Health insurance awareness in India is still extremely low

Howard Bolnick, who taught insurance at the Kellogg School of Management for 13 years,
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was in India for a five-day executive development programme on healthcare financing and health insurance, organised by the Insurance Foundation of India (IFI). Bolnick, a past president of the US Society of Actuaries, took time off to talk to Sarbajeet K Sen on how he viewed the Indian health insurance sector. Excerpts:n How do you see the present stage of the Indian health insurance market?It is very much in a formative stage still. I know there have been health insurance products roughly for 20 years but still the penetration level is barely 2 per cent of the population. This is very, very low. It is somewhat stagnant and listening to the people here, it is very clear in some sense it is not functional. I think insurance companies in India have things to learn; that they must do better. There is a generally bad but improving relationship between the health providers and the insurance companies. There is a low level of awareness of health insurance among the population. It is really not the kind of situation that is ready for takeoff.

However, given that the regulatory framework is improving and that many new players including foreign companies are willing to come in, I think things are at a takeoff stage.n Can the penetration levels of health insurance see a dramatic change in India going forward?You got a situation where there is 2 per cent market penetration. You will never get to 100 per cent. There are around 300-350 million people whom you could potentially sell health insurance to. I would argue to the health providers that if they cooperate with health insurance companies, the penetration can be as much as 150-200 million, which by the way will be growing. I am told there are some companies that take six months to pay claims. That’s absurd. How can you have a relationship with health providers if you are sitting on their money for so long?There are other things that from a US perspective might look perverse. I understand, not surprisingly, when an individual goes into a hospital here, one of the first questions asked is if the person is insured or not.That happens in the US too. But in the US context, you get a better deal than if you are not insured, but here it is a worse deal. Should that continue? No. At some point of time, there needs to be cooperation between the insurance industry and the health providers to set up a foundation to do things that makes sense to both of them.n Of late, there has been some debate on whether one should retain the commission structure in the insurance sector. What are your views given your US experience?My understanding here in India is that most of the market is retail. You have to reach them not through a place of employment but you have to reach them through an intermediary. In that sense, in the retail market, intermediaries are vital. In the US, agents are generally compensated by one of two ways, depending on the situation. Sometimes it is consultancy, sometimes it’s a flat fee. But in the retail market, it is a mainly percentage of the premium. It is a balancing act but where it will end up in India, I don’t know.n The government here is proposing to reduce the paid-up capital requirement of standalone health insurance companies to Rs 50 crore from the present Rs 100 crore. How do you see this move?I have heard that. Let’s put it this way. You really have to have capital in this industry, particularly when you are a nascent stage of development. I keep hearing about loss ratios of 140-150 per cent in India, which is very high. This really brings in the need of capital. What happens if you run out of capital? It is not good for the industry and not good for the policyholder if the company goes bankrupt. But bankruptcies and such types of disruptions will not help in the growth of health insurance in general. So, if I were a regulator, I would want to be careful to make sure that there is adequate capital.n What is the average loss ratio of health insurers in the US?Companies are making money. Health insurance companies in the US have been one of the real darlings of Wall Street.n Is it because they don’t pay claims?That’s bad. I have heard people say that. It is a political debate. There are certainly instances of it. But it is generally not true. The reason that they are so profitable is because they develop managed care techniques and have learned how to manage their business much better than what we knew as an industry 20 years ago. They have been able to reap nice profits.n There is a proposal to raise the foreign direct investment (FDI) cap in insurance to 49 per cent from the present 26 per cent. What are your views?Whether foreign partners should be allowed to own a majority or not is a local issue. But it seems to me that somehow the government and the regulator should create an environment that is encouraging for the right foreign companies to come in. Now, whether that is 49 per cent, 35 per cent or 55 per cent, I don’t know. But it should be a welcoming environment, an open environment, because a lot of foreign companies have knowledge, expertise that will help the health industry to grow.

Data Management in Insurance Industry

While data storage and management is a critical issue for IT professionals across industries, its severity gets accentuated for the insurance industry. Here's a brief on various techniques to control it.

Data is all pervasive � it begins much earlier than the initial stages of client understanding and diligence, and extends far beyond helping revenue generation, encompassing cross and up-selling products or services. It also helps to understand the business risks and verify whether the regulatory compliance needs are met. The insurance industry depends on promises made on paper, which are eventually converted into supporting databases and document repositories. This article elaborates on the types of data, modes of data acquisition, data checks and usage, and the prevalent techniques for data management.

Data sources

Insurance industry's data can broadly be classified as employee-related, distribution-related, customer-related, product-related, operations- related and accounting-related. Of these categories, employee-related data is required purely for internal workforce operations management and the rest have a direct impact on the cost and revenue of the insurance company.
All data is collected and stored in databases, data warehouses and as documents or images.

