Dynamo Analytics

Archive for April, 2016

Refocusing Business Planning – Aliska Van Niekerk

A useful tool in refocusing business planning is a monitoring and feedback process that provides information to management, allows assumptions to be updated and trends to be analysed. An efficient monitoring and feedback process will enable a business to adjust to changes in the market quickly, take corrective actions when needed and make pro-active decisions in order to manage the business effectively. Pro-active decisions can include identifying profitable products, sales channels and markets.

There are numerous analyses that can be undertaken, some of the most important ones, in my opinion are:

An analysis of the loss experience. The key here is not only the absolute amount, but the trend of loss escalation in relation to an appropriate inflationary index, in relation to market information or other small businesses.

Lapse rates should also be analysed. Some of the most important factors that impact the lapse rates are the current economic outlook, the product’s competitive situation and the perceived value of the product to the client. Several insightful splits of lapse rates are possible such as by region, product type and sales channel – the analysis will be important from the viewpoints of sales management, commission clawback calculations and measuring marketing campaign efforts.

An analysis of expenses is mainly concerned with the correct allocation of expenses between the different products in a portfolio which is used in premium setting and financial planning. Initial, renewal, claims and investment expenses should be identified separately. Expenses can be split further into overhead expenses and expenses dependent on the volume of business, although it can be difficult to distinguish between the two in practice.

Sales can be monitored against targets in order to understand the strains caused by the volume of new business in comparison to the capital set aside for the purpose. it also allows the monitoring of the mix of business versus the mix assumed in the pricing basis (as this affects the loss experience), staffing levels in terms of numbers and skills against those required by the business written and also commission paid against the amount assumed in the pricing basis.

Furthermore, an analysis of the investment experience should be undertaken in order to choose the investments that will maximise the overall investment return, subject to the liability based constraints and the risk appetite of the business.

A surplus and profit analysis allows management to understand the financial impact of divergences between the actual experience and the valuation assumptions.

Ultimately, the test of any analysis is whether it can contribute towards the business in a meaningful way. Good management information reports should be backed by reliable data, produced timely and be provided at an appropriate level of detail to allow management to make the right decisions and manage effectively.

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.