AI in Insurance
Compressing the Claim. Rethinking
the Risk. Proving AI in Production.
Introduction

The insurance industry has entered a new era when operational efficiency, climate risk, and AI adoption are coming together. Despite the anticipated deceleration in premiums growth for property & casualty, life and annuity, and group insurance sectors, Asia-Pacific is still an outlier, with premiums of life insurance growing at an annual rate of around 5.3% until 2035 owing to India, China, and Southeast Asia.

No more “just digitising” – faster underwriting, intelligent claims, resilient operating models, and AI that works in production and is explainable and auditable – are the new requirements for insurers’ success.

The vectors reshaping insurance
01
Innovative products and ecosystem orchestration

More personalised insurance products based on advanced analytics, usage-based pricing, and partnerships are being introduced by insurers.

Embedded insurance is becoming the main distribution model, enabling coverage to be provided directly at the point of sale through digital channels and ecosystem partners. It requires configurable products, API-first architecture, and faster go-to-market.

02
Lifecycle digitalisation and cloudification

The cloud, AI, and modern data platforms transform all stages of the insurance value chain – from quotations and underwriting to claims settlement. Modern underwriting makes use of telematics, IoT, satellite imagery, medical data, and unstructured documents in order to cut down manual work and increase decision quality.

According to Deloitte, the most valuable AI use cases in the insurance sector are:

  • Automation of underwriting process
  • Processing of claims
  • Detection of fraudulent activities
  • Customer engagement

These are enabled by the modern data foundation.

03
Sustainability, climate and human sociological change

Climate events reshape the insurance economics.

According to the report by Swiss Re Institute:

  • The economic losses caused by natural catastrophes totalled USD 220 billion in 2025 globally
  • USD 107 billion of it is insured across 190 catastrophe events, with the record 49% of insured share
  • Wildfires, floods, and severe storms were responsible for 88% of insured losses in catastrophe events
  • Only the January Los Angeles wildfires caused insured losses of USD 40 billion

At the same time, aging population, increasing life expectancy and prevalence of chronic diseases are changing actuarial assumptions and product design.

Leading insurers are not just paying out but also preventing the losses using analytics and managing customer risks.

04
Workforce and technology transformation

AI changes how insurers work but not why people are important.

Cloud, AI and IoT require new operating models in which employees supervise AI systems, handle exceptions and make impactful decisions.

According to Deloitte, although almost 90% of insurance executives understand the need for reskilling their employees in order to work with AI collaboratively, only about 25% take action to this effect.

05
Governance, not AI, is now the differentiator

The challenge is not building AI models but running them responsibly.

With AI going to underwriting and claims production, governance, transparency and explainability became the strategic differentiators.

Regulators in Europe are increasing oversight over AI governance, operational resilience and claims decisions. According to the European AI Act, life and health insurance risk assessment belongs to the high-risk AI category with compliance requirements from 2 December 2027.

Now is the time to establish a governance framework.

What we deliver
Claims intake and automation
  • Voice, image and document-based FNOL
  • Claims intake automation
  • AI-assisted damage assessment
  • Straight-through processing with human review for complex claims
Underwriting co-pilots
  • Submission triage
  • Intelligent document processing
  • Geospatial, telematics and IoT-based risk enrichment
  • AI-assisted rating and referral recommendations with full audit trails
Policy administration and core modernisation
  • Policy administration transformation
  • Core platform assessment and migration
  • API-first architecture
  • Faster product configuration and deployment
Catastrophe and exposure analytics
  • Portfolio accumulation monitoring
  • Catastrophe model integration
  • Location intelligence
  • Risk mitigation analytics
Fraud detection across the lifecycle
  • Application and claims fraud detection
  • Network and behavioural analytics
  • Explainable anomaly detection suitable for regulatory review
Customer 360 and Financial Services Cloud for insurance
  • Unified policy, claims and service data
  • Salesforce Financial Services Cloud implementation
  • Broker enablement
  • Retention, cross-sell and lapse analytics
Embedded insurance enablement
  • Partner APIs
  • Quote-and-bind services
  • Ecosystem integrations
  • Digital settlement workflows
AI governance for insurers
  • Model inventory
  • Bias testing
  • Technical documentation
  • Event logging
  • Human oversight frameworks aligned with EIOPA and the EU AI Act
Why TechKrill
We optimise for metrics that insurers use already – loss ratio, expense ratio, claims cycle time and leakage – and not for some abstract automation goals.
The underwriting AI starts from trusted exposure data. Our data engineering and AI teams work together to build production-ready solutions.
Our every AI solution is designed with explainability, auditability and regulatory compliance from scratch in order to perform in production and withstand regulatory scrutiny.
Compressing claim cycles or reinventing underwriting?
Talk to our AI experts in insurance to assess where intelligent automation may deliver the tangible results for you.