Innovation Insurance: Revolutionizing Claims Processing -

The theme this month at IE is how innovation can help insurance brands make gains. Could be saving money, faster admin, more accurate claims or pricing. Let’s get into it.

Here are some comments by Arsalan Minhas, AVP Sales Engineering EMEA & APAC at Hyland;

Can AI join the dots on streamlined FNOL and triage claims below say 2K, freeing up staff for higher value claims?

“AI is revolutionising claims processing by transforming First Notice of Loss (FNOL) processes and automating the triage of lower-value claims. Traditionally, claims under £2,000 – such as minor car accidents or small property damage – consume significant human resources, despite their routine nature. Automating claims using AI- can streamline this process, freeing up adjusters to focus on more complex, high-value cases.

“Through agentic transformation – where AI agents take on autonomous decision-making roles previously handled by humans – v AI agents can instantly assess claim legitimacy, validate documentation, and recommend next steps in real time. By leveraging unstructured data, AI extracts critical insights from photos, videos, and documents, ensuring a rapid, data-driven decision-making process. This approach eliminates manual bottlenecks and significantly reduces processing times, improving customer satisfaction.

“Hybrid cloud and data federation strategy also allow insurers to integrate disparate data sources – claims histories, policy details, and external databases – without requiring complete system overhauls. AI’s ability to connect fragmented information ensures accurate claim assessments while maintaining compliance.

“Beyond automation, the role of AI agents as co-workers is evolving. Instead of replacing adjusters, AI acts as a frontline triage system, handling straightforward claims while escalating complex cases to human experts. This collaboration enhances efficiency, minimises fraud, and empowers claims teams to prioritise strategic, high-value cases.

“The future of FNOL is not just automation – it’s intelligent, AI-driven transformation. Insurers that embrace this shift will not only reduce costs but also enhance customer trust and operational agility. Those who act now will set themselves apart from the competition, gaining an edge in an industry. The question isn’t whether AI can streamline claims under a certain amount of money – it’s whether insurers can afford not to adopt it.”

MORE DATA, BETTER RISK PRICING

Meanwhile Marat Nevretdinov, Managing Director at HDI Embedded, offers these thoughts on AI assistance on risk pricing, gathering data and a note on compliance too, as things get faster;

“Thanks to AI, the Internet of Things, and advanced data analytics, insurers now have access to real-time data that enables faster, more accurate risk evaluation.

For instance, when purchasing electronic devices, modern systems can instantly calculate the risk of mechanical damage or theft, allowing insurers to offer tailored coverage at the point of sale. Similarly, in the travel insurance space, real-time insights from mobile applications and weather-monitoring systems enable insurers to assess safety risks dynamically, adjusting coverage based on current conditions.

The advantages are significant: improved risk assessment accuracy, quicker underwriting decisions, and the ability to provide highly personalised policies. However, the implementation of such technologies is not without challenges.

Ensuring robust data protection, maintaining regulatory compliance, and managing the sheer volume of data require careful oversight.

Nevertheless, the future of risk assessment lies in the seamless integration of these technologies. By leveraging real-time insights, insurers can boost operational efficiency, optimise their product offering, and deliver greater value to their customers while building a competitive advantage.”

THIS IS A TRANSFORMATION, IN EVERY PART OF THE INSURANCE CHAIN

Steve Molly, Commercial Sales Director at AX sees data transforming every aspect of the insurance cycle;

Revolutionising data consolidation: how AX is leading the digital transformation

For a long time, the insurance industry has struggled with inefficiencies in data consolidation, compounded by the fact accident management firms and credit hire companies rely on separate systems. This data fragmentation is one of the biggest challenges in the sector.

Some suppliers in the accident aftercare space claim to offer data consolidation, but their approach typically relies on manual processes, PowerPoint documents and spreadsheets – this leads to inefficiencies, delays and missed opportunities. It also means fleet, supply chain and claims managers are presented with the same pre-packaged reports.

This lack of flexibility means insurers and other businesses struggle to extract the specific insights they need to improve efficiency, reduce costs, and make better decisions when it comes to risk mitigation. Worse still, many providers that position themselves as vertically integrated are unable to support non-fault claims, leaving a critical gap in the insurance supply chain.

With the industry crying out for a digitally-led, agile solution, new thinking is needed to reshape how insurers, accident management firms and credit hire companies utilise and share their data with clients.

