Thursday, February 5, 2015

To cut or not to cut: Tug of war between RBI and Govt

This article was first published in the business section of on February 03, 2015; Co-Authors: Puran Singh and Prachi Patke (BITS Pilani, Goa Campus).

Too less sugar means a bitter coffee, and too much sugar has health consequences. Both types have their patrons. Reserve Bank of India (RBI) likes its coffee bitter. Central government likes it too sweet. The twist is that there is a recipe sufficient only for one cup! RBI likes to see inflation under control and therefore does not like to cut interest rates when inflation is already at high. On the other hand, the central government bends more in favour of high economic growth and likes to see interest rates cut so that cost of capital goes down and economic activity picks up helping growth of economy.
For laymen, rate cut refers to reduction in interest rates charged by RBI on lending to commercial banks (Repo Rate). A high rate makes borrowings costly and reduces money supply to the markets. Reduced money supply in markets pins down demand in economy as a whole and helps prices come down. On the flip side, reduction in aggregate demand slows down economic activity and reduces growth in Gross Domestic Product (GDP) which is an indicator of economic growth.
For 16 months, Raghuram Govind Rajan, the Governor of the Central Bank of India, served the coffee bitter before finally letting the Finance Minister, Arun Jaitley, have a sip. The cries for rate cut had begun by the time he presented his third bi-monthly review for Financial Year 2014-15 in August 2014. Last year, the new Government, after its formation, persuaded Rajan to cut rates to provide a push that economy needed. Industry leaders as well pitched in favour of rate cuts.
Although Jaitley openly cited high cost of capital as the ‘one singular factor’ slowing down growth of the manufacturing sector, he did not come down hard on the RBI Governor to cut rates. In November 2014, Jaitley, was quoted saying "RBI, which is a highly professional organisation, in its wisdom decides to bring down the cost of capital, (this) will give a good fillip to the economy". Indian economy experienced less than 5% GDP growth rate during Financial Year 2013-14.
Rajan, on the other hand, had said, "Monetary stimulus will not do. The government needs to work on infrastructure". Apart from credit availability, constrains on inputs such as power, land and infrastructure, and Government policies impact output of an economy. Stable output helps exports and, eventually, in controlling Current Account Deficit (CAD).
Rajan didn’t budge from his stand on rate cuts since he joined as Governor of RBI in September 2013. Now, with inflation (Consumer Price Index) under the targeted 8% for January 2015 and assurance from the Government to adhere to CAD target this year, he cut the rate by 25 basis points to 7.75% in January 2015 much to the delight of the Finance Minister who called it a “welcome decision”. Drop in commodity prices in international markets, crude oil in particular coupled with easing inflation in vegetables in domestic markets allowed enough headroom for Rajan to cut rates. Also, Inflation Expectations Survey of Household by RBI in September 2014 indicated a downward revision in inflation expectations in coming year.
In ballroom dance, the couple has to learn the art of stepping forth and back in coordination and be careful not to step on partner’s toes. At the moment, RBI has stepped back and let the Finance Minister have his way by cutting rate. And this could be first rate cut in a series of few more if inflation hovers around 4.5%-5% on an average during the next one year, giving Rajan room to offer a gradual 100 basis points rate cut at the most.

In a multiple-shot game, players need to understand unsaid rules, gain trust of competitor with conflicting interest, act in coordination to win one by one, and not scare away the prey. The ball, therefore, is in Finance Minister’s court to make hay while the sun shines and push growth agenda in budget to be presented on February 28th 2015. And, as for that one cup of coffee, split it in two and add sugar to taste, serve hot.

Wednesday, February 4, 2015

Nat Cat Modelling

This article was first published in the IIB Bulletin, 2015, Vol. 1, Iss. 3, pp10-12

Co-Author: Pushpendra Johari (RMSI Private Limited)

Images of destruction caused by the Uttarakhand Floods, Cyclone Hudhud or the Bhuj Earthquake are still livid in the minds of most of us. While loss of lives and property are always painful, the scale of destruction during a natural disaster hits us with a sense of despair at the helplessness of human beings. Advances in technology and development in economy could not prevent the Tsunami or the Katrina.

