Cracking Private Credit's Babel Problem: Cardo AI's Bid to Standardize a $5.8T Market

NEW YORK, NY – October 27, 2026 – The journey from an innovative financial concept to a highly liquid, commercially viable asset class is almost always paved with a decidedly unglamorous material: standardized data. In the booming world of asset-based finance (ABF) and private credit, the absence of this foundational infrastructure has created a multi-trillion-dollar bottleneck. Today, fintech platform Cardo AI is attempting to break that logjam, introducing a unified data standard covering 30 collateral types that span approximately $5.8 trillion of the ABF universe.

The New York-based firm, which currently manages over $115 billion in assets under its technology umbrella and processes trillions of data points across 160 external systems, has rolled out a library covering 11 consumer and 19 corporate exposures. The goal is ambitious but essential for market maturity: ensuring that a loan tape, a covenant compliance test, or a payment waterfall calculation means the exact same thing whether the underlying asset is a pool of consumer auto loans, a fleet of leased aircraft, or a catalog of music royalties.

“I have frequently seen good deals stall simply because the two sides interpreted the underlying collateral data differently,” comments Altin Kadareja, Co-Founder and CEO of Cardo AI. “We built these standards so that a loan tape, a covenant, or a waterfall calculation means the exact same thing to everyone reviewing it, regardless of the underlying asset class.”

The $5.8 Trillion Tower of Babel

To understand the commercial significance of Cardo AI's product launch, one must first understand the sheer scale and messiness of the asset-based finance market. Estimates for the total ABF market size vary significantly across different sources, reflecting the broad and sometimes indistinct nature of the asset class. While some conservative estimates place the private ABF market around $5.2 trillion, other major institutional players suggest the broader addressable market, encompassing various forms of specialized lending, could be upwards of $13 trillion to $25 trillion by the end of the decade.

Despite this explosive growth, the sector suffers from a profound "Babel problem." Historically, each asset class in ABF has operated under its own bespoke data formats, terminology, and reporting conventions. A mid-market corporate loan looks entirely different on a spreadsheet than a portfolio of equipment leases or franchise financing.

For institutional allocators—pension funds, endowments, and sovereign wealth funds—this fragmentation is more than an operational headache; it is a barrier to entry. Cross-asset comparison becomes a manual, error-prone exercise. Deal onboarding slows to a crawl. The lack of standard reporting conventions creates operational risk and stifles secondary market liquidity, keeping ABF from achieving the seamless trading velocity seen in public equities or even syndicated loans.

From Aircraft to Royalties: Pricing the Esoteric

Private credit has expanded far beyond traditional direct lending. The sheer diversity of modern alternative collateral presents a massive quantitative challenge. How do you price risk consistently across such niche asset types?

The newly introduced standards establish unified definitions for eligibility criteria and payment waterfalls using open formulas. A payment waterfall dictates the priority of cash flows to different tranches of debt and equity. In a music royalty deal, this might depend on streaming volume thresholds across different geographies. In an aircraft lease, it depends on maintenance reserves, utilization rates, and depreciation schedules. Standardizing the formulas for these wildly different cash flow drivers into a single, shared data model is a massive undertaking.

By mapping esoteric collateral to a universal framework, the technology provider is attempting to equate the risk profiles of highly diverse portfolios. This allows lenders and institutional investors to evaluate a music royalty deal on the same fundamental basis as a commercial real estate bridge loan or a consumer credit portfolio.

As one structured finance analyst at a major alternative asset manager noted, "If you cannot trace a waterfall calculation back to its source field without hiring a team of forensic accountants, the asset class will never scale to its true potential. We currently spend half of our due diligence period just arguing over data definitions and reconciling bespoke spreadsheets."

The Vendor vs. Consortium Conundrum

The history of financial standardization is littered with failed attempts. Typically, successful standards are born from industry consortia like the Loan Syndications and Trading Association (LSTA) or the Structured Finance Association (SFA). The LSTA, for instance, has been highly effective at standardizing syndicated loan documentation. However, these bodies have yet to produce a comprehensive, open-formula data model spanning the wild frontiers of niche ABF collateral.

Groups like the Alternative Credit Council (ACC) heavily emphasize the need for transparency and data, particularly in discussions with regulators, but their focus leans toward market development and policy rather than granular, asset-level data engineering. This leaves a vacuum that commercial vendors are eager to fill.

Firms like Alter Domus, Oxane Partners, and Canoe Intelligence have long provided data normalization capabilities, but typically as proprietary processes housed within their own software ecosystems or specialized extraction services. Cardo AI is taking a different route by actively publishing a standard.

Can a single commercial fintech platform dictate the rules of engagement for a multi-trillion-dollar market? Institutional investors are notoriously wary of vendor lock-in. Adopting a proprietary data schema from a single provider can make switching costs prohibitively high. For the new framework to gain critical mass, it must be perceived as a neutral, robust utility rather than a clever customer acquisition tool. Large private credit managers have invested heavily in their own proprietary data infrastructure and may be reluctant to abandon bespoke internal systems for an external benchmark unless the operational efficiencies are undeniable and universally recognized.

Open Source or Proprietary Funnel?

The commercial viability of this standard hinges entirely on the definition of "open." The company claims its framework uses "traceable open formulas" that can be followed from source fields to final calculations. Each asset class standard is reportedly available to lenders and investors on request, complete with a shared data model, entity definitions, and worked examples against a representative pool.

However, the transition from a highly transparent logic framework to a truly open-source industry standard requires rigorous governance. If the formulas are merely conceptual and require the firm's specific software to execute efficiently, the initiative may be viewed more as a proprietary onboarding funnel than a public good. True market-wide adoption will necessitate clear, independent governance structures, open-source licensing, and the ability for independent quantitative modelers to implement the standards without a commercial contract.

The race to standardize private credit is no longer just an IT concern; it is the defining commercialization hurdle for the next decade of alternative asset growth. As regulatory pressures mount and institutional capital continues to flood the private markets, the demand for transparency will only intensify. Whether Cardo AI's unified data model becomes the definitive lingua franca of asset-based finance or simply another ambitious but siloed fintech solution will depend heavily on the industry's willingness to collaborate. In a market built on bespoke deals and informational advantages, convincing fierce competitors to speak the exact same language may be the most difficult waterfall calculation of all.