Data management stagesManagement of data could be defined in three major stages: Data Acquisition, Data Quality Management, and Data Exploitation or Data Utilization. Let us look at these in detail:
Data acquisition results from new business management, internal operations (HR, accounting, distribution and product & policy management systems). These are made available in unique respective data structures, in an integrated way. One step up, they can be consolidated into data warehouses and document management systems, jointly referred to as the universe of the insurance enterprise data.
Data exploitation could be done to cater to different needs like planning or analyzing growth of revenue, cost control, improving efficiency of operations, planning and executing business expansions, conceptualizing new products, and to provide data-related services to customers, distribution networks and employees.
Data Quality Management: Most of the big insurance enterprises have been operational for several decades and hence the data available with them may not be 100% accurate. Many such insurance enterprises still use green screens for systems support and policy administration. Data quality could be maintained and ensured, by continuously checking, correcting and preventing data errors, thereby making data ready for exploitation.
The link between data acquisition, data quality management and data utilization could be described in the ICO (Input-Check-Output) model.
Data management stagesManagement of data could be defined in three major stages: Data Acquisition, Data Quality Management, and Data Exploitation or Data Utilization. Let us look at these in detail:
Data acquisition results from new business management, internal operations (HR, accounting, distribution and product & policy management systems). These are made available in unique respective data structures, in an integrated way. One step up, they can be consolidated into data warehouses and document management systems, jointly referred to as the universe of the insurance enterprise data.
Data exploitation could be done to cater to different needs like planning or analyzing growth of revenue, cost control, improving efficiency of operations, planning and executing business expansions, conceptualizing new products, and to provide data-related services to customers, distribution networks and employees.
Data Quality Management: Most of the big insurance enterprises have been operational for several decades and hence the data available with them may not be 100% accurate. Many such insurance enterprises still use green screens for systems support and policy administration. Data quality could be maintained and ensured, by continuously checking, correcting and preventing data errors, thereby making data ready for exploitation.
The link between data acquisition, data quality management and data utilization could be described in the ICO (Input-Check-Output) model.
Data management stagesManagement of data could be defined in three major stages: Data Acquisition, Data Quality Management, and Data Exploitation or Data Utilization. Let us look at these in detail:
Data acquisition results from new business management, internal operations (HR, accounting, distribution and product & policy management systems). These are made available in unique respective data structures, in an integrated way. One step up, they can be consolidated into data warehouses and document management systems, jointly referred to as the universe of the insurance enterprise data.
Data exploitation could be done to cater to different needs like planning or analyzing growth of revenue, cost control, improving efficiency of operations, planning and executing business expansions, conceptualizing new products, and to provide data-related services to customers, distribution networks and employees.
Data Quality Management: Most of the big insurance enterprises have been operational for several decades and hence the data available with them may not be 100% accurate. Many such insurance enterprises still use green screens for systems support and policy administration. Data quality could be maintained and ensured, by continuously checking, correcting and preventing data errors, thereby making data ready for exploitation.
The link between data acquisition, data quality management and data utilization could be described in the ICO (Input-Check-Output) model.
Data Acquisition (Input)Structured data acquisition is critical to perform all subsequent data-related functions in an efficient and integrated manner. Data that is unstructured and not collected in databases is likely to create vacuums in data analysis. In today's insurance industry, data acquisition happens in five different broad segments:
Customer data: Customer relationship management, customer self service portals, new business management systems and other customer touch point systems are the sources for acquiring this data. It comprises of customer's personal data such as family, contact, activities, complaints, service requests, financial, health, campaign offers, policies, loans and benefits info. This group of data is generally administered in CRM, customer portals, and IVRS.
Distribution data: Distribution administration, sales & service management, compensation, compliance and other distribution touch point systems are the sources for acquiring this data. This group of data is generally administered in Distribution or Channel management systems, IVRS, FNA, quotation, applications and compliance management systems.
Policy administration data: New business, underwriting management, claims, accounting and actuarial data are the sources for acquiring this data. It comprises of financial needs analysis, quotes, new business applications, cashier entries, lock/collection boxes, accounting, valuation, loss ratios, document images, turn around time, underwriting, claims and policy services info. This group of data is generally administered in legacy policy administration, claims, accounting and actuarial systems; however, there could be number of separate systems for underwriting, policy services and new business support systems.
Product administration data: Product administration and pricing are the sources for acquiring this data. It comprises of product setup & management, profiling, pricing, profitability and product performance. A very few industries maintain market research data too. This group of data is generally administered in product management systems, actuarial systems, DWH and data marts.
Employee data: It comprises of employee personal details such as contacts, activities, payroll, education qualifications, certifications, credentials, job history, training and development info. This group of data is generally administered in HRMS; however, in some cases there may be separate payroll and training & development systems.
Missing, unstructured or disintegrated data acquired in any of the above five categories would create a gap in the data management chain and hence it is recommended to fill up these gaps diligently.