India is prone to natural disasters due to its climate and topography. As per the research done by Mishra (2014) during the past 100 years (1913-2013), 51.4 percent of the natural disasters in India were due to floods, 32.7 percent from storms, 7.4 percent from landslides, 5.6 percent from earthquakes and 2.9 percent from droughts.

The economic losses to the nation are huge; to give a perspective, in a report in 2003, World Bank estimated that the Economic losses to India due to natural disasters were around 2 percent of the Gross Domestic Product (GDP), per annum.

Reported direct losses on public and private economic infrastructure in India have amounted to approximately $30 billion over the past 35 years [up to 2001] (nominal values at then applying exchange rates). Since less than 25% of the registered loss events actually provide any loss estimates, the official numbers substantially understate the true economic impact of direct losses. A crude grossing up for reporting frequency indicates that direct natural disasters losses equate to up to 2% of India's GDP and up to 12% of federal government revenues”...Pg 8, The World Bank Report (2003).

The stakes could be as high as 4.4% and 6.5% of the States GDP in states like Gujarat and Orissa. The report also noted that the official figures are generally lower than the actual losses and it also observed a rising trend in the losses over the years. It must also be noted that these figures do not include the cost of rehabilitation and restoration.

According to a report on “Natural Hazards, UnNatural Disasters” by the World Bank and the United Nations, the impact of natural disasters on the GDP is 20 times higher in developing countries than in industrialized nations.

The years 2013 and 2014 have seen catastrophes like the Uttarakhand Floods and the Cyclone Hudhud, which have resulted in large losses, both of lives and property (Table 1).

No. Killed
No. Total Affected

~Economic Losses
(in Rs crores)
~Insured Losses   
(in Rs crores)
Uttarakhand Floods
Cyclone Phailin

Cyclone Hudhud
Source: EM-DAT: The OFDA/CRED International Disaster Database
*Estimate based on news reports

The irony is that the General Insurance penetration in India is very low, especially for personal property. The gap between people who need Insurance most and the penetration of Insurance amongst them is huge. The pace at which the economy of India is growing is indicative of a huge potential for increasing the insurance penetration.

The government of India is desirous to make Insurance as the primary mechanism for disaster risk financing in India (Ref. Disaster Relief and Risk Transfer through Insurance, IRDA-NDMA July 2013). A panel including NDMA, IRDA and general insurers in India is considering several options including:
·         Setting up a pool for states, NDRF, etc.
·         Parametric insurance solutions for NDRF
·         Optional simple Indian Natural Catastrophe Insurance Policy
·         Mandatory property insurance in highly prone urban areas

However, there are several questions that need to be answered before such schemes could be launched. Some of these questions are:
·         How much fund is needed for the pool
·         Who would fund the pool
·         Categories of population to be covered under the Indian Natural Catastrophe Insurance policy
·         How to price the coverage of such policies
·         What should be the triggers and how much payment should be associated to specific triggers for parametric insurance solutions, etc.

Natural Catastrophe modelling is the science that can help in finding the answers to several of these questions.

Probabilistic NatCat modelling can be used to arrive at the possible economic loss scenarios associated to various return periods, the impact of specific historical or latest hazard events, as well as the average annual direct economic loss by state or any other resolution at which the pool needs to be setup. Figure 1 shows the impact of cyclone Hudhud based on RMSI CycloneRIsk Model. 

Figure 1: Cyclone Hudhud wind and surge estimates using RMSI’s CycloneRIsk model.

Based on return period scenarios various categories of population that are under high risk zones could be estimated. Return period losses and average annual loss could be estimated for all these population categories thereby giving insights into the coverage pricing for various population categories. Based on the income levels and sample surveys eliciting willingness to pay for various population categories, an estimate of insurance affordability could be arrived at. This information could be combined with the NatCat modelled loss estimates to decide if the entire burden of the insurance could be passed to any specific population category or not.