At the forefront of this transformation is AX, which brings a level of agility, integration, and user experience that traditional competitors simply cannot match. This vertical integration supports every aspect of the process – including the all-important fault and non-fault claims – entirely in-house. This means greater efficiency, faster resolutions, and a seamless customer experience.

Through MAccess, a tailored data consolidation platform that adapts to each client’s needs – not a one-size-fits-all model – AX is harnessing insights across accident management, repair progression and credit hire services that ensure clients see exactly what matters to them.

This data-driven agility is powered by UX-optimised dashboards that don’t just display numbers but deliver real-time, actionable insights and enables smarter, faster decision-making through an intuitive interface.

As the insurance landscape evolves, digital agility is no longer optional –it’s essential. AX isn’t just keeping up with change; it’s driving it.

COMPLIANCE NEEDS TO BE EMBEDDED

Some thoughts from Karli Kalpala, Head of Strategy & AI Agent Business at Digital Workforce,

“The insurance sector faces the challenge of unlocking the value of AI while meeting rising regulatory expectations around customer outcomes. Success depends on recognising that innovation and compliance are complementary, and not competing priorities.

Compliance must be embedded from the outset. AI use cases should be aligned with defined risk categories and supported by robust data governance to address bias, privacy, and data quality. In areas such as underwriting and claims, human oversight remains critical to ensure decisions are fair and defensible.

Customers should also understand when AI is being used and how decisions affecting them are reached. Where models influence significant outcomes, their logic must be explained in straightforward terms. Regular bias testing and fairness reviews across customer groups help demonstrate transparency and that systems operate as intended.

Regulatory expectations for AI in insurance are now clearly defined. The EU AI Act and the UK Consumer Duty set standards around risk assessment and the protection of fundamental rights, particularly where AI is used in pricing and underwriting. Bank of England research indicates that 75 per cent of firms are deploying AI, with more than half of use cases involving some degree of automated decision making, though only a small proportion are fully autonomous. This activity remains mostly in lower-risk areas such as customer support and compliance, reflecting the sector’s cautious approach.

Discipline is important, but being overly cautious can slow progress and limit the benefits AI can deliver. These frameworks are designed to enable responsible innovation, not to constrain it. Used effectively, AI can strengthen oversight through real-time monitoring, identification of vulnerable customers and scenario testing of journeys before deployment. Effective governance brings together legal, compliance, technology and business leadership with accountability for outcomes, ensuring innovation is guided by shared responsibility.

AI enables more personalised products, faster claims resolution, more accurate underwriting and services available whenever customers need them. The differentiator will be the ability to demonstrate that AI-enabled journeys consistently deliver fair and positive customer outcomes in practice.”

ASTADIA SETS OUT THE CRUCIAL SIX

Chad Jones, Chief Revenue Officer (CRO) of Astadia (an Amdocs Company) serving the needs of financial and insurance institutions, shared the following commentary:

Customers have come to expect tailored service interactions, and therefore insurers are expected to increasingly leverage AI-driven personalization to anticipate needs and proactively offer coverage options. According to a recent Amdocs’ survey, 54% of customers will stop working with an enterprise after just four poor experiences, and 85% of loyal customers would be open to switching brands. To ensure customer retention, we anticipate insurers to offer more predictive, context-aware experiences powered by AI and real-time behavioral analytics.

In the insurance ecosystem in particular, high-value transactions require trust and empathy. Therefore, we also expect more insurers to deploy emotionally intelligent AI to guide insurance transactions, ensuring customers feel understood and supported.

However, AI personalization and emotional intelligence cannot exist in isolation. The more deeply AI engages with customer data, the greater the regulatory responsibility. In the highly regulated insurance sector, customer-driven AI innovation cannot succeed without governance. Insurance entities must deploy multi-agent systems for autonomous data quality checks, lineage tracking and regulatory audits. Manual governance is costly and error-prone, but leveraging Agentic AI for continuous governances ensures ongoing compliance checks and balances.

These are the six critical AI governance policies that insurers should have in place:

AI Ethics: To develop guidelines for fairness, transparency and non-discrimination.
AI Info Security: To protect company data and AI frameworks against security threats.
AI Bias and Fairness: To develop strategies to identify and mitigate bias.
AI Data Privacy & Protection: To ensure data is used responsibly and complies with consumer privacy laws.
AI Risk Management: To maintain review and development of future AI risks.
AI Compliance: To ensure AI systems are flexible to comply with impending regulations.