Price War Leads to Bankruptcy

Prices are set for risks in a competitive market, where competitors may be driven by different motives. The rationale behind pricing insurance products is, among other things, for the purpose of setting a commensurate premium. We find different premiums from different companies due to the diversity of their set up and operational efficiency.

The Ethiopian insurance sector is preoccupied with a price war, rather than seeking to improve service quality and product differentiation. The price war, however, will most likely lead to bankruptcy.

Pricing insurance in the Ethiopian market is becoming challenging, in light of a technologically intransigent environment.

How can insurers price a product without knowing all the production costs? The insurance industry faces this problem every day. While most industries know the cost of the materials, labour and profit margins to calculate the price of their products, insurers do not know the cost of the product when it is sold. The true product cost may not be known for many years, until claims are paid. Therefore, insurance companies – specifically actuaries – rely heavily on historical data to predict future behaviour for premium rate creation so they can price products. What is our view with regards to rating products?

The impact of a claims management cycle should consider time and the related cost of parts during compensation on pricing motor insurance. Do we really adjust our premium to inflation and exchange rates? What is the relationship between outstanding claims and cost of reinsurance? These are some questions the Ethiopian insurance sector needs to address.

The sector has to come up with scientific parameters with models to bring about a profound change, rather than intensively immersing itself in the already lost skirmish of price war.

If two competing firms with a different business philosophy, strategy, capability, vision, work process and mission come out with the same pricing for their product or service, we should question the value of dynamism.

Pricing is subject to analytical value decisions, which essentiality attach a different rate for the same type of risk from different companies. Researchers of pricing insurance products and mathematicians, actuarial and other risk analysts pinpointed major reasons why pricing insurance, or premiums in insurance, vary.

Some factors include the target return on capital and amount of capital allocated; a difference in management expense; a risk may be regarded as prestigious; variation in investment performance; desire for market share; desire for business from which to gain experience; profit assessment by clients rather than by classes of business; the need to achieve a budgeted level of income, and different assessment of the risk premium. I have serious doubts if the pricing method employed by Ethiopian insurance companies embraces these factors.

Other categories of factors cause variances in pricing insurance and very near to the Ethiopian insurance experience consists outstanding claims, technical provision and its associated costs, marketing costs/intermediaries, claims inflation, exchange rates, and financial fees and management expense. These can be augmented with the pure premium to arrive at a commensurate value. I wonder if we adapt frameworks professionally in our day-to-day pricing insurance quotations.

Loopholes in the system and inadequate knowledge of pricing in insurance will naïvely lead the sector to economic bankruptcy. We need to adapt a professional antidote in order to set a commensurate premium and to manage the perception index of policy holders, as well as boosting dynamic insurance service and product differentiation.

I argue that scientific competition has to force insurers to adjust rates more frequently to retain existing customers and attract new ones, compared to the traditional response to customer enquires towards valuing our product.

Insurers looking to implement a price optimisation strategy must consider these essential components.

Many insurers take years, if not decades, to implement a new rating structure, and the effective performance of new models rapidly deteriorates over time due to dynamism and endless price sensitivity of customer behaviour. But inevitably, insurance is changing its approach with regards to product pricing. As insurance becomes more and more of a commodity, insurance companies are trying to differentiate themselves from their competitors based on customer services, claims experience and financial strength, but mostly by price. This is true and indeed a normal course of action. And, to gain a competitive advantage, insurers have to use price optimisation. The question is how do we optimise operating traditionally without dynamic pricing model and knowledge in Ethiopia?

Price optimisation goes beyond the traditional insurance ratemaking process, with sophisticated methods such as predictive analytical models, customer lifetime, value calculations and scenario simulation to increase rating accuracy and improve profitability. Although the concept of price optimisation is relatively new to the insurance industry, it has been used in other industries, such as travel and retail, for a number of years.

Every now and then, we argue that regulations, lack of reliable IT tools and even limited online presence for modelling insurance pricing as reasons for failing to come up with dynamic pricing models. I believe the trend of regulations and the growth of aggregator websites will challenge insurers to work on pricing their product in order to benefit from price optimisation. Actuarial driven pricing is becoming more of a reality.

Key to the success of using price optimisation is the quantity and quality of the available data, especially claims and customer data. The emergence of business analytics software, like data exploration and visualisation tools, helps insurers refine their analysis and evaluation of certain risk elements. For example, 20 years ago, credit score was probably deemed unimportant. Now it is probably the most used variable in determining premium rates.

Predictive modelling. Insurers must use analytical tools to perform what-if simulation and scenario testing to forecast future behaviour and improve the underwriting performance of the insurance company. High-performance analytics. To process the large data quantity and perform complex analytical calculations, insurers need an in-memory or distributed computing environment. Price optimisation requires an in-depth understanding of the competitive landscape, industry-wide pricing strategies, and customer demographics and buying preferences. Especially in lines of business where price is a key differentiator – such as automobiles, home and some commercial lines – price optimisation represents the future for insurance.


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