Insurance has five problems – and AI isn’t one

While billions trust their lives and livelihoods to insurers every day, the insurance sector itself is facing an existential crisis.

Record losses due to more frequent and severe natural disasters have skyrocketed premiums and deductibles. Carriers face significant backlash as policyholders scramble to find coverage in abandoned, high-risk markets. Meanwhile, analysts warn that the rate hikes and non-rate underwriting actions taken to navigate these market conditions will prove unsustainable in the long term.

In search of workarounds, some insurance leaders pinned hopes on the generative AI (GenAI) boom for quick fixes. However, allegations of faulty algorithms rendering unfair denials have fueled critics, who claim that AI has become just one more problem in an already fraught landscape.

The truth, per SAS' Stu Bradley, is more nuanced.

"While insurance leaders will certainly encounter obstacles as they advance their analytic maturity, AI itself isn't the problem," said Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions. "Rather, organizations' understanding of their data and the potential unintended consequences of AI is the root of the issue. With the appropriate ethical guardrails and human oversight, trustworthy AI is a solution, delivering the insights and agility needed to redefine the industry."

At this potential turning point, experts from SAS have examined and offered insights on thetop five insurance problems - and using AI isn't one of them. Confronting the insurance community's current technology challenges won't only bring AI closer to its full potential; it will also help future-proof the sector.

1. Data chaos awaits law and order.

When data points align with private personal details, the current lack of legislation and regulation governing the use of artificial intelligence can feel unsettling - especially for an industry so steeped in compliance and regulatory reporting. Language in the EU AI Act, China's Interim Measures and the NAIC AI Model Bulletin are among the first efforts to establish AI guardrails in insurance, but with the regulatory landscape in flux, insurers and insurtechs are stepping into the breach with proposals for self-governance.

"To set the stage for meeting regulatory standards yet to come, the insurance industry, like the banking sector, must prioritize data lineage and governance within its AI capabilities," said Prathiba Krishna, AI and Ethics Lead for SAS UK and Ireland, who helped author the voluntary AI Code of Conductfor claims. "As important as it is for insurers to extract valuable insights from their large datasets, it's equally important to cleanse this data of errors and inconsistencies. This helps ensure reusability, improve decision-making accuracy, boost productivity and reinforce reliability of results.

"AI education will also be a key determinant in successful AI deployment and in preparing for future compliance. Fostering data literacy across the organization empowers the entire enterprise to discuss, understand and ultimately embrace ethical AI practices."

2. AI overload strains risk management.

Amid the industry's rapid digitalization and the explosive growth of AI and generative AI, risk managers are rightly concerned about the unintended consequences of robot algorithms - particularly as business leaders race to translate AI productivity gains into long-term business value.

Prototyping may look promising, but production AI requires robust infrastructure to ensure responsible and safe deployment. Meanwhile, "black box" AI solutions that limit customization may appeal to executives for the perceived simplicity, but the lack of transparency and explainability exposes the organization to considerable AI risk.

"Insurers must delve deeper into the importance of integrating AI into existing systems within context while aligning with an enterprise AI strategy with strong governance," said Terisa Roberts, Global Lead for Risk Modeling and Decisioning at SAS, and author of the book, Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning. "Insurers must also consider the broader scope of GenAI use cases beyond large language models. Effective applications of synthetic data generation, for example, could strengthen data privacy while optimizing pricing, reserving and actuarial modeling."

3. Indemnification tunnel vision stalls progress and partnership.

A paradigm shift is brewing in insurance - that is, if the industry can reach the required critical mass. The tech sector foresees carriers evolving from reactive indemnifiers to proactive partners with their policyholders, consumers and businesses alike.

Consider the following use case: the World Health Organization recently reported a staggering proportion of health events - over 30% of global cancer deaths and 80% of chronic diseases - are tied to preventable habits. Meanwhile, insurers already collect extensive health data on their customers to offer appropriate coverage. Why not put this data to use?

"Through existing channels like smartphone apps, insurers could offer customers the opportunity to opt in to AI-powered health coaching, delivering tailored advice that reinvents the conventional customer experience - and reduces policy payouts," said Alena Tsishchanka, Senior Insurance Practice Leader for EMEA and AP at SAS. "Beyond wellness services, insurers should also strongly consider the market potential of like partnerships for climate change and ESG. Not only could such initiatives help address insurers' solvency issues; they could greatly enhance public perception of the industry."

With appropriate ethical guidelines in place, the insurer-as-partner model is far from a fantasy. Cutting-edge insurtechs and parametric insurance policies operate in a similar vein, making this a novel path for forward-thinking insurance players.

4. Hidden digital risks require centralized solutions.

With near-ubiquitous smartphone technology, an insurer today can reach markets wherever wireless exists. For insurers looking to offset premium increases for customers, investing in digital migration helps put modern offerings for modern customers on the market.

However, to compete in this digital marketplace, insurers must offer increasingly individualized products and services to policyholders. The appeal of these tailored offerings and ease of signing up online has lured a slew of potential customers, which has proven a blessing and a curse.

Carriers are inundated by the volume and speed of applications. Unfortunately, in the race to approve or deny coverage, insurers don't have enough time to properly research and identify clientele most likely to commit fraud or who fundamentally pose unwanted risk. Therefore, the insurer will generally absorb these customers - and the risk they pose.

Mounting the technological infrastructure to accurately identify fraud and other threats en masse still thwarts legacy carriers and insurtechs alike. And both insurance fraud and the underwriting of unwanted risks means an increase in the insurer's loss ratio and combined ratio, and, ultimately, increases insurance premiums for customers.

"A successful digital carrier must orchestrate efforts to garner customers, serve them appropriately and balance the various kinds of risks they impose, ideally in an integrated, cloud-based ecosystem," said Thorsten Hein, Insurance Lead in Risk, Fraud and Compliance Solutions at SAS.

"When insurers centralize and integrate the tasks of actuaries, underwriters and fraud analysts, it helps ensure the carrier can make a profit with risk-appropriate customers, while serving and protecting customers exactly as they need, and at a price that does optimum justice to all parties."

5. Life insurance: newly vulnerable, but still vital.

Life insurance is a perfect microcosm of much of what plagues the insurance industry today. Life insurers have long depended on commercial real estate for profitability. Since the COVID-19 pandemic, however, the value of these assets has nosedived. This subset of the industry needs to create new opportunities.

"When we talk about an insurable interest, not everyone needs to protect an asset like a car or a home - but everyone has a life," said Franklin Manchester, Principal Global Insurance Advisor at SAS. "Health data organizations forecast global life expectancy will increase to more than 78 years by 2050. With growing risks in a world in polycrisis, life insurers have a part to play in driving positive change.

"For example, one of the many forces that can begin and perpetuate generational poverty is a death in the family, and the resulting decrease in income or support. When the worst happens, dependents left behind often face the stark reality of going without. Life insurance can greatly ease the financial burden, but unfortunately, due to a lack of accessibility and historic marginalization, many who could greatly benefit from a policy are uninsured."

Today, by incorporating cleansed data, with pricing decisions made within principled frameworks, and with the global outreach of digital platforms, insurers can reach, educate and protect more people, potentially breaking multi-generational cycles of suffering.

Reimagining insurance in context

Addressing the environmental, economic and ethical challenges facing insurance will require human ingenuity. AI and other technologies can power a more equitable and climate-resilient pivot for the sector - and bestow competitive advantages to traditional carriers and insurtechs in the process.

To further explore how insurers can learn, adapt and compete within the current insurance marketplace, download Top 5 insurance problems - and AI isn't one of them.

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