Private Markets Risk Modelling

LGPS Central Limited is one of the UK’s Local Government Pension Scheme asset pools. Founded in 2017, LGPS Central brings local authority pension funds together to invest collectively, combining scale, expertise and strong governance to help deliver long-term value for scheme members, employers and stakeholders. This market engagement exercise is intended to explore the range of private markets risk analytics solutions available, assess how these services could meaningfully and practically enhance LGPSC’s existing risk oversight framework, and help refine our requirements should we subsequently proceed to a formal procurement. We are inviting providers to describe their capabilities across private markets risk analytics. Areas of particular interest include but are not limited to exposure and concentration monitoring, scenario analysis and stress testing, liquidity and cashflow forecasting, and risk modelling. We also welcome information on other capabilities, methodologies or approaches that providers consider relevant to effective private markets risk oversight. Through this engagement, we would like to understand: • The asset classes currently covered, together with any planned enhancements or future developments. • Typical client profiles and use cases, including practical examples of how asset managers and institutional investors use the solution to support private markets risk oversight. • The nature of the outputs available, including reporting capabilities and the scope for developing tailored reports. • How the solution could integrate with and complement existing portfolio management and data platforms. • Any additional capabilities or approaches that providers believe could enhance private markets risk oversight. This is a fact-finding exercise intended to help the Authority better understand the market, available solutions and delivery approaches, and to inform and refine its requirements. This exercise does not form part of any supplier selection, evaluation or procurement process