Using probabilistic NatCat modelling, homogeneous risk zones could also be created , that associate hazard intensities to average losses within every homogeneous zone and provides a hazard risk score. Specific rates could be developed by risk zone for taking into account the NatCat risk in pricing of policies. Figure 2 shows the flood hazard risk score zones. This could serve as a basis for the definition of the triggers for specific areas along with payouts associated to the trigger. For every such homogeneous zone, an authentic source that provides the hazard intensity values at the time of the event will have to be setup to ensure success of parametric insurance. So, NatCat modelling not only helps to setup the triggers and associated payouts but also the number of  trigger monitoring stations and areas where these should be setup.

Figure 2: Flood Risk Score Map categorizing every pincode in India flood risk categories

The models could also be used to test out various insurance penetration scenarios and how various levels of penetration could impact the risk as well as pricing of the coverage.

Health is the greatest wealth

This article was first published in the IIB Bulletin, 2015, Vol. 1, Iss. 3, pp6-7

Of late it has been noticed that the trend has been shifting from communicable diseases to non-communicable diseases. As the IRDA Chairman, Shri T.S. Vijayan pointed out in FICCI’s 7th Annual Health Insurance Conference: Health Insurance 2.0: Leapfrogging beyond Hospitalization on December 5, 2014,

“The shift to non-communicable diseases is profound and impacts the elderly more than the average person, particularly in India”.

We found that people above the age of 60 had the maximum number of claims for Circulatory diseases. This category of diseases not only has a higher average claims paid, it also results in higher number of days spent in the hospital on an average. Arthropathy and Nervous are other category of diseases which result in high claims amount paid but the number of claims are not very high.

While Circulatory diseases result in higher average claim paid, the number of claims for infectious diseases is the largest. Number of claims was found to be the highest for children below 5 years of age under the infectious diseases category.

India has one of the highest reported cases of communicable diseases amongst the BRICS nations. According to a report by the Organization for Economic Co-operation and Development (OECD), India witnessed 253 deaths per 100,000 persons, in 2012, due to communicable diseases alone. This is much higher than the global average of 178 (Source: OECD Health Statistics 2014).

Top 10 Diseases for FY2012-13: Number of Claims, Average Claims Paid and Total Claims Paid 

Source: IIB Data

Clinical Findings refer to ICD10 code R00-R99- Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified.

Only claims where the amount of claims paid is greater than Rs5,000 are considered for this study. For a large number of records, disease codes were not filled appropriately and hence they were ignored.

Tuberculosis, Malaria, Dengue, Hepatitis and many other infectious diseases are a major threat in India. Many of these are “zoonoses”, that is diseases which pass from the animals to the humans. Lack of toilets leading to defecation in the open, open sewers, general lack of sanitation, clean drinking water, food and surroundings are some of the main reasons for the spread of such diseases.

Out of pocket expenditure on healthcare in India is very high when compared to other nations, at about 60-70% of total health spending. The government spending, as a percent of Gross Domestic Product (GDP), was only about 4.8 percent in 2012, in India. People who are most prone to infectious diseases are the ones who are the least aware about the need to buy Health Insurance. This might explain the high average number of deaths due to communicable diseases in India, as reported by OECD.

Better awareness and improved GDP per capita income is leading the growth of the Health Insurance industry. The industry has grown at a Cumulative Average Growth Rate (CAGR) of over 30 percent in the past seven years and is expected to continue growing at a fast pace in the coming years too. However, a concerted effort on the part of all stakeholders is required to spread awareness about not only Health Insurance, but also the need to maintain better hygiene standards. In large cases, development of basic infrastructure would be required before basic hygiene standards can be met.

The Complement of Credibility

This Research Summary was first published in the IIB Bulletin, 2015, Vol. 1, Iss. 3, pp8-9

Co-Author: Vishnuvardhan Pallreddy, IIB

Joseph A. Boor, FCAS, Ph.D. has been a working casualty actuary since 1979 and a Fellow of the Casualty Actuarial Society since 1988. Over a long and varied career he has had roles as diverse as regulator, Chief Actuary, consultant, and regional actuary. Currently, he works as an actuary for the Office of Insurance Regulation of the State of Florida in Tallahassee, Florida. He is the author of several published articles, including theoretical contributions to the theory of credibility and the optimum weightings of years of data and single-topic ratemaking papers on the complement of credibility in pricing and tail factors in loss reserving. He has a Bachelor’s of Arts degree in Mathematics from Southern Illinois University in Carbondale, a Master of Science degree in Mathematics from Florida State University, and a Doctor of Mathematics degree from Florida State University.

