Artificial Intelligence (AI) in Insurance is changing how the industry operates. From automating claims to improving underwriting accuracy, detecting fraud and providing faster customer service, the benefits are significant. Yet many insurers still find it difficult to move beyond pilot projects and scale AI in Insurance effectively.
This article explores the eight most common challenges and practical ways to overcome them.
1. Data Quality and Accessibility in AI in Insurance
AI in Insurance relies on access to large amounts of accurate and consistent data. Many insurers are still dealing with information stored in fragmented systems or in formats that do not work well together.
The challenge: Data cleansing, standardisation and centralisation take time. In some organisations, different departments use incompatible formats, which prevents AI models from learning effectively.
How to overcome it: Insurers need to invest in proper data governance, introduce common standards and use cloud-based repositories to create a single version of the truth. This gives AI the quality of information it needs to deliver reliable results.
2. Regulatory and Compliance Constraints Affecting AI in Insurance
The insurance sector is one of the most heavily regulated in the world. AI systems must comply with strict requirements on data protection, fairness and accountability.
The challenge: Many AI models are seen as black boxes, which makes it harder to prove compliance with GDPR, CCPA and other regional insurance rules.
How to overcome it: Use explainable AI techniques that allow decisions to be understood and traced. Keep clear audit trails, involve compliance experts in the design stage and make sure every AI system is transparent enough to meet regulatory expectations.
3. Legacy IT Systems Holding Back AI in Insurance
Most insurers still rely on systems that were built years or even decades ago. These platforms are not designed for real-time analytics or the data demands of AI in Insurance.
The challenge: Introducing AI into a legacy environment often requires significant IT modernisation. This can be costly, complex and disruptive to day-to-day operations.
How to overcome it: Plan modernisation in phases rather than attempting a full replacement at once. Use middleware and APIs to bridge old and new systems so that AI can be introduced gradually without major disruption.
4. Talent and Skills Gaps in AI in Insurance
AI in Insurance needs a combination of data science, machine learning and insurance expertise. Finding professionals with all three skills is not easy.
The challenge: There is a global shortage of people who understand both AI technology and the detailed workings of insurance.
How to overcome it: Build mixed teams where data scientists work alongside insurance professionals. Invest in training to upskill current staff and create partnerships with universities and technology providers to close the gap.
5. Bias and Fairness Risks in AI in Insurance
AI can unintentionally reproduce the biases found in historical data. This can affect pricing, claims decisions or how customers are segmented.
The challenge: If left unchecked, biased models can lead to unfair or discriminatory outcomes, damaging both reputation and compliance standing.
How to overcome it: Apply strict validation processes to every model. Test for bias before systems go live, put governance frameworks in place and adopt ethical AI practices to ensure fair treatment for every customer.
6. Customer Trust and Transparency with AI in Insurance
Customers are often cautious about AI-driven decisions, especially when they involve sensitive matters such as claim denials or premium changes.
The challenge: Lack of explanation creates mistrust and may discourage customers from engaging with AI-led services.
How to overcome it: Provide clear explanations of decisions, design customer-friendly interfaces and ensure communication is simple and transparent. When customers understand how AI works, they are more likely to accept its outcomes.
7. Cybersecurity and Data Protection in AI in Insurance
As insurers digitise more of their operations, the risk of cyber threats and data breaches increases. AI adds new complexity and creates more potential entry points for attackers.
The challenge: Sensitive customer data must be protected without slowing down AI-driven services.
How to overcome it: Build strong security measures into every AI system. Use encryption, multi-factor authentication and continuous monitoring to safeguard data. Regular security audits and penetration tests should be part of ongoing operations.
8. Integration of AI into Insurance Workflows
AI only delivers real value when it is embedded in everyday processes across underwriting, claims, customer service and risk management.
The challenge: Too often, AI pilots are kept separate from the core business. This leads to limited value and resistance to change.
How to overcome it: Align AI with business objectives from the start. Redesign workflows so AI supports staff rather than replacing them, and provide training to help teams adapt. Change management is as important as the technology itself.
Final Thoughts
AI in Insurance has the potential to reshape the industry, deliver faster and fairer outcomes and create value for both insurers and their customers. But the journey is not straightforward. Data issues, regulation, legacy systems, talent shortages, fairness concerns and customer trust all present real barriers.
These challenges can be overcome with the right mix of strategy, technology and culture. askelie helps insurers address these barriers directly by providing practical solutions, transparent AI systems and implementation support that is built on real industry knowledge. With the right approach, AI in Insurance moves from a difficult ambition to a genuine competitive advantage.
Comments are closed