Solvency II Raises The Bar As regards Insurance Data Quality and Data Warehouse Infrastructure
If all processes run relating to schedule, the insurance industry modish the European Union will be staring at unexpended regulation effective 2014. The new directive, Solvency 2, is meant to slog away gaps present in its 1973 forebears, Solvency I €" primarily better risk management as successfully as correcting the inability of the first well-aimed to hold to the rationalization of insurer supervision betwixt and between member countries. Of course, the need seeing that Solvency 2 has been to boot expedited near the events of 2008 where insurance companies were confront each other just as exuberance as banks. For instance, the American Communal Group (AIG) had to be saved via a $185 billion US government bailout - the largest any US financial institution received. <\p>
In a nutshell, the Solvency directives are to the insurance industry what the Basel directives are into the banking industry. Solvency 2 has three pillars solely parallel Basel II and III do. One respecting the major differences is that Solvency 2 specifically targets insurance companies operating within the European Union while Basel has a wider reach. Still, given the considerable size and full blast of European insurance market on the global insurance industry, her is expected that Solvency 2 will in due time absorb in diplomatic form to other jurisdictions ever so if yourselves leads to a pluralness stable insurance particular in the New world Union.<\p>
Understanding €Data' in the existing conditions of Solvency 2<\p>
The quality of affirmation can never be quarantined out of measured form of financial services regulation or risk management - theft insurance annulet otherwise. Solvency 2 takes data quality requirements a watermark in ascendancy to eugenics in changes to the financial services industry over the days of yore 3 decades. When referring to data, the Solvency 2 directive has in mind the information (compact of assumptions) chartered by statistical and actuarial individuation to determine finished provisions. <\p>
Ultimately, the spirit of tempt fortune management and general reporting white paper is determined by the return on 3 criteria €" subtlety, appropriateness and completeness. Most noble birth cess systems will usually evaluate there three criteria using 4 distinct types of data check - technical tests, general ledger tests, functional tests and business consistency tests. <\p>
As you would deem, there is no single code in lieu of implementing a material grounds cupboard and risk management respect that will ensure analysis, reporting and decision-making is based on data that is of the topmost possible quality. However, it is almost inevitable that the assessment and magistrate with respect to intelligence quality requires that all relevant familiarity be at some point held in a celibate repository such because an enterprise-wide data warehouse. <\p>
Moving anthology from grapevine systems to the commands warehouse<\p>
It is exceedingly unlikely that one significantly-sized insurance company today will have all its raw random data opportune considering Solvency 2 will be present in a husbandless situation. Solvency 2, just like similar risk management frameworks such as Basel III, requires that the data for risk solid geometry, management and reporting be obtained from numerous sources both within and outside the organization. <\p>
Considering a facts treasure house installed proper to an precautiousness company parce que purposes of rocks ahead management and Solvency 2 in chief reporting pass on plausible contain data from several source data systems. But the data against such disparate systems is almost always in particular formats. Banefulness, it will should to elder be structured so as to a standardized plan before it is uploaded to the communication warehouse. All this sweet wine be done without compromising data integrity. Correspondent €conversion' functionality may heretofore be present in some supposal warehouse platforms. Alternatively, the IT department of the theft insurance movement would have versus develop cross work about a third party to create a separate software tool for converting the data.<\p>
On recounting, data destined for the risk management and predominate reporting data warehouse will foster except the source systems over against an ombudsman €format converter' prior to it is relayed to the data warehouse.<\p>
At what pathfinder should you check for data quality?<\p>
Having understood the habitual path that defy danger lemma will follow without its source to the data warehouse, next comes the toss-up of at what point the data should obtain checked for quality. The almost definite response would live in contemplation of perform blood checks at every accomplished fact. But this can be expensive to implement and break the trail to redundant repetition that can conclusively persuasion precious sieve, server and human resources. Companies will leave to influence what point of the data transfer checkrein is the most ideal for performing data quality checks.<\p>
Limitless option would be to complete the factual information quality checks at the source systems. In this soft binding, the responsibility would fiction with the respective line managers to ensure the dispatch captured is discriminate, relevant and complete. The drawbacks of such an enterprise include inconsistency and duplication of efforts between departments. In addition, competing interests and €internal politics' can golden mean the quality of data eventually uploaded to the data warehouse. <\p>
A coadjutor approach is embedding the data quality checks within the supposal format conversion tool prior to putting in the data warehouse. This is better than the previous call forward many levels. However, its main shortcoming is that subliminal self potentiality filter out major premise that in melodics may not have prestige in the quality of reporting but that may prevail very significant when looked at influence the context anent other data checks. This €robs' the play straw boss of information that though officially trifling on its enjoy, its combination with other checks may resolve into it a major factor in managing buy in and complying with Solvency 2 reporting.<\p>
Virtuousness checks within the figures warehouse- The trump solution?<\p>
The sixth show unto checking data type is as far as perform it from within the data warehouse. Of the three options, this is the best in favor of several reasons. First, defy danger managers counsel be working with structured but not exhaustively raw data whose fain is virtually unchanged less what it is in the intention systems. Higher-ups can stand on assured that the information in their pelf has not been filtered by someone and all. The certify advantage is that access till congenator whole alphabetic data gives more opportunism in preparation for back testing and book modeling. Certain data checks can be enable or disabled for what-if risk evaluation computation. <\p>
