Settling a claim. Underwriting a policy. Serving a customer.
Every step in insurance is a decision and every decision shapes trust.
From quote to claim, insurers operate in a continuous loop of high-stakes choices. These are the moments that determine whether customers feel protected or exposed.
That pressure is only increasing. Geopolitical instability, regulatory scrutiny, climate volatility and shifting market dynamics are colliding with rising expectations for personalized, real-time experiences.
Insurance has always been complex. What’s changed is the speed and the consequences of getting it wrong.
AI’s promise – and its reality check
AI has captured the industry’s attention, promising faster decisions and better outcomes. But for many insurers, that promise hasn’t translated into value.
According to the IDC Data and AI “Trust Imperative” report, only 7% of insurers self-describe as “transformative.” The report further finds that only 9% of insurers have optimized their AI use, balancing strength and trust.
Recent signals reinforce this AI value delusion. Major financial institutions like Goldman Sachs, Morgan Stanley and JPMorgan Chase have publicly noted AI added “basically zero” to overall economic growth in 2025.
At the same time, failures are becoming harder to ignore. A lawsuit involving Cigna’s PxDx system (procedure-to-diagnostic) allegedly resulted in upwards of 60,000 preapproved claims being denied in a single month (an average of one every 1.2 seconds). Upon appeal, nearly 80% of those decisions were reversed (the class action lawsuit was advanced just last year).
Unfortunately, “good enough” AI won’t cut it in the insurance industry.
Where real value is won and lost
The stakes are massive. Poor decisions in claims alone account for an estimated $170 billion in losses, with another $160 billion tied to underwriting inefficiencies.
But the upside is just as real. Insurers that lead with AI outperform peers significantly – in some cases, achieving more than 6x total shareholder return.
Striking the balance between AI’s use while avoiding its downside risks will not be easy. Connecting decisions across the insurance value chain will involve alignment with strategic objectives, commitment to data management, and cultural evolution.
From theory to execution
And while it would be easy to discuss hypotheticals and throw around buzzwords like “agentic” or “generative AI,” let’s explore three real-world use cases: real insurers, real results.
DB Insurance protects capital by fighting fraud
DB insurance protects over 10 million customers in Korea. Like many insurers, bad actors found weaknesses in their processes, exploiting vulnerabilities to defraud the insurer.
With so many customers and so many claims, human investigators were overwhelmed, often missing more complex patterns and being outmaneuvered by ever more sophisticated fraud networks.
So, DB Insurance committed to fighting with fire. They partnered with SAS to develop Korea's first AI-powered fraud detection network, the DB T-System. By using machine learning to unify operational and informational data on a single platform, decades of policy, claim and customer information were brought together.
The AI learns with every new case processed, refining its ability to distinguish between legitimate and fraudulent claims. With the implementation, the analysis time dropped from hours to just two minutes. Detection accuracy improved by 99% and cases processed increased 30-fold.
Investigators can now act proactively to prevent fraud. DB Insurance can now protect people and businesses, deliver fairer premiums and resolve claims faster.
Read the full storyNeova Sigorta secures revenue with AI premium modeling
The Turkish auto property and casualty company had experienced quote-to-bind rates below half the industry benchmark, no sales growth and poor profitability. With a new mandate from their CEO, they sought a single platform to grow sales, improve segmentation and fight fraud.
The company’s pricing method was working against their business goals – antiquated GLM algorithms. They then began investing in machine learning-based pricing to enable smarter underwriting, agent and customer segmentation. This initiative had the potential to offer 95% of their customers better insurance rates. Additionally, forecasts conservatively estimated an increase in sales of up to 15% while decreasing the insurer’s combined ratio by a staggering 10%.
As a result of adopting a machine learning-based approach, Neova Sigorta shared that not only has their hit rate increased – translating into increased sales – but that rates are now more competitive, with about a 9% reduction in overall premiums. Their AI-powered approach to underwriting and risk selection delivered returns so compelling that they are now rolling out AI solutions across the entire company.
Read the full storyERGO crowns customers by leading with customer intelligence
ERGO is one of the largest insurance groups in the world and a SAS customer for over 35 years. They continue to push the envelope with Artificial Intelligence (most recently deploying an enterprise-wide Generative AI assistant to 28,000 employees). And they continue to innovate. Their pursuit of transforming customer data from run-of-the-mill CRM functions into a revolutionary and comprehensive digital strategy required more than just AI. Creating a true omnichannel experience meant blending technological innovation with human authenticity.
ERGO’s strategy combined both online and offline channels, delivering fast, convenient, and relevant experiences for its customers. Drawing on their pioneering experience in machine learning and deep learning, their approach significantly improved next-best-offer, next-best-action, customer service and processing time across all customer engagements, regardless of channel.
This forward-looking, customer-obsessed philosophy paid off. ERGO has achieved its strongest new customer business in a decade for two consecutive years.
Read the full storyDecisions build – or break – trust
Insurance runs on outcomes.
Every approved or denied claim, every priced risk and every customer interaction shapes whether insurers protect capital, secure revenue and retain customers.
The insurers highlighted here aren’t experimenting with AI solely for efficiency. They are improving the quality of decisions – reducing fraud, pricing risk more accurately and delivering relevant customer experiences.
When AI lacks consistency or transparency, it puts critical insurance decisions at risk.
In a regulated industry, a decision you can’t explain is a decision you can’t defend.
Because in insurance, the question is whether the decisions it drives can be trusted.
If you’d like to know more, consider joining us at SAS Innovate, from April 27-30, or one of the many stops of the global SAS Innovate on Tour.
And, if you’d like to keep up with the latest technological innovations like quantum AI, agentic AI, or synthetic data, you can subscribe to the SAS YouTube Channel.
