High Street banks fall behind newer players in alva’s latest ESG benchmarking report
- High Street banks HSBC (-41), Lloyds (-40), Barclays (-24) and Natwest (-59) fall far below the sector average due to a failure to meet a variety of ESG challenges including limited adaptability due to scale in a COVID world and branch closures, and the concern for the elderly and vulnerable being left without access to banking services
- High Street banks are struggling to keep up with the less established banking players Paragon (+58), Shawbrook (+42) and Virgin Money (+37) who lead the ESG index due to a stronger focus around greener products such as energy efficient mortgages and adaptability capabilities
- Virgin Money has moved up the ESG benchmarking rank by an impressive +15 places, thanks to its outstanding ESG efforts and to launching Europe’s first ever sustainability linked loans and capabilities in renewable energy
- UK banks in general are focussing on launches of greener products for customers this year including green financing, increased pledges against fossil fuel financing and more energy efficient mortgages on offer.
alva’s report entitled ‘UK Banks ESG Intelligence report’ analysed and benchmarked 20 of the UK’s largest influential and high-street banks and has ranked them based on their ESG efforts to identify competitor advantages or highlight risks to generate returns. The alva ESG™ score is calculated in real-time on a -100 to +100 range and combines sentiment and materiality in relation to ESG topics helping stakeholders make better informed decisions. The findings of the report are calculated through the use of advanced technology including: natural language processing, industry expert analysis, machine learning and high-speed real time data monitoring.
alva analyses over 25 million pieces of publicly available content from print, online, broadcast and social media sources, regulatory disclosures, NGO communications and many more, classifying them by the SASB sector standard issues taxonomy. Each piece of content is then analysed for sentence-level sentiment based on a combination of machine learning and natural language processing. The content is also given a materiality score incorporating the volume of coverage, the influence of the source and the prominence of the issue.