CoVenture Credit - Esoteric Credit with Ail Hamed, Brian Harwitt, and Marc Porzecanski - [Invest Like the Best, EP.108]

Episode Summary

Executive Summary: Patrick O'Shaughnessy interviews CoVenture Credit's Ali Hamed, Brian Harwitt, and Mark Porzikansky on esoteric lending. They explain how technology-enabled niches can create new loan products with data moats, switching costs, and strong risk-adjusted yields.

Main Topics: Why CoVenture focuses on 'Lending 2.0' (Priority: 10/5): They seek new credit products enabled by tech, not commoditized loans with shrinking spreads. Moats in specialty lending (Priority: 9/5): Switching costs, unique data, and platform influence can protect yield and reduce default. Examples of niche products (Priority: 9/5): Returnly, payroll deduction lending, and produce financing show how new data creates credit edges. How venture and credit intersect (Priority: 8/5): Equity insight helps source deals and underwrite origination risk before traditional lenders arrive. Structuring and downside protection (Priority: 10/5): ABL, forward flow, covenants, and borrowing-base controls are used to limit loss. Monitoring after funding (Priority: 9/5): Performance tapes, on-sites, triggers, and servicing plans matter as much as initial underwriting. Bitcoin and other uncorrelated collateral (Priority: 7/5): They see liquid, mark-to-market assets like Bitcoin as financeable under the right LTV and custody.

Key Arguments: Lending 1.0 failed to democratize yield; institutions still dominated those platforms. The best credits observe data others can't, creating order-of-magnitude default improvement. Switching costs matter more than tiny rate cuts in protecting a lending moat. Seeing equity and debt helps judge both product-market fit and credit durability. Origination risk is central: the platform must actually attract borrowers, not just look safe. Short-duration assets reduce macro exposure and can still generate mid-to-high teens returns. Post-close monitoring and covenant enforcement are as important as initial underwriting.

Data Points: Lending 1.0 platforms: Lending Club, OnDeck, Prosper, SoFi - Examples of the first wave of online lending discussed by Ali Hamed Data points collected by some lenders: 150 data points - Lenders claimed to use many borrower variables, but few had real signal Signal-bearing data points: 3 - Ali said only about three of those 150 data points actually mattered Returnly purchase lift: 4 times - Instant return/refund improved likelihood of another purchase Returnly processed instant refunds: over 200,000 - Used to build proprietary returns and borrower behavior data High-deductible health plans: 140 million Americans - Size of the market potentially needing deductible financing Emergency expense affordability: nearly 50% of Americans can't afford a $400 emergency payment - Used to frame demand for alternative credit solutions Payday loan shop density: more payday loan shops than there are McDonald's and Starbucks combined - Illustrates scale of payday lending in the U.S. Typical FICO cutoffs in consumer lending: 660 or 680 - Shows that many so-called mass-market loans still serve prime borrowers Bitcoin loan pricing: 12% to 25% APR - Ballpark pricing mentioned for Bitcoin-backed lending Asset coverage multiple: 2 to 7x - Typical coverage range they aim for in their deals Default rate example: 5% base case / 10% stress / 2x coverage - Illustrative modeling example used by Brian Loss tolerance example: 14-15% default before income loss; 30% before principal loss - Example from a specific deal with strong coverage Collateralization example: 90 cents on the dollar - Typical ABL overcollateralization example Borrower utilization trigger: 8% default rate - Example of when funding would stop Default collection trigger: 12% default rate - Example of when they would default on the loan and collect receivables Payroll timing: every two weeks - Used to motivate factoring and payroll-deduction product design

Pivotal Quotes: "in venture capital, your job is to make money and in credit, your job is to not lose it." — Ali Hamed: Contrasting equity investing with credit underwriting "the best marketing tool we have is those people that we passed on." — Ali Hamed: Explaining reputation-building and referral flow among founders "we will get fed by somebody at some point." — Ali Hamed: Describing why vigilance, structure, and monitoring are essential

Implications:* The frontier is not more generic lending, but better structures, data, and servicing discipline; listeners should look for moats, not just high yields.