Sound Point signs $720m forward-flow deal with Pagaya's AI lending network
January 26, 2026
Sound Point Capital Management has executed a $720m forward-flow agreement with Pagaya Technologies to purchase point-of-sale (POS) consumer loans sourced via Pagaya's AI-driven lending platform. The $45bn alternative credit manager gains scaled access to short-duration consumer assets while Pagaya secures stable takeout for its origination partners.
This is Pagaya's first forward-flow transaction for its POS program. It follows the firm's AAA-rated POS evolving asset-backed securitisation initiative launched in May 2025, which has built $3bn in prospective funding capacity to date.
"Pagaya has built a differentiated, institutional-grade platform for accessing consumer credit," said Philip Bartow, head of specialty finance and fintech lending and portfolio manager at Sound Point. "We're excited to partner with Pagaya to support the continued growth of its point-of-sale strategy, while offering our investors consistent exposure to short-duration assets with highly attractive risk-adjusted returns and durable income."
What this means in practice
A forward-flow deal sets predefined criteria for loans that will be purchased over time, giving the buyer predictable volume and the seller dependable funding. In parallel, Pagaya's POS assets can be packaged into an evolving asset-backed securitisation program, expanding capacity and distribution. For a primer on ABS structures, see Investor.gov: Asset-Backed Securities.
Pagaya applies AI models to identify higher-quality consumer loans and match them with funding partners across personal loans, auto, and POS financing. Learn more about the company's platform at Pagaya Technologies.
Why it matters for management
- Predictable deployment: Forward-flow provides line-of-sight on loan volume, helping portfolio managers and treasurers plan liquidity and duration.
- Short-duration income: POS loans typically amortize quickly, supporting steady cash generation and faster reinvestment decisions.
- Risk calibration: Underwriting is model-driven, but performance still hinges on vintage mix, merchant verticals, and macro conditions (employment, rates).
- Data and oversight: Demand granular performance data (approval criteria, score bands, loss curves, prepayment rates) and clear model governance documentation.
- Counterparty and ops: Review origination controls, servicing capacity, and backup servicing. Confirm rep/warranty frameworks and dispute resolution timelines.
- Compliance: Assess fair lending controls, data privacy, and disclosures across banks, fintechs, and merchants in the chain.
Key questions to ask your team
- How do loss, delinquency, and recovery assumptions shift under mild and severe recession scenarios?
- What is our max exposure by merchant, sector, and model version? Are concentration and correlation risks capped?
- How will funding costs and advance rates move if spreads widen or ratings change?
- What KPIs do we track weekly? (e.g., early-stage delinquencies, roll rates, cohort losses vs. model, payment method mix)
- Do we have audit rights, data SLAs, and clear triggers for tightening eligibility or pausing flow?
What to watch next
- Vintage performance: Q1-Q2 2026 cohorts as consumer savings normalize and promotions shift at checkout.
- Funding stack: Pace of future securitisations from the POS program and appetite from AA/AAA buyers.
- Macro sensitivity: Employment, revolving credit utilization, and rate paths affecting approval and loss curves.
- Regulatory signals: Any updates around BNPL/POS disclosures, servicing practices, or data usage.
If you're upskilling leaders and teams on practical AI for finance, see our curated tools list: AI tools for finance.
Your membership also unlocks:
