these checks in mind:

• Confirm the borrower's financial statements before the application moves toward an approval queue.
• Confirm collateral value, since both the borrowing base calculation and the real risk in the deal depend on it.
• Check for existing liens on the collateral before treating a facility as approvable.
• Automate the credit pull first, because it is a single, well-defined trigger with no branching.