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«Protecting the poor A microinsurance compendium Edited by Craig Churchill Protecting the poor A microinsurance compendium Protecting the poor A ...»

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For the information to be useful, parts of the discussion are by necessity somewhat technical – this should also convey the message that professionals are needed to price products correctly. The example illustrated in Box 44 is a Pricing microinsurance products 239 common occurrence. Informal insurance schemes that do not rely on actuaries to price their products, including several of those depicted in the case studies, tend to be too cautious and charge too much, or they under-price the product and threaten the viability of their scheme – neither of these results contributes to successful microinsurance.

Pricing problemsBox 44

An MFI in India started its microinsurance programme with a single-level premium for life insurance coverage for all of its clients. In February 2003, GTZ sponsored a training session on pricing. When the rate components discussed in this chapter were reviewed, the MFI realized that its scheme would be bankrupt within a few years. The session helped managers understand their data needs, how data are used in pricing, and that all of the components of pricing must be considered when deriving the appropriate rates needed for long-term MI sustainability.

Aside from the direct underwriting impact of pricing, it is important to note that appropriate pricing can help build trust in the microinsurance product, while a poorly-priced product can lead to abrupt adjustments in premium rates and an erosion of confidence in the scheme.

1 Database design requirements for pricing (and sound microinsurance management) Premium rates are established by the actuary using available experience data.

In the early days of a microinsurance scheme, when there is still no specific data available on the proposed participants for calculating their expected claims, population statistics and data from similar programmes have to be used to the extent that they are available. The actuary must then rely on observations of and assumptions about the participants and their proposed or ongoing insurance scheme to adjust that information in the calculation of the premium rates.

Actuaries prefer to use the specific data of the group or population to be insured since this will result in more reliable and accurate rates. Credibility of the data increases with volume. In deriving crude group mortality rates for example, the actuary would consider a minimum of 10,000 life years of exposure1 to be necessary for the data to be considered fairly credible – less credibility would be attributed for smaller data amounts.

–  –  –

From the beginning then, it should be clear that one of the key determinants of a scheme’s long-term success is a properly designed and well-maintained database and management information system (MIS) for capturing and screening the data used in subsequent pricing reviews. The main objectives of an MIS are to accumulate data and to assist with managing the insurance scheme in a professional, efficient and technically prudent manner. The database design should be based on the relational database model, built with the help of IT professionals, and with inputs from an actuary and the management of the scheme (see Box 45).

Database design problemsBox 45

TYM in Vietnam operated its microinsurance scheme for several years.

However, the database was not properly designed and maintained, which made it very difficult later on to analyse and re-price products. For example, the relational database table with loan information could not be properly linked to the table containing member information, making it impossible to calculate risk exposures by age and gender.

Grameen Kalyan’s micro health insurance has kept detailed information at each of the health centres. This information could have been very useful for analysis and pricing purposes if each client’s membership number had been retained from year to year rather than assigning a new number on each renewal, or if the records had had another unique identification field for each member. This unique identifier would have allowed the actuary to track the exposure and claims for each member through the years.

–  –  –

Great care and time must be invested in the design phase of the database since it is the foundation of a good MIS. The following tables of information must

be included:

a) Institutional and branch information If the scheme is servicing several institutions then the details of each institution needs to be maintained.

–  –  –

These data are used to prepare a demographic profile of the group, which is needed for projecting future mortality trends. If the country has a national ID number then that could be used as the unique identifier; otherwise, the scheme will have to generate its own unique ID number that clients should retain throughout their history with the scheme.

c) Beneficiaries and covered dependants For health insurance, some of the same data should be maintained for each covered dependant, including name, unique identifier, gender, date of birth, relationship to the participant and a photograph.

d) Coverage history What are the coverage details? A coverage history for each enrolled person has to be kept, not just the coverage currently in effect. If a change is made to an individual’s coverage then a new record should be created in the coverage history table with the effective date of the change as one of the fields in the record.





Apart from monitoring and for administration purposes, the main objective of keeping a coverage history is to permit the reconstruction of a complete history of each person’s exposure to every covered risk so that expected claims can be calculated for comparison with actual claims experience (this will be further discussed in the next section). In fact, software applications can be developed to monitor the expected claims in relation to actual claims on an ongoing basis – this is a powerful tool to assist microinsurance managers. For example, at Yeshasvini in India, management noticed that one of its accredited hospitals was performing an unusually large number of hysterectomies. On further investigation, it found that the medical management of patients was not appropriate, resulting in termination of the relationship with the hospital. AssEF had a similar experience, as described in Box 46.

