Cardano Advisory has launched a new artificial intelligence (AI) based technology to evolve the way in which covenant assessments are provided to smaller schemes or scheme sections.
The tool, named Discover, aims to use Cardano’s database of historical covenant assessments, alongside AI technology, to provide automated assessments and reports for schemes to assess their covenant strength.
Cardano stated that Discover has been designed for schemes where high-level covenant analysis may be more appropriate, including schemes that have less than £50m in assets and simple covenant structures.
The tool can also conduct AI-led reviews of the underlying employers of sectionalised or non-associated multi-employer schemes, including the Local Government Pension Scheme.
It aims to provide an employer covenant rating based on quantitative and qualitative factors, examining the “main drivers” underlying the rating, benchmarking rating metrics, and providing corresponding recommendations.
Discover can also be used to support triennial valuations, scheme return submissions, ongoing monitoring and risk assessments, according to Cardano, as well as explore the covenant impact of ‘what-if’ scenarios, for example an improvement in the sponsor’s trading or an increase in its debt.
“Discover is a further step towards modernisation within the pensions industry, as we continue to harness new technology to build tools and solutions for schemes,” commented Cardano Advisory director, Felix Mantz.
“With our wealth of data, we can increasingly harness this through AI to bring innovative risk management solutions to trustees, employers, and members in a cost-effective manner.
“Traditional covenant reviews involve in-depth analysis from a team of dedicated covenant professionals and can be out of reach for smaller schemes due to costs.
“Given the importance of covenant to member outcomes, and with an increased focus from The Pensions Regulator on covenant, we believe that Discover will make external covenant analysis more accessible and affordable to smaller, simple, or sectionalised schemes.”
