# Transforming Insurance Through Artificial Intelligence and Machine Learning
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2021-05-17
Category: TECHNOLOGY
Category URL: https://financedigest.com/category/technology
Meta Title: Revolutionising Insurance with AI and ML
Meta Description: Discover how Artificial Intelligence and Machine Learning are transforming the insurance industry, improving customer experiences and operational efficiency.
URL: https://financedigest.com/transforming-insurance-through-artificial-intelligence-and-machine-learninghtml

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_By **Eleanor Brodie,** Sr Manager, Data Science at LexisNexis Risk Solutions_

Artificial Intelligence (AI) and Machine Learning (ML) are being used in many walks of life today, but insurance has to be one of the most exciting. Almost everyone needs to buy insurance and some of us will have the misfortune to find we need to make a claim. At a base level, AI and ML are helping to make those experiences better.

Consider the volume of [customer data held by insurance](https://www.financedigest.com/should-insurers-buy-data-direct-from-their-customers.html "Should insurers buy data direct from their customers?") providers in claims, marketing, underwriting – often held in different silos, sometimes in different sub-brands. AI and ML techniques can help to make sense of that data. The linking and [matching technology](https://www.financedigest.com/all-shapes-and-sizes-how-to-match-your-technology-to-your-finances.html "All shapes and sizes: How to match your technology to your finances") now available to the insurance market means a single, consolidated view of the customer can be created based on all previous touchpoints. This gives [insurance providers a more holistic view of that individual to best support their needs](https://www.financedigest.com/reasons-you-need-a-car-insurance-when-driving-in-thailand.html "Reasons You Need a Car Insurance When Driving in Thailand") and provides a strong foundation to build that picture further by bringing in additional data sources.

**[Insurance providers need to operationalise growing data](https://www.financedigest.com/big-data-lowers-insurance-premiums-and-optimises-business-performance.html "Big Data Lowers Insurance Premiums and Optimises Business Performance") volumes**

These additional data sources include market-wide contributory data – policy history, past claims, quote history – some of this data has been gathered for over 6 years. Add in public address and ID validation data and other external data sources, for example environmental data, property characteristics and data [related to assets such as the safety features on a motor](https://www.financedigest.com/2020-the-paradoxical-year-that-has-reshaped-the-future-of-motor-insurance-and-related-sectors.html "2020: The paradoxical year that has reshaped the future of motor insurance and related sectors") vehicle or the data from a telematics device. [Insurance providers need to operationalise all of this data](https://www.financedigest.com/aon-data-empowers-unica-to-offer-flood-insurance-for-5-2bn-canadian-peril.html "Aon data empowers Unica to offer flood insurance for .2bn Canadian peril"), bringing it in at the right time to support quotes, price a risk, expedite a claim, flag possible fraud or understand a cross-sell opportunity.

**ML helps take the guesswork out of [home insurance](https://www.financedigest.com/what-is-the-difference-between-a-home-warranty-and-home-insurance.html "What is the Difference between a Home Warranty and Home Insurance?") applications**

AI and ML techniques are making that possible, enabling data based decisions to be made, at speed.  A great example is the [way the application process has been simplified in household insurance](https://www.financedigest.com/predictive-analytics-a-new-way-of-insuring-success.html "Predictive analytics: a new way of insuring success") through ML techniques, improving pricing accuracy while cutting the time it takes for a customer to gain a quote. Prefill and data validation [solutions speed the whole process but are only possible through a huge amount of modelling](https://www.financedigest.com/jaywing-releases-horizon-a-new-ifrs-9-modelling-solution-that-encapsulates-its-extensive-credit-risk-expertise.html "Jaywing releases Horizon, a new IFRS 9 modelling solution that encapsulates its extensive credit risk expertise"), linking and AI-ML techniques to pull all the data together to return accurate and up-to-date information on the person and property. For the customer it means no more guessing at rebuild costs or [property age](https://www.financedigest.com/intellectual-property-in-the-age-of-industry-4-0.html "Intellectual property in the age of industry 4.0").

![Eleanor Brodie](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/eleanor-brodie-450x593-1736838332608-compressed.jpg)

Eleanor Brodie

**AI can deliver** [location intelligence for businesses](https://www.financedigest.com/dont-judge-a-business-by-its-location-entrepreneurial-creativity-could-help-fix-the-fraud-industry.html "Don’t judge a business by its location: entrepreneurial creativity could help fix the fraud industry")

For businesses, AI can also provide valuable insights regarding a potential location for a new branch or business relocation – footfall, crime rate, exposure to perils or other local circumstances that increase [risk in commercial property insurance](https://www.financedigest.com/using-threat-intelligence-to-minimise-cyber-insurance-risks.html "Using Threat Intelligence to Minimise Cyber Insurance Risks"). Armed with this valuable knowledge, the [customer may take preventative](https://www.financedigest.com/how-strong-customer-authentication-can-prevent-cart-abandonment.html "How Strong Customer Authentication can Prevent Cart Abandonment") measures which has the benefit of reducing the risk and potential claims costs.

**Making IoT data meaningful**

With the increasing volume of [data coming from connected](https://www.financedigest.com/real-time-connect-nyc-where-adtech-leaders-come-together-to-redefine-data-strategies.html "Real-Time Connect (NYC): Where AdTech leaders come together to redefine data strategies") things, data normalisation through ML techniques is creating standardisation and consistency for usage based insurance based on this data.

