Insurance is necessary for protection against future financial loss from injury, illness, property damage, or liability for the losses of others. Insurance companies receive proposals for insurance coverage. They evaluate the potential risk, and accept, reject, or modify the policy according to law or company standards.
Before deciding whether to accept or reject a policy, Underwriters investigate the factors affecting the probability of loss. They analyze Information on the policy application and collect additional data.
This additional data can be in the form of physical condition of the property, protective devices and safety procedures used, and reports of safety inspections made by company loss control technicians or by independent inspection services. Underwriters also study financial statements to estimate the financial value of the insurable assets.
Most of the standard insurance products available in our country for covering material damage to assets of individual or business, business interruptions losses or export credits require certain accurate financial data. Industrial All Risk policies require the fixed assets schedule pertaining to that plant.
Consequential Loss policies require the annual turnover, variable and standing charges, the gross profit etc. Corporate with huge balance sheets like manufacturing concerns, oil companies and airlines require various kinds of insurance covers like those mentioned above. Several energy and power majors opt for mega risk policies.
It is the general practice to collect such data from the proposer on a signed proposal form. Though it may seem easy but collection of right data is the most difficult part. There are complications involved.
First and the most critical is the competition. With so many companies vying for the same share of pie, insurers are left with very little time to reach the client, make a pitch and collecting all the required data as per their choice and comfort. Clients look for a quick solution to their problems. They need the best quotes with lowest premiums in least possible time without getting into the hassle of diligently sharing the information with insurers.
Accuracy of information is the other issue. Understatement of asset values is common practice as it leads to cheaper policies. It makes a significant savings in case of risks with large asset values.
However in the eventuality of a claim, the average clause is invoked by the insurers and claim payments are reduced by proportionate amounts. It leads to no win situation for both the parties as the insurer didn’t get the adequate premium and insured was not adequately compensated for the loss.
In the current situation, underwriters have started referring the financial statements of their clients. The advantages are manifold. It helps to identify potential clients. Insurance firms also analyze financial statements of clients to identify insurable risks and cross-sell various policies.
Financial statements also disclose the values of the insurable asset possessed by a client. Thereby such disclosures form a base for reference and verification by the underwriters. But such reports are scattered in print or web media. Gathering the data in a standardized and predictable form is a challenge.
So using XBRL is the way forward……..
What is XBRL?
XBRL is a language for the electronic communication of business and financial data which is set to revolutionize business reporting around the world.
It helps in the preparation, analysis and communication of business information. It saves costs, provides greater efficiency and improved accuracy and reliability to all those involved in supplying or using financial data.
XBRL has been developed by an international non-profit consortium of approximately 140 major companies, organisations and government agencies. It is an open royalty free software application. It is already being put to practical use in a number of countries and implementations of XBRL are growing rapidly around the world.
XBRL stands for eXtensible Business Reporting Language. It is one of a family of “XML” languages which is becoming a standard means of communicating information between businesses and on the internet.
The idea behind XBRL is simple. Instead of treating financial information as a block of text – as in a standard internet page or a printed document – it provides an identifying tag for each individual item of data. This is computer readable. For example, company net profit or P/E ratio has its own unique tag.
The introduction of XBRL tags enables automated processing of business information by computer software, cutting out laborious and costly processes of manual re-entry and comparison.
Computers can treat XBRL data “intelligently”: they can recognise the information in a XBRL document, select it, analyse it, store it, exchange it with other computers and present it automatically in a variety of ways for users.
XBRL can handle data in different languages and accounting standards. It can flexibly be adapted to meet different requirements and uses. Data can be transformed into XBRL by suitable mapping tools or it can be generated in XBRL by appropriate software.
Insurance underwriters with access to XBRL software can download financial statements in a standardized format.
What is there for Underwriters?
XBRL also has certain distinct advantages for insurance underwriters.
The XBRL Risk Taxonomy has the potential to reshape the dynamics of financial regulation.
Every regulated financial institution would provide loss event and tail data to regulatory authorities in XBRL via the Risk Taxonomy. As time flows , more and more organizations will report financials on a regular basis, the data repository would grow large quickly, but meaningful trend data would still take at minimum a few years to accumulate. A time will come when this data repository will give the regulatory authorities a feel of the risks involved in the financial systems.
It will also enable financial institutions to compare their own loss reporting to industry aggregates to improve trending and forecasting. If this loss trending information is linked to management decision cycles then every decision point can be compared to past experiences and future forecasts. This will enlighten human decision-making with risk probability information at the point of action, record decisions and results, and constantly learn from mistakes and improve over time.
Specifically for finance managers who are responsible for insuring the risks involved in their respective businesses, this kind of a loss analysis will give them better insights. Even XBRL tool can be used by banks and other independent agencies for credit analysis and rating.
The IRDA (Insurance Regulatory and Development Authority) performs this kind of loss data aggregation for a variety of insurance lines and many insurance companies procure this to calculate insurance premiums and reserves. Even in banking, with several years of loss data accumulated it should be possible to create an open insurance exchange to underwrite the losses with insurance coverage thereby mitigating the credit risk.
This would allow banks to transfer operational risks (which are most similar to professional liability exposures) off their balance sheets to insurance vehicles. The banks would pay a premium for the coverage, the market would price risk on a near-real time basis, and regulators like the RBI and IRDA could govern premiums and fair trade mechanisms.
In some ways this would function like credit default swaps but the trades would be on an open insurance market, and rising risk in financial institutions would result in higher premiums, which in turn could be correlated with equity and bond markets to create additional incentive and penalty mechanisms for risk management.
For any company Risk self-insurance is inherently inefficient capital allocation without deep loss history. In the insurance market self-insurance is most practical when commercial coverage is unavailable or too expensive. In the banking world, banks have self-insured their own losses for decades without empirical risk measurement programs.
A more pragmatic model would price future risks based on past losses and make companies pay premiums for loss producing behaviour. The XBRL risk taxonomy can create a data model to facilitate loss history aggregation that can create enough data for accurate underwriting. And when that information can be placed on an open market, companies would have a financial incentive to report losses – because the market would transfer the losses to insurance coverage and companies would have more capital for investments. At a time when most companies are short of capital, this model can be beneficial for every one – market mechanisms, regulatory reform, and better capital allocation.
Accurate valuation of risk is also a critical requirement for insurance underwriting. The value of risk is to be determined based on two important aspects i.e.
1. By verification of assets physically.
2. To know the value of asset through financial statements where the actual value is mentioned.
As per Indian accounting system, the value of assets mentioned in the financial statements is the book value or the market value whichever is less.
At present, most of the organizations are switching over to International financial Reporting Standards (IFRS) which has now been accepted by more than 100 countries as the common business accounting standard.
This has provided convenience to Global Investors who access the financial health of a company before making investments.
In IFRS, the value of assets is determined on the basis of Fair Market value which gives a correct picture to insurance underwriters for arriving at the insurable value of a property. Through XBRL, insurance companies can access historical data of various assets without studying the year wise financial statements.
The information provided through XBRL is authentic information which has been provided the company to the various statutory authorities like Registrar of Companies or income Tax/ Central Excise Authorities. This data will help insurance companies to avoid loss of premium due to under insurance. It will also help in accurate assessment of claim liabilities leading to indemnification only to the extent of loss incurred.
