A successful systematic credit strategy is not just about predicting returns. It equally relies on accurately forecasting the associated risks and, crucially, the transaction costs, described as avoiding giving a 'liver and a kidney to Goldman Sachs.'

Identifying flawed investments, especially in opaque markets like private credit, is rarely about one decisive discovery. It involves assembling a 'mosaic' from many small pieces of information and red flags. This gradual build-up of evidence is what allows for an early, profitable exit before negatives become obvious to all.

Many firms mistakenly focus on AI outcomes first. True success, as shown by THL Partners, begins with the unglamorous foundational work of establishing a solid data structure, aggregation, and strategy before building tools or chasing insights.

Before implementing AI, organizations must first build a unified data platform. Many companies have multiple, inconsistent "data lakes" and lack basic definitions for concepts like "customer" or "transaction." Without this foundational data consolidation, any attempt to derive insights with AI is doomed to fail due to semantic mismatches.

Instead of treating private credit creation as a black box, analyze it by tracking corporate bond issuance in real-time and observing whether the market is rewarding high-debt companies over quality names. A rally in riskier firms signals a positive credit impulse.

Before deploying AI across a business, companies must first harmonize data definitions, especially after mergers. When different units call a "raw lead" something different, AI models cannot function reliably. This foundational data work is a critical prerequisite for moving beyond proofs-of-concept to scalable AI solutions.

The common practice of bifurcating credit portfolios into 'investment grade' and 'high yield' is an artifact of historical benchmarks and institutional mandates, not an economically optimal approach. A purely systematic view would blend them based on risk characteristics.

A credit investor's true edge lies not in understanding a company's operations, but in mastering the right-hand side of the balance sheet. This includes legal structures, credit agreements, and bankruptcy processes. Private equity investors, who are owners, will always have superior knowledge of the business itself (the left-hand side).

Ex-Palantir lead Alex Boris clarifies the company's 'unsexy' function. Its key is building an 'ontology'—a high-level view defining what each data piece means. This allowed the DOJ to treat a single loan as a trackable object, spotting fraud by seeing it reappear across different mortgage-backed securities.