Data Quality Management (Check)Data acquired through various systems and databases needs to be checked for desired quality before being exploited. Data quality errors could result from inadequate verification of data stored in legacy systems, non-validated data leaks from the front end, inadequate integration, redundant data sources / stores, direct back-end updates, etc. In today's insurance Industry, data quality management is mostly ignored. Where implemented, it is done in one of the two ways described below.
Unstructured approachMost enterprises rely on a few batch programs to check some portions of the data acquired, and most of the times, these programs are triggered by a serious problem identified in customer or financial data. Some enterprises schedule these batch runs and some still pursue to run only on demand. Such intermittent and unorganized batch runs can neither help to scale or integrate, nor make an impressive improvement to the overall data quality of the enterprise.
Structured approachStructured data quality management, greatly contributes to scale up, integrate and thus create a big impact to the overall enterprise data quality. A structured data quality management model would pass through the following stages:
Extraction of data from source and/or target systems.
Run quality checks for identifying data transfer errors, data link/reference errors and domain integrity errors.
Create a data quality mart to keep all the error records and all error-related details, to help in tracking and monitoring the aging of the problem and to do other analyses.
Integrate the data quality errors into problem/incident trackers so that closures can be tracked.
Provide online data quality error reports to the data owners along with its aging so that they can be fixed by them.
The data volume, sensitivity/criticality and the data quality error exposure risks play a vital part in designing the right frequency to run, level of follow up and escalations settings, etc.
The data quality errors are critical to be fixed & prevented in time so that businesses can stop revenue/opportunity losses, cut additional recovery expenses and build confidence of all stakeholders in the value chain. (There would be a separate paper discussing in detail on the evaluation of the existing data quality management along with gaps to help insurance industries to implement a proper data quality management system.)
Data Exploitation (Output)Data acquired and checked thoroughly, is ready for exploitation. Data exploitation is the key stage which, if properly done, will help to reap the benefits of efficient data management. In other words, this is the value generation stage - which includes revenue growth, cost savings, operational efficiency gains, risk controls, etc, which are very critical for any business. This stage is also viewed as the information management stage. In Insurance industry today, the data exploitation which is the Output stage of the data management, is done in one of the two ways described below:
Legacy approachMost enterprises extract data or information required on an ad hoc basis from their operational systems and use their applications or batch programs to generate some reports to help in decision making. This method is not sustainable when the demand grows or multi-dimensional needs come up or when data becomes voluminous. Moreover, data users need to trail behind a big Q number which might render it too late to initiate desirable action on an issue for which data was originally extracted.
Structured approachWith the advantages of structured information management already reinforced in the last couple of paragraphs, an enterprise would be easily able to adapt to any volume or time challenges, thus creating a big impact to the overall information needs that are critical to the functioning and growth of the enterprise. Structured information management implementation can be done as laid down below:
Enterprise Data Ware House (EDWH): Most enterprise data, which is called universe, needs to be extracted, loaded and transformed for information needs, and then segmented for summaries and details.
Data Marts: Specific business functions (for example � accounting, compliance, etc) can have their data marts to address the key business problems in their functions.
Reporting Needs: Detail lists and structured (authored and custom) reports can be published from DWH, data marts and operational data stores.
Analysis Needs: Summaries need to be done with appropriate dimensions and measures to enable multi-dimensional analysis from DWH and data marts.
Information management should be viewed from the perspective of enterprise needs that would cover all functions of the enterprise that would minimally or majorly impact the business. All functions of the enterprise can be seamlessly integrated through suitable enterprise information management systems.
Frequency of refreshing the EDWH and data marts, extent of data integration, efficiency summaries depend on the business need or pace; hence, they need to be worked out during the design stage. The data needs to be exploited by creating data marts, reports and analysis to bring value to the enterprise.
ConclusionIt is recommended that Insurance industries do a stock check of their data management implementation at all three stages: data acquisition, data quality management and data exploitation. The value of data management should be clearly understood and structured approaches need to be adopted at all stages. With these implemented, an enterprise can make informed decisions, refrain from information starving, remain highly integrated and scalable, and most importantly, stay ahead of competition.