Whatever the source of data – aftermarket telematics devices, smartphone apps, connected vehicles, even in the future from smart home data – data normalisation means consumers can enjoy an [improved shopping experience using the consented data from their device or vehicle and insurers](https://www.financedigest.com/improving-online-visibility-for-insurers.html "IMPROVING ONLINE VISIBILITY FOR INSURERS") have consistent quality standards and consistent pricing for all consumers. Individuals then benefit from being judged based on their individual behaviours as is already the case in telematics insurance, rather than [paying premiums](https://www.financedigest.com/finnish-businessman-zilliacus-ready-to-pay-premium-for-manchester-united.html "Finnish businessman Zilliacus ready to pay premium for Manchester United") based on average habits.

**Helping insurance providers understand ADAS fitments**

A prime example of data normalisation is behind a new solution [\[i\]](#_edn1) to allow insurance providers to price based on the ADAS features on the car, at a Vehicle Identification Number (VIN) level.  [Machine learning](https://www.financedigest.com/machine-learning-in-artificial-intelligence.html "Machine learning in artificial intelligence") has been used to scan millions of lines of car manufacturer vehicle data to logically sequence and classify vehicle safety features and component’s intended operation or purpose to create an ADAS classification system. This task would have been extremely difficult, time consuming and error prone without the use of AI/ML.

**Taking the pain from claims**

[Motor insurance](https://www.financedigest.com/uk-motor-and-home-insurers-headed-for-heavy-losses-ey-warns.html "UK motor and home insurers headed for heavy losses, EY warns") claims are also benefiting from AI/ML techniques as virtual claims handling speeds up claims resolution, cuts costs and provides a smoother customer experience.  Image recognition technology captures damage or invoices, runs a system audit, and the claims is paid automatically if it meets the right criteria. Bringing in historical policy and quote history to claims in the [future may add an additional level of security prior to an insurer](https://www.financedigest.com/insuring-themselves-for-the-future.html "Insuring themselves for the future") releasing any claim payments.

Many [customers with telematics policies benefits](https://www.financedigest.com/online-marketplaces-embracing-embedded-finance-to-benefit-both-customers-and-vendors.html "Online marketplaces: embracing embedded finance to benefit both customers and vendors     ") from an improved claims experience thanks to AI and ML. From the point of impact through to claim resolution, telematics [data can allow insurance](https://www.financedigest.com/simplifying-home-insurance-through-property-characteristics-data.html "Simplifying home insurance through property characteristics data") providers to get on the front foot at first notification of loss (FNOL), to support the customer post-accident with emergency services, roadside recovery, vehicle rentals and repairs whilst providing invaluable insights regarding the circumstances of the collision.

Insurance providers can look at a range of data such as air bag deployment, impact sensor activation and g-force metrics to understand claim severity and bodily injury potential. They can also bring in [vehicle build](https://www.financedigest.com/bmw-invests-1-7-billion-to-build-electric-vehicles-in-u-s.html "BMW invests .7 billion to build electric vehicles in U.S") data to understand the repair cost and potential impact to expensive ADAS features.

**Finding new [data points to help predict risk](https://www.financedigest.com/your-real-risk-appetite-could-be-hidden-in-your-unstructured-data.html "Your real risk appetite could be hidden in your unstructured data"), for use in pricing**

In pricing, machine learning algorithms can expedite the identification of the most predictive attributes behind claims losses for use at point of quote. The most [recent data points are cancellations data and gaps in cover in motor based on industry](https://www.financedigest.com/high-quality-low-voltage-motors-market-recent-industry-developments-and-growth-strategies-adopted-by-players.html "High Quality Low Voltage Motors Market: Recent Industry Developments and Growth Strategies Adopted by Players") contributed policy history data.  [Insurance providers can also distinguish between policy changes directly related to the first national lockdown versus changes that occurred outside of that time to support the fairest treatment of customers when they come](https://www.financedigest.com/being-aware-of-the-insurance-blind-spot-when-it-comes-to-driverless-cars.html "Being aware of the insurance blind spot when it comes to driverless cars") to buy or renew their next motor annual policy.

Building insight from [insurance specific behaviours and bringing in more data about the asset such as the car](https://www.financedigest.com/5-tips-for-selecting-the-best-car-insurance-coverage.html "5 Tips for Selecting the Best Car Insurance Coverage") or home, where relevant can help insurance providers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite. In turn, customers get more personalised quotes based on their unique [risk characteristics across any line of business](https://www.financedigest.com/why-smart-businesses-minimise-risk-with-smart-detectors.html "Why smart businesses minimise risk with smart detectors").

The [insurance industry](https://www.financedigest.com/how-can-the-insurance-industry-avoid-having-a-kodak-moment.html "How can the insurance industry avoid having a ‘Kodak Moment?’") is being transformed through its use of internal and external data to better assess risk, price more accurately, reduce claims losses and support customers at every touchpoint.   At the heart of this transformation is analytics, [Artificial Intelligence](https://www.financedigest.com/artificial-intelligence-in-banking-robo-advisors-and-beyond.html "Artificial Intelligence in Banking – Robo Advisors and beyond") (AI) and Machine Learning (ML).

From making the [insurance application quicker and simpler to expediting the claims](https://www.financedigest.com/how-to-streamline-claims-conversations-in-the-insurance-sector-and-transform-your-cx.html "How to Streamline Claims Conversations in the Insurance Sector and Transform Your CX") process, AI and ML techniques are creating new insights, allowing human skills – the ‘personal touch’ – to come in at the points it matters most to the customer.

[\[i\]](#_ednref1) LexisNexis Vehicle Build – https://risk.lexisnexis.co.uk/about-us/press-room/press-release/20200618-vehicle-build-uk


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