##ity risk show that indian banks are generally resilient. given the ongoing financial crisis and its likely impact on the indian economy, the cfsa conducted stress tests for the end of september 2008. under the worst - case scenario ( 150 per cent increase in gross npas ), the overall capital adequacy position of the banking sector would have declined to 10. 6 percent in september 2008 – still well - above the regulatory requirement of 9 per cent. thus, even under the worst case scenario, crar remains comfortably above the regulatory minimum. growth in bank credit remained strong up to october 2008 but has decelerated since. the financial system is working normally and accordingly there has been no need for any enhancement of government guarantee for bank deposits or banks ’ other liabilities. in view of the strong balance sheets and the transparency in the operations, there is no mistrust between banks and the inter - bank money market has been working throughout the period normally ( table 7 ). volumes in the money market have in fact grown over the past few months. there was some volatility in the call money rate, but this resulted from the sudden reversal in capital flows and resulting tightening of liquidity in september - october 2008. thus, the indian banking system is displaying none of the distresses that the western banking system has exhibited since the start of the sub - prime crisis. the resilience of the indian financial sector in the face of the worst global financial crisis since the 1930s can also be attributed to our approach to financial globalization. the key features of our approach have been reflected in a full, but gradual opening up of the current account but a more calibrated approach towards the opening up of the capital account and the financial sector. as far as the capital account is concerned, whereas foreign investment flows, especially direct investment inflows are encouraged, debt flows in the form of external commercial borrowings are generally subject to ceilings and some end - use restrictions. macro ceilings have also been stipulated for portfolio investment in government securities and corporate bonds. capital outflows have also been progressively liberalized. along with the calibrated approach to opening of the capital account, we have also practised prudential regulation, particularly of banks to manage financial instability. the financial sector, especially banks, is subject to prudential regulation, with respect to both liquidity and capital. a number of initiatives have been taken by the reserve bank over the past 5 - 6 years with a view to mitiga

industry : issues, remedies and impacts ” dealing with issues relating to data quality and has suggested a new framework towards achieving this ; this may prove beneficial for banks. bis central bankers ’ speeches 14. though one may be tempted to think that a term like data management is more apt for technologically agile sectors like it, the truth is that good data management practices can bring a huge benefit to the banking industry as well. further, the benefits arising out of this are not generic in nature, but rather deal with the core challenge today ’ s financial industry is facing – regulatory compliance. defining data quality 15. the broad components of data quality are precision, consistency, completeness, reliability and accessibility of standardised data across variety of platforms. but how do we define data quality? the meaning of the word “ quality ” depends on the context in which it is applied. we generally use it to indicate the superiority of a manufactured good or at least to a high degree of craftsmanship or artistry. however, it is difficult to define quality for data. unlike manufactured products, data does not have the physical characteristics that allow quality to be easily assessed. quality has therefore to be seen as a function of certain intangible properties such as “ completeness ” and “ consistency ”. 16. the question that logically follows is what is it that makes the quality of data a suspect? there could be variety of reasons such as duplication of data, maintenance of multiple data sets / standards, sluggish response and time to retrieve, polish and make it presentable, creation of redundancy within systems etc. the poor data quality always necessitates reconciliation, manual process of neutralisation or “ torture the data ” before it could be used. this low reliability, on account of poor quality of data often leads to time and cost over runs to reaffirm the reliability. the poor quality of data not only emanates from it but from business streams as well. this poor quality is further sustained by ambiguous roles and responsibilities in data management governance and oversight. this cost, both in monetary and time terms, impairs the business decision or the missed opportunity ceded to a better prepared competitor. 17. the basic issue as regards the computerisation of the indian banking system was that we migrated from the branch banking software to a core banking solution in which every activity / transaction processing is centralised. at the time of migration, since a variety of platforms were used for capturing different business activities,