MOYNIHAN TO HEAD BANK OF AMERICA

Bank of America (B of A) Consumer and Small Business Banking President Brian T. Moynihan will replace Kenneth D. Lewis as Bank of America President and CEO when Lewis retires on December 31, 2009. Moynihan joined B of A as Global Wealth and Investment Management President in 2004, when B of A acquired FleetBoston Financial. Moynihan served in this position until 2007, when he was appointed president of B of A’s Global Corporate and Investment Management, a post he held until August 2009. B of A Chairman Walter Massey said, “Brian’s wide range of experience, his relationships inside and outside the company and his demonstrated ability to understand business dynamics and effect constructive change made him the best person for the position.” Moynihan said, “This company has a long tradition of operational excellence and strong execution. My goal is to refocus our efforts and attention to those core capabilities that will make us the best financial services firm in the world.” Moynihan is 50 years old and chairs B of A’s Global Diversity and Inclusion Council.

AUTO INSURERS SATISFY CUSTOMERS MORE THAN HOME INSURERS

Insured Americans are more satisfied with their auto insurance claims experience (842 on a 1,000-point scale) than they are with their homeowners claims experience (828 on a 1,000-point scale), according to J.D. Power & Associates. J.D. Power & Associates Senior Director of Insurance Practices Jeremy Bowler said, “By thoroughly explaining the limitations of the policy coverage and fully managing customer expectations, insurance companies may be able to lower the number of negotiations and improve claimant satisfaction considerably.” During the first notice of loss process, 81% of auto claimants said their insurer provided them an explanation of their policy coverage, while less than 75% of homeowner claimants received the same information.

FINANCIAL CRISIS EXPECTED TO HELP DRIVE TORT COSTS UP 3% IN 2009

Litigation associated with employment practices, medical malpractice and professional services errors and omissions associated with the financial crisis are expected to drive U.S. tort costs up 3% in 2009, up an additional 4% in 2010 and up an added 6% in 2011, according to a recent study completed by Stamford, CT-based Towers Perrin. Since 1950, annual growth in tort costs has exceeded gross domestic product (GDP) growth by an average of 2%, the 2009 Update on U.S. Tort Cost Trends reveals. In 2008, the U.S. tort system cost $254.7 billion or $838 per person, up from $252 billion, or $836 per person in 2007.

COLI, TOLI, NQDCs & SERPs

Eighty-five percent of U.S. companies offer nonqualified deferred compensation plans (NQDC), and 67% offer supplemental executive retirement plans (SERPs), according to Executive Benefits – A Survey of Current Trends, a study completed by Dallas, TX-based Clark Consulting, a subsidiary of The Hague-based AEGON. While the number of companies that informally fund their NQDC plans has climbed to 71%, up from 62% in 2007, the number informally funding their SERPs has dropped to 39%, down from 48% in 2007. Corporate-owned Life Insurance (COLI) and Trust-owned Life Insurance (TOLI) remain the most commonly used funding vehicles for both NQDCs (61%) and SERPs (68%). Increasingly, companies are turning to third party administrators or a combination of in-house/third party administrators to manage NQDC plans, leaving only 3% relying on in-house administrators, compared to 15% in 2007. In contrast, 32% administer their SERPs in house, up from 30% in 2007, but down from 44% in 2005. Turning to outside administrators, Clark Consulting said, “may reflect a need for more sophisticated administration in light of the requirements of the Internal Revenue Code section 409A.

U.S. PROPERTY-CASUALTY INSURERS SEE NET INCOME JUMP 268% IN FIRST NINE MONTHS

U.S. property-casualty insurers reported net income for the first nine months of 2009 jumped 268.2% to $16.2 billion, up from $4.4 billion in the first nine months of 2008, but tumbled 67.3% compared to $49.6 billion earned during the same period in 2007, according to Jersey City, NJ-based ISO and the Property Casualty Insurers Association of America (PCI). Net underwriting losses dropped to $3.2 billion, down from $19.8 billion in 2008, driven by a $26.5 billion drop in claims costs, and the combined ratio improved to 100.7% compared to 105.5% a year ago. Despite the negative 49.6% annualized return rate posted by mortgage and financial guaranty insurers, property-casualty insurers achieved an overall annualized return rate of 4.5%, up from a 1.2% annualized return rate a year ago, but almost half the average annualized return rate of 8.9% achieved during the past 24 years. Commenting on the industry’s performance, PCI President and CEO David Sampson said, “Property-casualty insurance failures remain extremely rare events, but, year-to-date through December 19, regulators had seized 140 banks. Insurers’ superior performance reflects the strength of their balance sheets. Combining insurers’ $490.8 billion in policyholders’ surplus at September 30, 2009, with their $552.4 billion in loss and loss adjustment expense reserves and their $204.7 billion in unearned premium reserves, insurers had more than $1.2 trillion in funds available to fulfill their promises to consumers.”