Complements have a special role in ratemaking exercises where the data is sparse or has high deviation from the mean over the years and hence has low credibility. “The Complement of Credibility” paper by Joseph A. Boor (1996) provides a good overview of the qualities and effectiveness of a good credibility complement and explains different credibility components commonly used. The paper also looks at the practical aspects of selecting a complement.

According to the paper, “The complement of the credibility deserves at least as much actuarial attention as the base statistic (historical loss data)”. Special attention should be given to its unbiasedness and accuracy. In some cases, interdependence must be avoided. Ease of computation and implementation must be reasonable. Explainability of statistics used must be considered, too.

Fundamental Principles
The paper explains a few issues that an Actuary must consider before selecting the complement of credibility:

Practical Issues: Complement should be readily available. The best possible statistic to use is the next year’s loss costs which are unknown. It has to be chosen from the available statistics. Ease of computation would also be a factor to consider as it involves time, costs and also increased chances of error.

Competitive Market Issues: The rates that are eventually produced will be subject to market competition. If rates are too high or too low, the outcome will not be desirable. So, the rate should be neither too high nor too low over a large number of loss cost estimates (unbiasedness) and the rate should have as low an error variance as possible (accuracy).

Regulatory Issues: Regulators typically require that the rates are not inadequate, not excessive, and not unfairly discriminatory. This implies that rates should be as unbiased as possible. It could also be implied that rates should be as accurate as possible, as highly inaccurate rates pose a greater risk of insolvency through random inadequacies.

Statistical Issues: For greater accuracy, error variance should be lower. If complement of credibility has low error variance in its own right and relatively independent of base statistic (which receives the credibility), the resulting rate will be more accurate.

When both the base statistic and complement are unbiased, the predictions are generally best when there is actually a negative correlation between the two errors (that is, they offset) but this rarely occurs in practice. So, a complement of credibility is best when it is statistically independent of the base statistic.

Based on the above four issue that must be considered by an Actuary when selecting a complement, Boor summarizes the desirable qualities that a complement of credibility should have:

1.       Accuracy as predictor of next year’s mean loss costs
2.       Unbiasedness as a predictor of next year’s mean subject expected losses
3.       Independence from the base statistic
4.       Availability of data
5.       Ease of computation
6.       Explainable relationship to the subject loss costs

Commonly used Components
Boor goes on to compare different types of complements used by Actuaries for First Dollar Ratemaking and Excess Ratemaking. Few often used methods for First Dollar ratemaking discussed in the paper are:
·         Using loss costs of a larger group including the class-Bayesian Credibility
·         Using loss costs of a larger related class
·         Harwayne’s method
·         Trending present rates
·         Applying the rate change from a larger group to present rates; and
·         Using competitors’ rates

For Excess Ratemaking, the four methods discussed in the paper are:
·         Increased limits factors
·         Derivation from a lower limits analysis
·         Analysis reflecting the policy limits sold by the insurer; and
·         Fitted curves

The use of the complement of credibility may differ from case to case, depending on the “availability of data” and reasonability of effort. For example, in the case of excess (or large) losses, fitted curves uses data available with the Insurers and is generally unbiased, but is complex to compute and may be difficult to communicate as well.

For pure premium ratemaking, using competitors’’ rates may be easy to use, especially for new companies or companies with low experience, but may suffer from inter-company difference in portfolio mix and may be harder to obtain.

“In Harwayne’s method, actuaries use countrywide (excepting the base state being reviewed) class data to supplement the loss cost data for each class, but they adjust countrywide data to remove overall lost cost differences between states (or provinces)”.

