A business wanted the space, but approving it meant weeks of collecting statements, records and references by hand. By the time finance finished, the prospect had cooled or signed elsewhere, and the office stayed empty. The screening that was meant to protect the firm was quietly costing it the deal.
Every prospective tenant triggered a manual due-diligence check, and gathering the financial statements, company records, bank statements and references by hand stretched underwriting into weeks, during which prospects went cold and offices stayed empty.
The checks themselves rested on gut feel. Finance staff judged credit risk from whatever data they managed to collect, often incomplete, which meant tenants with shaky cash flow slipped through and later fell behind on rent while genuinely strong ones waited in the same slow queue.
And the team was the bottleneck. At month-end the finance department buckled under the volume of approval requests, so the process that was supposed to guard the firm's income was the very thing capping how fast it could fill space.
Pull the data automatically, auto-approve the clearly safe, and hand a finance manager only the applicants that actually need a judgement.
The applicant sends a link or a file of their documents, and the engine connects in seconds to legitimate external sources, open-banking data, a business-credit bureau and the companies registrar, so nobody spends days chasing statements and references by hand.
A machine-learning model reads the applicant's trading history, cash flow and debt ratios and produces a consistent risk score, replacing the incomplete, gut-feel assessments that let shaky tenants through and made strong ones wait.
Businesses the model classes as low-risk are approved automatically with no human touch, so a strong prospect gets a yes in minutes and signs while they are still keen, rather than cooling off over a two-week wait.
Medium and high-risk applicants are routed to a finance manager with a focused summary that marks the exact warning signs, a loan maturing soon, frequent changes of chief executive, so the human spends their time on the real question, not on assembling the file.
The model clears the obvious and surfaces the doubtful; a finance manager still makes the call on anything borderline, so the speed never comes at the cost of who approved a risky tenant and why.
A smooth, modern onboarding replaces the bureaucratic slog that used to lose prospects at the last step, so more deals close on the spot and fewer offices sit empty waiting on paperwork.
Prospects sign while they're keen, and the risky ones still get a human eye.
Due diligence fell from about ten business days to fifteen minutes for auto-approved applicants, which stopped prospects cooling off in the queue and lifted immediate deal closes 25%, turning empty offices into signed leases faster.
A consistent, data-driven score sharpened risk decisions and cut late payments and bad debt about 40%, while the finance team was freed from clerical data-gathering to focus on the exceptions that genuinely need judgement.
Auto-approving the clearly safe is only responsible when the doubtful applicants are pulled out and handed to a person with the red flags already marked. It is the same green-lane risk-underwriting discipline behind our credit-underwriting work, aimed at commercial tenants and empty offices rather than a bank's loan book.
A bank's manual onboarding took weeks and lost clients. A secure data pipeline plus document AI and a green-lane approval cut onboarding to 24 hours, lifted conversion and cut false alerts.
Read the case → AML & onboarding · FintechManual screening was slow and inconsistent, and the risk slipped through. Automated checks cleared the honest customers fast and held the ones that needed a human eye.
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