Cardo AI announced a new cash flow modeling tool for asset-based finance and specialty finance, built as a modern alternative to incumbent structured-finance cash flow engines such as Intex. The tool addresses a problem that has constrained analysts for years: engines designed for public ABS deals that do not fit today’s esoteric asset classes or the needs of insurance investors, who have become major holders of private credit and structured assets.
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“For years, analysts modeling esoteric deals had to either force them into templates that don’t fit or rebuild everything from scratch in a spreadsheet,” said Altin Kadareja, Co-Founder and CEO of Cardo AI. “The tools simply weren’t built to model a deal across its full lifecycle, through ramp-up and reinvestment, against live market data. That’s the problem we solved.”
As private credit has expanded into data center financings, cell tower deals, buy-now-pay-later receivables, music and other royalty rights, and other esoteric assets, the tools used to model these structures have not kept pace. Analysts have been left to adapt legacy engines built for public ABS deals.
The new tool brings structured-credit modeling rigor, waterfalls, tranching, eligibility tests, covenants, and cash flow simulation, to the asset-based and specialty finance collateral that legacy engines were never built for. Purpose-built for esoteric and non-standard asset classes, it pulls live market data directly into cash flow simulation, accrual, covenant monitoring, and stress testing, and lets teams model deal structures across their entire lifecycle, not just the steady state.
During the ramp-up phase, analysts can model the structure as collateral is acquired toward target par, testing how partial portfolios, warehouse financing, and timing affect the waterfall before a deal is fully invested. Through the reinvestment period, the tool models the redeployment of principal proceeds into new collateral under the deal’s eligibility criteria and reinvestment rules, so projected cash flows reflect how the structure is actually managed over time, rather than assuming a static, fully-ramped pool.
Cardo AI’s Reference Rates module supports SOFR, SONIA, EURIBOR, and custom adjusted indices such as “EURIBOR 3M + 20bps.” Powered by Bloomberg data, the module replaces batch uploads with live forward-curve data pulled directly into cash flow simulations and daily accrual calculations, so pricing reflects current market conditions rather than the last manual refresh. The engine accepts forward curves, interest-rate stresses, and custom yield-curve inputs per index and tenor combination, allowing analysts to run portfolio stress tests against live market curves instead of static estimates.
“Insurance investors must run independent scenario analyses to meet regulatory requirements, but today they only have access to static cash flows, which limits their ability to meet those requirements,” said Marco Masotto, Head of Product of Cardo AI. “Bringing live curves and full-lifecycle modeling into a single engine gives them the dynamic cash flows those analyses depend on.”
That capability matters most for Private Credit managers and Insurers, where those cash flows feed a chain of regulatory and accounting processes that legacy engines leave analysts to bridge by hand. The tool is built to produce the quarterly cash flows insurers rely on for income recognition, to support allowance-for-credit-loss (ACL) provisioning under CECL and other-than-temporary impairment (OTTI) assessment, to underpin statutory reserving under NAIC frameworks such as VM-21 and VM-22.
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