The Hidden Cost Driver in Corporate Health Benefits: Fraud, Waste and Abuse

The Hidden Cost Driver in Corporate Health Benefits: Fraud, Waste and Abuse
June 17, 2026 6 mins

The Hidden Cost Driver in Corporate Health Benefits: Fraud, Waste and Abuse

The Hidden Cost Driver in Corporate Health Benefits: Fraud, Waste and Abuse

Fraud, waste and abuse in corporate health benefits are becoming a material — but often hidden — driver of rising healthcare costs in Asia Pacific. Employers know they must manage spend more effectively but often lack visibility on inappropriate utilisation.

Key Takeaways
  1. AI has moved from hype to reality in APAC, and leaders now need a coherent workforce strategy rather than isolated pilots and efficiency projects.
  2. Many organisations are deploying AI without a clear view of which roles, locations and colleagues will be most affected, creating an AI visibility gap for HR and the C-suite.
  3. HR leaders must reframe AI as a workforce risk and opportunity, using task level insight to guide decisions on skills, careers and rewards and to align the organisation on a people-centred AI narrative.

As employers across Asia Pacific (APAC) continue to grapple with rising medical costs, one theme stood out at Aon’s recent Human Capital Innovation Symposium in Singapore: Fraud, Waste and Abuse (FWA) in corporate health benefits is no longer a marginal issue — it is a material and often hidden driver of healthcare spend.

During the Innovation Showcase, Aon’s Health & Benefits team demonstrated a new FWA Module within our Claims Analytics Platform, designed to help employers identify patterns that traditional approaches often miss.

The Cost Pressure: Medical inflation is cooling, but healthcare costs are not

According to Aon’s 2026 Global Medical Trend Rates Report, medical cost growth across APAC is projected at 11.3 percent this year. While general inflation is cooling across much of the region, healthcare costs continue to rise significantly.

For employers, that pressure is felt most acutely during renewals, where premiums and claims costs often rise faster. A meaningful — and frequently invisible — portion of that differential is driven by Fraud, Waste and Abuse.

  • Fraud – intentional misuse, such as billing for services not rendered.
  • Waste – inefficient use of resources, including unnecessary tests or duplicative treatments.
  • Abuse – patterns of behaviour that may not be legally fraudulent but still generate costs that are not clinically or plan appropriate.

The scale of the issue may be greater than many organisations realise. In a survey of social and commercial health insurance leaders across APAC, the largest proportion of respondents estimated that 30 to 40 percent of health claims may be linked to FWA, highlighting the potential magnitude of avoidable healthcare spend.1

Because these behaviours are often dispersed across providers, members and time, they can be difficult to detect using traditional approaches.

The Visibility Problem: Why traditional FWA approaches fall short

Most insurers investigate FWA on a claim by claim basis: Is the claim valid? Was it coded correctly? Is it supported by medical documentation?

That approach remains necessary, but it can miss broader patterns that only emerge across larger datasets. The challenge is compounded by the wide range of behaviours contributing to FWA. Majority of insurers identified the following issues as particularly significant concerns in the region.1 

  • Overprescribing (~70%)
  • Provider-induced demand (~65%)
  • A lack of consistent care pathways (~60%)
  • Collusion between policyholders and providers to make fraudulent claims (~50%)
  • Daycase work done as inpatient activity (~45%)

These findings reinforce why organisations need to look beyond individual claims and identify broader utilisation patterns that may be driving unnecessary costs.

11.3%

Medical cost growth across APAC is projected at 11.3 percent this year

Source: Aon’s 2026 Global Medical Trend Rates Report

Connecting the Dots: Seeing patterns across markets and carriers

Aon’s new FWA Module sits on top of our Claims Analytics Platform, with one important advantage: access to cross market, cross carrier datasets.

Since we work with multiple insurers and hundreds of employers across APAC, we can benchmark utilisation and cost patterns at a scale that individual organisations often cannot replicate alone.

This allows us to:

  • compare utilisation and cost patterns across markets, industries and providers,
  • identify anomalies that deviate from expected norms, and
  • distinguish between legitimate plan differences and genuine red flags.

Rather than relying on intuition or one-off audits, employers gain objective, data-driven indicators of potential FWA.

The Broker Advantage: Connecting claims, plan design and wellbeing trends

As the broker sitting between client and carrier, Aon is uniquely positioned to connect claims data, plan design and wellbeing trends into a single view.

That perspective matters because anomalies rarely exist in isolation. When unusual patterns emerge, the next question is often “why?”.

  • Is plan design unintentionally encouraging overuse?
  • Is there a member education gap around appropriate care pathways?
  • Or is there a provider-driven concern requiring deeper engagement with the insurer?

Because we understand both client strategy and carrier practices, we can help organisations contextualise anomalies quickly and determine the most appropriate course of action — enabling better decisions around redesigning benefits and tightening controls to targeted communications or focused engagement with specific providers.

Turning Insight into Action: Addressing overutilisation without restricting care

One case study highlighted during the Innovation Showcase illustrates this clearly.

Aon’s Claims Analytics Platform identified a single provider responsible for nearly 10% of a client’s total claims spend — significantly above expected benchmarks for that provider type and population. The data suggested that aggressive direct marketing was contributing to unnecessary utilisation.

Instead of defaulting to an adversarial investigation, Aon facilitated a structured data-led discussion between the client, carrier and provider. Together, they were able to:

  • share evidence of overutilisation,
  • align on expectations for clinically appropriate care, and
  • agree on practical steps to reduce inappropriate usage.

The result: spend associated with the provider fell by nearly half, without restricting access to necessary care for employees.

This was not about denying legitimate claims; it was about improving alignment between care utilisation, clinical appropriateness and plan intent.

From Hindsight to Foresight: Moving from reactive audits to proactive action

Historically, many employers only discovered FWA issues during renewal discussions, after claims costs had already escalated and prompted questions that are hard to answer.

By embedding FWA detection directly into Aon’s Claims Analytics Platform, we’re able to help clients move from hindsight to foresight.

  • Identify problematic patterns earlier. 
  • Quantify their impact on cost and utilisation.
  • Intervene more constructively with carriers, providers, and employees.

In an environment where APAC medical costs continue to grow at double-digit rates, organisations need more than retrospective claims reviews. They need greater visibility into the hidden drivers of spend — and the ability to act on those insights early, collaboratively and strategically.

By turning complex claims data into actionable insight, employers can move beyond reactive cost management towards more proactive, data-driven health benefits strategies that better protect both workforce wellbeing and long-term programme sustainability.

Get in touch with Aon to learn how FWA analytics can help your organisation make better decisions by identifying emerging risks, uncovering hidden cost drivers and translating analytics into practical action across benefits strategy, plan design and claims management.

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