The global private credit market has experienced significant growth in recent times. At the start of last year there were 436 private credit funds in the marketplace. By October 2020, that number had risen to 520, according to the Financial Times, as investors sought out alternative yield opportunities in response to the surge in public equity and debt markets. The yield on 10-year Treasuries fell to 0.53 per cent at the start of August last year, and while this has crept up to 1.32 per cent (at the time of writing), it hasn’t assuaged investor fears.
In response, alternative fund managers have launched an array of private credit strategies, from opportunistic credit and distressed credit funds, to senior secured loan and mezzanine funds, in a bid to meet investor demand. The result has been a record amount of dry powder, with European private credit managers alone, sitting on USD93 billion of capital at the end of last year, according to Preqin figures.
However, as managers wait to deploy those hard fought investment dollars, they need to think about the operational complexity involved and, crucially, whether they have the system capabilities required to meet the customisation needs of discerning investors.
Customisation is increasingly becoming a calling card for investors with deep pockets. But rather than solely preserving bulge-bracket managers, those with modest AUMs and technology agility have the potential to respond to customisation and excel.
By having robust IT systems that can handle the data management, accounting and valuation aspects of running potentially multiple legal entities, each with their own LP-specific waterfall calculations, managers who explore investment opportunities across the private credit space can do so with the confidence of overcoming any and all operational complexities, and can meet the customisation challenge head on.
Indeed, those who are able to run complex structures with the right technology not only reassure their investors, they also mitigate the risks of overly relying on manual processing and potentially reporting misleading performance figures.