Archive for Insurance

Big Data in the Insurance Industry – Wesley Montgomery

Data is the lifeblood of the insurance industry, but when you are dealing with a quintillion bytes of data from a growing array of different sources, it becomes a different story. This is Big Data, a major challenge facing insurance companies now and in the years to come.

Let’s start by defining Big Data. In short, big data is a collective term to describe all data – structured or unstructured, on your server(s) or available to you in any form. The increased amount of data available to companies has led not only to a greater cost of storing data, but also to greater costs involved in mining this data in order to extract the required information. Adding to this, the volume and complexity of available data is ever increasing, as unstructured data is growing much faster than structured data, and can be accessed faster than ever before. Now, the challenge facing insurers is to find ways to exploit this and ensuring that adequate systems are in place to cope with the volume, variety and velocity of data, such as using it to obtain information on product- and customer needs.

However, the question remains: How can a company benefit from big data?  Companies can take data from almost any source and analyse it to find answers that enable:

  • Cost reductions
  • Time reductions
  • Optimized product development
  • Smart decision making
  • Reduced fraudulent claims

The sources of big data generally fall into three categories, namely streaming data, social media data and publicly available data. After identifying all potential sources of data, there are many other problems that arise, such as: How do we store and manage it? Or how much of it should we analyse? Whereas storage might have been a barrier a few years back, there are now relatively low cost options available to store data. Before analysing big data, companies should first outline its potential uses, followed by data validation and homogeneous grouping as far as possible with the goal of obtaining a subset that can be used for analytical purposes. It is important to determine the balance between having sufficient information to enable the company to make wise strategic business decisions and selecting the relevant data to use from a wide variety of sources.

By combining big data with analytics, companies can accomplish goals such as determining the root causes of failures and shortfalls in near-real time, recalculating entire risk portfolios in a matter of minutes, as well as detecting fraudulent behaviour before it affects your company adversely. Social media data, together with claims data can be used to verify whether a claim is valid, whereas other sources of big data can be used against the existing customer base to anticipate customer needs in advance by offering new products or renewals and hence retaining existing customers. Or, if a customer’s personal circumstances change, for example when a student graduates and starts earning his/her first salary, or when someone retires and needs an annuity rather than a saving product.

The final step in making big data work for your company is to research the technologies that help you to make the most of big data and analytics. For this purpose, is definitely worthwhile considering:

  • Faster processors
  • Cheaper, but more abundant storage
  • Affordable open source, big data platforms
  • Parallel processing and clustering
  • Cloud computing and other flexible resource allocations.

Scheduling your validation over draft and final submissions

A client recently asked our opinion on how to spread validation over the draft SCR and final SCR submissions.  There aren’t definitive answers to be found saying exactly what to do, but there are several considerations we think you should take into account when looking at this.

Materiality is probably the biggest factor to consider in your decision-making.  Make sure you carry out as much validation as possible on the material risk classes and material risk groups for the final run of the model.  You can probably do the lower materiality risk groups or risk classes on the draft. More