This means that as the private credit market gains more steam, the lines between traditional securitization markets and alternative lending are blurring — creating both opportunities and challenges. GlobalCapital spoke to David Oh, Product Manager, PolyPaths at Numerix, on how best to navigate private credit’s growing momentum and the role of synthetic risk transfers (SRTs) in this environment
How is private credit evolving beyond traditional direct lending — and what risks and vulnerabilities are becoming more visible?
David Oh, Numerix: The growth of private credit was spurred by post-crisis capital requirements and balance-sheet pressures from banks, creating opportunities for private lenders to provide faster and more customised financing. As investors continue searching for yield, private credit delivered attractive spreads with minimal volatility, particularly for cash-rich insurance companies, funds, and even retail investors. But the rapid rise of AI has unveiled exposure to software and SaaS sectors, undermining the promise of steady and known cashflows with performance downturns and defaults. In turn, several private credit vehicles have recently marked down portfolios, cutting down their NAV per share. But private credit has still become a vital player in the explosion of AI as it finances data centres, infrastructure, and semiconductor ecosystems. It has grown rapidly over the past several years, and without significant guardrails in place, nor a history and established precedent for performance, the past few months have been learning moments, but growth is not impeded. As investors become more familiar with the limitations and considerations that come with private credit, technology that enables more meaningful insights about its valuation and risk are more invaluable.
While private credit has grown rapidly, data quality and transparency are cause for concern. What analytical techniques are becoming essential for investors who need to model downside/risk scenarios with limited information?
The question of transparency is unique. Private credit funds should have perfect visibility into the performance of these assets, but the reality is that there is limited data and much of it can be obscure. With the software and SaaS exposure, many investors have had uncertainty around fair valuations and default risk around private credit. AI tools have helped in minimising operational risk by extracting and validating data from documentation into something more structured and analytical. However, as investors practice caution, it is not uncommon to evaluate loan-level credit/default scenarios to better understand not just the plausible range of fair values but also to model potentially non-linear sensitivities to various macroeconomic factors. Using tools such as loan-level ESG scenarios, investors can drill deeper into drivers of fair value of private loans, while managing the impact on the fund’s liquidity and performance.
How are banks using SRTs not just for capital relief, but as a strategic tool to revamp their credit portfolios in a higher rate, higher volatility environment?
SRTs allows banks to offload the loan portfolio’s losses up to an agreed threshold, but the bank still retains the loan relationship. By purchasing credit protection on a defined tranche of losses, SRTs are able to reduce risk-weighted assets (RWAs) and free up regulatory capital. In higher-rate and higher-volatility environments, SRTs are becoming increasingly attractive as banks try to preserve their lending capacity, while also facing weaker borrower credit quality and even higher expected losses. Given higher volatility with geopolitics, as well as corrections underway to various technology sectors, SRTs have become a strong vehicle for banks to tactically revamp their portfolio composition to reduce exposure to vulnerable sectors.
Private credit, SRTs, and CLOs all rely on understanding how portfolios behave under extreme but plausible scenarios. What common modelling challenges cut across these markets, and where do you see the most innovation?
The key challenge for private credit, SRTs, and CLOs is understanding uneven loan performance, which is to say not only is there uncertainty caused by concentration risk, but valuation of these products are heavily driven around assumptions about the default timing and correlation behaviour during stress. Furthermore, higher interest rates and higher volatility have led to uneven loan performance. For collateral managers, AI has been a powerful tool to provide more operational efficiency and transparency. For the investor, scenarios which stress defaults are not enough anymore, and there is greater placement in understanding second-order system risks such as recovery analysis, ESG-driven credit spreads, and liquidity stress.
What is your outlook for the SRT market in 2026?
Through 2026, I expect the SRT market to continue its growth. It has already transitioned from one-off vehicles for regulatory optimisation into a strategic capital management tool. As the Basel output floor phases in, more banks will be increasingly under pressure to mitigate credit RWA. As investors continue to look for yield and as banks remain capital constrained, SRT issuance should continue to grow, though there may be a necessary maturation of the relationship. This means investors may become more selective and scrutinise loan-level risk more deeply while demanding wider spreads and/or thicker protections for concentrated portfolios or risky loans.