Many such practical insights on the above listed methods are provided in the paper along with the model itself and examples. As the Indian market moves towards an era of ratemaking, his paper is a valuable guide to the choice of a complement of credibility. The paper can be read at the following link:

Boor, Joseph. A., “The Complement of Credibility" (Proceedings of the Casualty Actuarial Society,  Vol. LXXXIII, Part 1, No. 158, 1996, 32p;

Tuesday, February 3, 2015

Where is the Indian Market headed?

This interview was first published in the IIB Bulletin, 2015, Vol. 1, Iss. 3, pp4-5

Bruce A. Howe is the Chief Operating Officer, QBE Insurance, for Asia Pacific. In a career spanning more than three decades in Insurance, he has worn many a hats. He has handled valuations of Insurance companies, been an Appointed Actuary, undertaken benchmarking exercises, has been involved with risk management and governance, has led teams to devise entry strategies into India, China, Korea, Taiwan, Indonesia and Vietnam for HSBC Insurance. He was the Chief Executive Officer of HSBC Insurance UK, Europe and Middle East before joining QBE in 2013.

Howe is a Fellow of the Institute of Actuaries of Australia and holds a Masters of Economics degree from the University of New England, Australia. He has authored and co-authored several books in the area of General Insurance.

In a conversation with Dr. Nupur Pavan Bang of the Insurance Information Bureau of India, Howe talks about his experience with the Indian Insurance Market, from the perspective of a man who has worked in many different markets.

What is your take on the Indian Insurance market?
The first thing that strikes me is that it is difficult to sell insurance in India. People are willing to take the risk of not being insured. Even Life insurance is challenging.  My current focus is general insurance.  The Indian market is structurally unsound with industry Net Combined Operating Ratio (NCOR) in excess of 150% in recent times; even with investing the ‘float’ aggressively it is not possible to be profitable.

General insurance companies should be making money from accepting underwriting risks and therefore are uncorrelated with market risks.  When NCOR rises above 100, GI companies become asset managers correlated with markets.

What according to you are the strengths of the Indian market?
If we look at the S-curve relation between per-capita income and insurance penetration, India still has very low penetration. The Life business has a penetration of about 3% and the Non-Life business is less than 1%. As the middle class emerges with the penetration rising to 8-9%, the Indian market will be very big.

What are some of the challenges that you face while working in the Indian market?
There are several.  In my view low FDI inhibits innovation.  Retaining state-owned insurance companies with large market shares inhibits the development of sound competitors with rational pricing.

What are the kinds of innovations that the Indian market needs right now? What would you like to see the market doing that it is not already doing?
By definition, innovation is difficult to predict and it has a 'non-linear' effect on markets. But the existing trends are clear and have a long way to go yet. Customers increasingly are accessing all services through mobile technology and this is equally applicable to all forms of insurance. Secondly, providers of services, including insurers,  seek at least some customisation and timeliness to their product offering based on the unique characteristics of the customer and what they happen to doing at that moment in time. To give some reality to these abstract ideas, imagine a customer is purchasing Canadian dollars at their bank in January. There is a fair chance they are considering skiing on a holiday. There is a need to check the bank's records for whether the customer has travel insurance and if it currently covers skiing. The right offer in real time, by mobile or by the banking staff if in the branch, is the need of the hour. It could be a travel policy covering skiing or an extension to an existing policy to cover skiing. GEN Y is already thinking this way.

Do you have a view on the Insurance Amendment Bill which has been referred to the Select Committee of the Upper House of the Indian Parliament? Does 49% Foreign Direct Investment interest you?
The short answer is yes. The long answer reflects the need for sound development of the industry for meeting the insurance needs of Indian consumers and businesses.  Insurance has a significant role to play in the further economic development of India.

So where is the market headed?
Things have changed a lot since 2005-2006. Good thing about India is that it is flexible in dealing with Governments. It moves forward, in spite of the political party at the helm.

Infrastructure has improved. People are getting a taste of the digital experience. New businesses are making progress, like travel insurance and medical coverage. There is a lot of introspection happening. Market has started talking about cost of distribution and expense ratio management.

These are all steps in the right direction in my opinion.