The partnership arrives at a moment of acute financial strain across the UK's healthcare insurance market. Spending on private medical insurance has surged 50% since the COVID-19 pandemic, rising from £6.3 billion, as pandemic-related disruption fuelled a sharp increase in NHS waiting times and prompted more large employers to offer private cover as an employee benefit.
Under the agreement, Marsh clients will gain access to Kirontech's retrospective analysis of historic claims data. Combining machine learning with medical expertise, the platform scrutinises treatment records and insurer-approved care plans to identify areas of waste, fraud and abuse, flagging issues affecting an estimated 10% to 30% of claims value.
Founded in Cambridge, Kirontech combines deep medical knowledge with big data and machine learning to detect inefficiencies and inappropriate spending in health insurance payments. The partnership reflects a broader shift across healthcare insurance from reactive claims management towards more proactive, data-driven oversight of clinical spend.
"Healthcare organisations are sitting on data that could transform how they manage risk and allocate resources, but turning that into confident, board-level decisions requires the right expertise alongside the right technology," said Omar Chebli, Kirontech's chief executive officer. "Collaborating with Marsh means we can bring that full picture to a much wider range of organisations, faster."
Corporate and group policies accounted for a 67.56% share of the UK health and medical insurance market in 2025, making employer-funded schemes the largest pool of health insurance spending in the country. Demand for private cover is also expected to continue growing. Research from employee benefits consultancy Broadstone found that almost a third of employers without private medical insurance are considering introducing it within the next three years, while the proportion with definite plans rose from 9% in 2023 to 11% in 2025.
As employers assume greater responsibility for healthcare costs, demonstrating effective oversight of claims spending is becoming increasingly important. For organisations operating health trusts or self-insured arrangements, unnecessary spending translates directly into higher costs, heightening demand for analytics capable of identifying claims leakage and inefficient care pathways.
The implications extend beyond commercial insurance. The NHS Counter Fraud Authority estimates that fraud, bribery and corruption cost the public purse around £1.21 billion annually, a figure that excludes the wider costs associated with low-value care and clinical waste. With healthcare budgets under pressure across both public and private systems, technologies designed to improve spending efficiency are attracting increasing attention.
The collaboration also highlights how artificial intelligence is becoming more deeply embedded in healthcare risk management. Rather than functioning solely as a post-claims tool, analytics are increasingly being used to provide continuous oversight of clinical spend and support more informed decision-making.
Around 63% of healthcare providers have now introduced AI into administrative workflows, according to Howden's analysis of the UK healthcare AI landscape.
The deal may also signal an evolving role for brokers. As employers shoulder rising healthcare costs through large group schemes and self-funded structures, brokers are increasingly expected to deliver cost-management and analytical capabilities alongside traditional placement and advisory services.
For brokers advising corporate clients, particularly those with healthcare trusts or self-insured arrangements, demonstrating data-driven oversight of medical spending could become an increasingly important differentiator at renewal.
The UK private healthcare market was valued at US$13.75 billion in 2024 and is projected to reach US$18.56 billion by 2033.
The Kirontech collaboration marks the latest expansion of Marsh's UK healthcare proposition, positioning its consulting arm at the intersection of two of the insurance sector's most pressing challenges: controlling medical inflation and using AI to deliver more intelligent healthcare risk management.
