The insurance sector is in the midst of a transformation, from being dominated by legacy software and manual processes, to embracing a whole host of emerging technologies. And one technology that is now having an impact on numerous aspects of insurance and insurtech is machine learning.
A subset of artificial intelligence (AI), machine learning algorithms are designed to analyse, learn from, and adapt in response to data, without any further human intervention or programming. This enables computer systems to analyse large data sets to identify patterns, and trends, as well as make accurate predictions about what will happen in the future.
Machine learning and insurance are an ideal combination, particularly when combined with robotic process automation (RPA), as the technology can be put to work handling and processing big data in areas such as underwriting, risk assessment, and claims management, to improve accuracy, efficiency, and customer service. As a result, the sector has seen numerous use-cases emerge, both for insurtech innovators, and established insurance companies.
Here are the top five use-cases of machine learning for insurance companies:
1. Fraud Detection and Prevention
Fraud remains one of the biggest costs insurers face, and it’s impossible for humans to manually check every claim. Machine learning can rapidly analyse claims data, customer details and third-party information to flag suspicious behaviour, for example, if someone holds near-identical policies with multiple insurers. Adoption reflects that: fraud detection now has one of the highest AI adoption rates in insurance, at 84%, among the highest of any use case in the sector.
2. Claims handling
Beyond fraud checks, machine learning speeds up claims handling more broadly, extracting information from forms, analysing images, assessing market value and applying rules-based decisions automatically. Insly’s Claims Portal puts this into practice directly: every claim gets an AI recommendation with a confidence score and referenced policy terms, resolving straightforward decisions in under 60 seconds.
3. Risk assessment and underwriting
Underwriting involves analysing customer demographics, historical claims data and third-party information, traditionally a manual, skilled job. Machine learning can now handle much of that heavy lifting, considering a wider range of structured and unstructured data, from driving records to wearable health device data, than a human underwriter reasonably could, working toward fairer, more accurate pricing.
4. Marketing and customer retention
Customers increasingly expect insurers to understand their needs and tailor pricing accordingly. Machine learning can analyse purchasing and renewal behaviour, changing demographics, and market trends to spot patterns a human team would likely miss, directly boosting sales and retention.
5. Price optimisation
Pricing strategy, once the domain of actuaries working manually, can now be done faster and more accurately with machine learning, analysing loss and expense data, market activity and forward-looking trends to land on the most appropriate rates.
Machine learning has real power to transform insurance, and the market has caught up to that fact fast: 86% of insurance organisations plan to increase their AI spending in 2026, machine learning adoption now sits at 74% among insurers, and nine out of ten are evaluating or actively implementing AI in some form. The bar has moved from “should we try this” to “how fast can we scale it.”
That’s where Insly comes in. Nora, Insly’s AI layer, removes manual work across the underwriting and operations lifecycle, the same data extraction, pattern recognition and decision-support tasks described above, without adding headcount. It sits on a platform that already covers product design, distribution, accounting and claims, so the foundations for putting machine learning to work are already there.
We work with 70+ MGAs and insurers, and on average our customers handle double the gross written premium they managed on their previous systems.
