Six insurance tech trends for working smarter in 2025 has been saved
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Spatial computing
Enhanced risk assessment and claims processing
Using augmented and virtual reality can help to enhance how companies visualize insurance policies.
Spatial computing is transforming the insurance industry in key strategic areas across the value chain. It is being leveraged to enhance risk assessments during underwriting and claims processing through advanced technologies such as aerial imagery, geographic information systems, and real-time data analytics. Insurers are utilizing high-resolution aerial images and geospatial data to assess property risks more accurately, enabling precise underwriting decisions and more efficient claims management. These tools can reduce operational costs, enable more accurate assessments, and drive better financial performance for insurers.
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Information
Small language models
Improved customer service
SLMs can provide highly relevant responses to insurance-specific queries about policy details and claim statuses.
Small language models are being used to improve accuracy and reliably of AI solutions for specific tasks across the insurance value chain. This automation not only speeds up response times but also allows human agents to focus on more complex issues, enhancing overall customer satisfaction. Similarly, in claims processing, SLMs can quickly extract and interpret data from documents, streamlining the claims life cycle and reducing manual errors.
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Computation
Hardware for AI
Better performance and efficiency
Machine and deep learning rely heavily on data. To properly build a fraud detection model, you’ll need high memory capacity to handle the large data sets.
For an insurer to build a fraud detection model, it needs high memory capacity to train the model of high-complexity and fast storage to store and handle large datasets. All these factors are increasing the power and cooling requirements of a data center, which is leading to an increase in nuclear energy plants to run the graphics processing units (GPUs) in data centers.¹ Insurance companies should consider these requirements as they evolve in their AI journeys.
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Business of tech
Operating model
Human-in-the-loop model
Improve your workforce by pairing them with GenAI. Allowing them to make claims processing, product personalization and cyber risk simpler.
Some insurers are investing in upskilling their workforces.² They are developing artificial intelligence and machine learning-based training programs to prepare employees for human-in-the-loop operations. Through these modernization efforts across software engineering, data, cloud and cybersecurity, insurers are enabling AI-driven claims processing, product personalization, and cyber risk modeling to create long-term enterprise value.
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Cyber and trust
Cybersecurity
Safeguard your business
Emerging regulations are requiring insurers to assess and address algorithmic biases and provide transparency on how AI models make decisions.
Consumers across the insurance industry conduct cyber transactions with organizations hundreds or thousands of times a day through web-based interactions, digital insurance policy purchases and online insurance claims submissions. These interactions generate significant volumes of customer data that can be used by AI models to enhance customer experience and optimize product offerings.
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Core modernization
Core systems
New AI-powered products
Technology such as AI code mining can help provide visibility into underwriting rules leading to faster service and lower costs.
Carriers are adopting new products based entirely on AI-powered ecosystems across the insurance value chain and integrating to legacy environments only where necessary. New AI-assisted core systems' modernization techniques could reduce the information technology (IT) labor needed for transformation. AI-based code mining technology extracts business logic from legacy systems and provides visibility into underwriting rules. This can enable faster creation of business requirements and the ability to rewrite proprietary applications to run on modern platforms, which decreases the applications' total costs of ownership and operation.