242 Microinsurance operations

Importance of a health insurance MIS: Experience of AssEFBox 46

For its health insurance scheme, AssEF in Benin carefully monitors actual claims in relation to expected claims. In some cases, substantial differences between projected and actual figures emerge. Once it identifies the discrepancies, then management can determine how to address them. From the

results in 2004 (see table below), two issues captured management’s attention:

the high rate of prenatal consultations and nursing services.

–  –  –

At AssEF, a three-month waiting period for new members is the principal barrier against adverse selection. This safeguard appears to be sufficient to reduce opportunistic behaviour with regard to outpatient consultations and hospitalizations; however, it remains to be proven in terms of planned health services. For example, by monitoring claims, management identified a strong adverse selection phenomenon with respect to prenatal consultations (subsequently affecting deliveries in 2005).

This phenomenon was heightened following the numerous drop-outs that began in mid-2004, since many of the remaining women were pregnant.

Specific measures could have been implemented to curb adverse selection, such as increasing the waiting period for prenatal consultations and deliveries. However, a decision was made to use the phenomenon for marketing purposes. As the claims in question were not out of control, they could be used to increase the visibility of the scheme, particularly with a target group made up of women. The frequency of utilization is very carefully monitored Pricing microinsurance products 243 and measures could still be implemented if the risk of adverse selection becomes too significant.

As for the nursing services, the frequency of utilization was also monitored according to the healthcare provider, and these results showed that one clinic had a much higher claims experience. In this case, microinsurance clearly led to a behaviour change. As the beneficiaries were insured, the clinic asked them to come back several times during the same illness to receive treatment; the first visit is recorded as a consultation and the subsequent ones as nursing services. The scheme’s management approached the clinic and discussed the anomaly in treatment patterns, and this resulted in a return to more normal claims experience. Had the management not monitored the situation, the claims would have exceeded the financial resources of the plan and possibly resulted in its bankruptcy. A well-designed database and MIS is crucial for microinsurance management.

Source: Adapted from Guérin, 2006.

e) Premium history For each product, a premium payment history must be kept for each insured, including the following fields: payment date, payment amount and receipt number if applicable.2 Besides being needed for administration purposes, the premiums history will be used to study the pattern of drop-outs (lapses and surrenders), which in turn will affect the pricing of many products (see Box 47). For products with savings and equity accumulation features, the payout values will depend on the premium history since interest must be credited accordingly.

VimoSEWA’s renewal ratesBox 47

VimoSEWA in India implemented a new MIS system in 2001, which permitted it to measure its renewal rates. Management was quite surprised to learn that the organization had a very low renewal rate – just 22 per cent for members paying annual premiums. Having been made aware of the problem, management took steps to increase the renewal rate by communicating the value of maintaining insurance to members and by setting target renewal rates for each Aagewan (sales promoter).

–  –  –

All records should be kept indefinitely, either in the current database or in an archive, for cumulative experience and actuarial analysis. The data should be carefully managed just like any other resource of the microinsurance scheme.

Actuaries attach great importance to the way data is collected and managed because erroneous and incomplete data can be more of a liability than an asset if misinterpreted. To ensure the completeness and integrity of the data, robust controls and thorough cross-checking should be built into the MIS. Standard coding values and formats should be used to simplify queries and to improve consistency. For example, participants’ occupations should be selected from a menu of standard occupation codes rather than being typed in each time.

All data should also be verified as far as possible against other independent systems such as accounting. For example, premiums, commissions and claims must be balanced against the accounting system at the end of each accounting period to make sure that there is consistency between the two systems (which is also a very useful integrity check for the accounting system). Furthermore, database changes should be monitored and confirmed regularly against manual systems.

2 Pricing components, key factors and methodology The primary objective of any pricing exercise is to ensure that premium rates are sufficient to realize the scheme’s aims and meet its obligations in the long run, while maintaining equity among the participants. For life and health insurance, rates can either vary by age (age-structured) or, as is most common, remain the same for all participants (sometimes described as “community rating”). If level rates are used, it is advisable to impose a maximum entry age and perhaps also a maximum coverage age (otherwise the rates will probably be too high, which in turn will affect the marketing of the product).

An alternative to a maximum coverage age is a declining schedule of benefits for the older participants.

Several components should be considered in establishing premium rates.



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