The insurance industry is very heavily data driven – in terms both of using and generating data. As such it is very amenable to application and leveraging of machine learning in general and Deep Learning in particular.

Potential use cases pertain to images, numbers and text based analysis and predictions.

Product Design, Pricing and Underwriting, Distribution including sales and marketing, Claims Management are all processes that can be impacted using Deep Learning.

Underwriting processes needs to evaluate and process various sources of information to make a predictive judgement. Rule based systems can be limited in how they process interdependencies and are less agile. However, Deep Learning can be used to build intelligent multi-functional systems that can take input from multiple data sources and formats to create considered outcomes. Such systems can also be designed for more complex tasks by creating recurrent learning loops into the process that can give continuous incremental performance advantages.

Pricing preferences fluctuates on various dimensions including geography, age, income. Customers have come to expect and even demand tailored products. In such high complex environment, Deep Learning can be applied to design personalized pricing for various products at each phase – Auto Insurance, Pension Premiums, Travel or even create a more complex combination as required

Deep Learning can be applied on numerical and image datasets such as past claims, current risks to generate precise and personalised pricing.

Health insurance is one such domain where insurers are facing major challenges. Claims processing today involves increasingly unmanageable operational costs, turnaround times and fraud risks. Ability to understand users and settle claims quickly is a key to customer satisfaction. An effective approach, now made feasible by deep learning technology, is to develop an intelligent application that learns inferencing or to predict outcomes – faster, cheaper and more accurately compared to a human process.

In Insurance, the ability to manage risk allows the institutions to scale faster. The fundamental behaviour of ‘risk’ varies not only with products but also with time to time. With increase in new data sources, primary insurers and re-insurers need to adapt the systems to consider in-directive information source for the focused goals such that these system never miss the influential parameter in the risk equation.

Few segments where Deep Learning can be used to built risk management systems:

Claims Assessment from Life Sciences to Property & Casualty

Assessment of damage based on images – again, ranging from images of automobiles in an accident to grasping the magnitude of damage due to a floods – Deep Learning can make this process quick, accurate while reducing human effort. Genuine claims can be processed quickly to gain customer trust while potential fraud can be detected reducing financial risk.

Asset Evaluation

In Property & Casualty (P&C), it is important to understand the asset to define policy and premiums. With asset type, the relevant risk parameters will vary. Using the Geographical, Spatial information – images, previous assets, textual information, public sources etc, can be considered in building Deep Learning system to predictive risk factor of such assets in P&C.

Maintenance and damage prediction

With IoT going mainstream, there are new additional new source of inform

New ways of customer engagement through conversational systems, product recommendations based on customer behaviors and demographics, smooth self service through intelligent data capture methods – these are some of the ways in which Deep Learning is helping Insurance better reach and engage with customers.

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