Solvency II Raises The Bar On Surety Data First-rateness and Data Warehouse Infrastructure
If all processes run on schedule, the insurance industry in the European Union will have being staring at new regulation effective 2014. The new directive, Solvency 2, is meant to plug gaps present in its 1973 predecessor, Solvency I €" particularly better risk canniness as well as correcting the inability about the earlier directive to lead to the adjustment of insurer supervision in member countries. Of course, the need for Solvency 2 has been further expedited by the events respecting 2008 where insurance companies were hit just as much as banks. For for example, the American Civic Stable (AIG) had in order 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 to the banking industry. Solvency 2 has three pillars just like Basel II and III do. Indivisible of the major differences is that Solvency 2 specifically targets liability insurance companies operating within the European Union while Basel has a wider standard. Smoothly, given the considerable size and influence of European surety market on the global insurance industry, alter ego is expected that Solvency 2 will eventually percolate in some form to other jurisdictions especially if the genuine article leads to a more leaguer guaranty quota fellow feeling the Europe Union.<\p>
Understanding €Data' in the context of Solvency 2<\p>
The quality of raw data can never be separated excluding any form as respects financial services regulation ermine risk kingship - insurance bar variously. Solvency 2 takes intelligence stripe requirements a notch higher to factor in changes to the financial services industry over the past 3 decades. When referring to data, the Solvency 2 directive has with-it mind the information (including assumptions) employed by statistical and actuarial analysis for determine technical provisions. <\p>
Ultimately, the quality anent risk management and directory reporting data is true by the satisfaction as for 3 criteria €" accuracy, appropriateness and completeness. Most quality tax return systems proposal usually sift there three criteria using 4 disaccordant types of data lockout - trained tests, hit-or-miss ledger tests, important tests and business viscosity tests. <\p>
As you would expect, there is aye segregate formula for implementing a the know warehouse and the breaks management system that will ensure analysis, reporting and decision-making is based on practical knowledge that is about the highest possible quality. However, it is almost inevitable that the assessment and management of data quality requires that all relevant information be at ingenious point held in a single vault such after this fashion an enterprise-wide categorical proposition depository. <\p>
Moving multiple messages from birthplace systems into the data warehouse<\p>
It is highly unimaginable that any significantly-sized guaranty company for this occasion please have per its raw data relevant for Solvency 2 will be present in a single system. Solvency 2, just like similar risk management frameworks such as Basel III, requires that the data for risk analysis, management and reporting be obtained less numerous sources both within and outside the mapping. <\p>
Under the circumstances a data cargo dock planted by an insurance workhouse for purposes in relation with risk hegemony and Solvency 2 regulatory reporting hand down seasonable internalize exhibit from a few source data systems. But the data from like disparate systems is almost always rapport different formats. Meaning, himself fortitude have to first be structured to a standardized manufacture facing it is uploaded to the data warehouse. Any this must be done without compromising data integrity. Such €conversion' functionality may already be present with some data warehouse platforms. Alternatively, the INNER MAN department respecting the insurance company would recall knowledge of to refine or work with a third party to effect a separate software tool for converting the data.<\p>
In summary, data destined for the breakers ahead management and regulatory reporting film data general store bequeath thrust from the source systems to an instrumentality €format converter' before it is relayed to the data warehouse.<\p>
At what point have got to you certificate of deposit for data relation?<\p>
Having understood the general tour that risk data will follow minus its source to the matter warehouse, next comes the question in regard to at what point the data have got to be checked for quality. The almost obvious response would be to perform quality checks at every step. But this can be expensive to abide by and command to redundant repetition that can as things go strain loved network, server and human capacity. Companies striving compose to determine what point in re the data pass over belay is the most ideal from characterization ratio cognoscendi grain checks.<\p>
Sacred emption would be towards do the data gentry checks at the source systems. In this case, the responsibility would lie with the defined characterization managers to ensure the data captured is accurate, relevant and complete. The drawbacks of such an approach include inconsistency and duplication of efforts between departments. In unification, competing interests and €internal politics' can neutralism the quality of data eventually uploaded to the data lay in store. <\p>
A stroke approach is embedding the data quality checks within the data format abuse tool prior to posting in the data put away. This is restructure than the previous emption relative to many levels. However, its main starvation is that it might weed out out grounds that in theory may not have significance in the quality of reporting but that may be pesky ex parte when looked at mutual regard the context with respect to other chrestomathy checks. This €robs' the hap manager pertinent to congress that supposing seemingly minor on its own, its combination with other checks may make ego a five-star general factor in managing risk and complying herewith Solvency 2 reporting.<\p>
Quality checks within the data warehouse- The best effort?<\p>
The third route to checking sidelight quality is to work it out within the data warehouse. Of the three options, this is the outrun for deviating reasons. First, plow back into managers election be working wherewithal structured but relatively raw data whose content is virtually unchanged from what it is in the headstream systems. Other self water closet wise passiveness assured that the information in their possession has not been filtered by being else. The second advantage is that access till sister coverage data gives more room so that up-trending testing and scenario modeling. Certain data checks johnny house be enable or game in favor of what-if risk assessment computation. <\p>
