Since personalisation has become an important facet of marketing, marketers can use machine learning to improve their brand strategies and marketing campaigns. 

Machine learning is very effective in creating customer segmentation models, particularly in extracting a group of customers within the data that have similar behaviors, interests and preferences. 

Furthermore, by finding patterns within customer data, predictions can be made on the lifetime value of customers at the beginning of their policy lifecycle. Customer lifetime value forecasts are not only important for customer segmentation and grouping, but also for insurance organisation profitability and growth.

Underwriting new risks through machine learning can be handled in two ways. 

Firstly, by automating low value tasks such as searching, aggregation and selection, whereby the underwriter is supported by machine learning to automate the data collection phase. 

Secondly, by automating the full underwriting process where the machine learning substitutes the underwriter in the risk evaluation phase. However, if a task is very complicated, it can be fully or partially assigned to an underwriter. 

Machine learning improves response time by processing increasing volumes of new applications efficiently. It identifies inconsistencies during point of sale underwriting, which reduces premium leakage. 

It screens new applications coming from new customers before entering insurance organisations’ portfolios. It does this by extracting information from different external sources to evaluate risk selections and avoid an exaggeration in the applications made by new customers.

While insurers are attempting to meet customer demand in claims filing via convenient online platforms and mobile applications, fraudulent claims are on the rise. Insurers’ ability to detect exaggeration and fraud in claims, alongside compliance with the regulatory obligation, is an absolute necessity. 

Machine learning technologies will not merely help in detecting exaggeration, fraud and misrepresentation, but will also speed up the claims process and curb extra costs in the claims lifecycle.

The true challenge in claims processing is when customer service comes into the picture, as timely responses and excellent communication drive customer satisfaction and retention. Machine learning aids insurers to provide better communication through automation. 

One example is during the subrogation phase where faster claims task assignments can be improved through data-driven decisions. It can quickly assess and extract predictions based on the information available and whether claims should be assigned to a third party or not. 

Another example is when complex claims are identified and allocated to the most experienced adjusters. This smarter assignment of work ensures that the claims lifecycle is minimised as far as possible. 

Furthermore, claims leakage is the loss of money due to claims process inefficiency. Tackling this issue, insurance providers usually opt to outsource auditing or use audit tools to reduce leakage within the claims process. 

However, auditing only analyses a sample of the data as it would be inefficient to process each claim application. Therefore, applying machine learning enables insights to be gained from the audit process, and implements these insights into the stages of claim investigation, evaluation and settlement.

Many IT vendors are increasingly moving to incorporate machine learning technology in their solution strategies. This trend is driven by the insurance industry’s need for accuracy and efficiency, as well as to fundamentally advance existing solutions. 

Software providers, who are offering advanced analytics tools, are now using machine learning to evolve their current advanced analytics solution. They are doing this by integrating machine learning functionality into their existing capabilities where it might be simply hidden or explicitly emphasised. 

Consequently, insurance providers who are using advanced analytics applications, particularly for big data, are perhaps already using some sort of embedded machine learning technology.

 
Many software vendors have developed products that offer machine learning functionality to some degree. While traditional giants usually consider machine learning as an add-on feature available in their analytics stack, startups with a particular focus on the insurance industry often have machine learning as central in their analytics offerings. 

Machine learning software is typically categorised in two types. 

The first type is open source, which is publically accessible software that allows data scientists or developers to modify and enhance the source code. Open source software has an open code that is available to others who like to view, enhance, or share it. Examples of machine learning open source software are Weka, R and Apache Mahout.

 
This software has enormous benefits such as lowering costs and flexibility. However, it comes with some disadvantages as well, namely a lack of personalised and official support or any warranty as no single company supports this software. 

Moreover, open source software can be continuously modified, which makes it difficult to ensure that the given software is compatible with other applications already in place. 

The second type is commercial software, which comes as a solution to open software and requires an expert to build, test, monitor and run predictive models. Software vendors are now repackaging open source by taking its code and developing tailored applications. 

The deployment of this commercial software is also offered via the cloud as software as a service (SaaS) or on-premise. The key selling point of commercial software is that the service comes with training and service support.

GlobalData considers machine learning  to have tremendous potential for the insurance industry, it offers insurers the opportunity to handle their business in a markedly efficient manner. This can be translated into streamlining every stage at the value chain by extracting valuable insights from big data and automating processes. 

The combination of modeling rules, text-mining and predictive analytics deployed through machine learning will enable a powerful future for the insurance industry. The current size of the machine learning market within the insurance industry is still unidentified and perhaps minor. 

Insurers are known as traditional and cautious; they are heavily regulated and unaccustomed to sudden change, deploying machine learning can prove to be a steep hill to climb because of the demands it places on their data architecture and systems. 

Insurers’ existing legacy systems will make issues arise with the implementation of machine learning. In countering this hurdle placed by legacy infrastructure, investment in time and resources are a necessity as well as buy-in from the executives in order to actively allocate budget towards machine learning and the cloud. 

Engaging with SaaS providers to manage data and technology in cloud will offer insurers the flexibility to work with startups as well as established vendors. In the meanwhile we see such strategic planning activities in the space, concept of proof from innovative insurers who exploring avenues for approaching machine learning. 

The inevitable clear trend is in statured markets where insurers are betting millions of dollars on machine learning technology through insurtech innovation to embrace the disruptive technology. 

AXA made strategic ventures with Neura, a company that uses machine learning to create a digital identity profile of customers. Through their platform, Neura enriches applications and devices with insights about users’ past and present actions and makes predictions about their future actions. 

Another huge investment by AXA is on BioBeats, which was founded in 2013 with the idea that prevention is the best approach to well-being. Leveraging existing wearable devices and smartphones, this cloud-based platform collects biometric information about wellbeing and building a distinctive profile on a personal dashboard regarding sleep, activity and stress. 

The software uses machine learning technology to create these insights. It is evident that Internet of Things (IoT) will be key for machine learning success where the ever-growing data flowing through IoT ecosystem will accelerate machine learning adoption pace. 

In the near future, some insurers will be ready to deploy machine learning, while other won't. Insurers will need to constantly look to partner with a mix of startups and established vendors who will help them to understand data and offer technology expertise.