As generative AI becomes a powerful tool in Private asset-backed finance (ABF), the need for precision and transparency is more critical than ever. At RiskSpan, we’re applying Large Language Models (LLMs) to automate and accelerate private ABF deal modeling and surveillance. But speed is only half the battle—accuracy is non-negotiable.
That’s where Human-in-the-Loop (HITL) validation plays a vital role. While the RiskSpan platform incorporates sophisticated AI guardrails, we believe the right blend of automation and expert oversight ensures results that are not just fast—but reliable, auditable, and production-ready.
The AI-Powered Workflow: What’s Automated
Our private ABF modeling and surveillance system uses LLMs to handle several critical tasks:
- Data Extraction: Parsing offering memos, indentures, and loan tapes to extract structural and financial data.
- Deal Code Generation: Producing executable waterfall models based on extracted rules.
- Database Ingestion: Uploading validated deal terms and triggers into the RiskSpan system of record.
- Surveillance Automation: Running periodic deal performance analyses and compliance checks.
But What About Hallucinations?
Generative models are powerful but imperfect. Without the right controls, they can fabricate securitization tranches or fees that are not present in the deal documents. They can also misinterpret waterfall rules or omit critical override conditions or generate semantically incorrect code for cashflow models. To address these challenges, RiskSpan employs a multi-layered safeguard framework, combining asset class based extraction; LLM-as-Judge; Rule-Based Guardrails and Inline Human Review
Humans in the Loop: Three Layers of Oversight
We’ve embedded human validation at three key points in the deal lifecycle:
- Pre-Modeling Validation: before LLM-generated outputs are finalized, RiskSpan analysts review extracted terms and model prompts—correcting anything misaligned with the source documents.
- Inline Oversight: during waterfall code generation, humans validate AI-generated logic in context, ensuring correct treatment of subordination, triggers, caps/floors and other.
- Post-Deployment Monitoring Surveillance: outputs are reviewed both by the RiskSpan team and client-side structuring or credit teams. Feedback is looped back into model tuning and prompt optimization.
Looking Ahead: RAG and Continuous Improvement
We’re actively exploring Retrieval-Augmented Generation (RAG) to reduce hallucinations even further. By grounding AI responses in deal-specific material such as offering documents, trustee reports, and internal risk memos—we aim to: 1) eliminate off-topic responses. 2) increase trust in model-derived outputs and 3) enable deeper customization per issuer or asset class.
The Takeaway
AI is transforming how private ABF deals are modeled and monitored—but it must be grounded and guided by human expertise and built for institutional rigor. At RiskSpan, we’re not just accelerating workflows—we’re raising the bar for accuracy and trust in AI-assisted private structures. Human-in-the-loop is not a fallback—it’s a strategic pillar. Want to see how our AI platform works in action? Reach out to schedule a demo or contact your RiskSpan representative to learn more.
