Reduce missed-installment losses — automated recovery sheets + AI reconciliation

Merchants and lenders that offer installment plans—from SMB retailers to enterprise BNPL providers—regularly lose revenue and spend disproportionate labor hours when customers miss payments. Across an estimated 2,000,000 merchants and lenders with an average contract value around $9,000, the aggregate addressable market for reducing missed-installment losses is roughly $18.0B; the failure modes are fragmented payment records, poor reconciliation, and ad hoc collections workflows that don’t scale. You could build a platform that produces automated recovery sheets (actionable, reconciled customer/payment ledgers) paired with AI-driven reconciliation and propensity models to prioritize accounts and automate compliant outreach via email, SMS, and payment links. Tight integrations with modern Payments APIs (Stripe, Adyen, etc.) plus ML for dispute detection and individualized messaging would cut manual reconciliation costs, speed recoveries, and create auditable trails for compliance. This is an attractive moment: BNPL expansion increases demand for installment recovery tools, payments API standardization reduces integration friction, and AI advances make personalized, compliant collections practical—our market score is 90/100 and revenue potential 82/100, with medium competition indicating room to specialize. Strengths include measurable ROI and high ACV customers; challenges are data heterogeneity, regulatory risk around debt collection, and the need to prove value in pilots. If you can secure 5–10 pilot partners and embed with one or two major processors to validate KPI improvements, this is worth pursuing, but expect a 12–18 month, compliance-heavy build and sales cycle before meaningful scale.