Managing Operational Risk in Data Frameworks

Sanjay Sharma and Pankaj Singal

Harmonised data structures are the backbone of financial institutions’ information and analytics systems. Lack of robust data taxonomies and frameworks across financial institutions’ front-, middle- and back-office functions lead to competitive vulnerability and operational risks. Rather than providing an informational edge, data frameworks become a cost burden and hinder sound business decisions and effective risk management. They slow down the dynamism that financial institutions need in a business environment that is strife with macroeconomic uncertainty, increasingly rigorous regulatory standards and competition from non-bank challengers. This dynamic is compounded in institutions that have broad geographic footprints, and complex and diverse product sets that require sophisticated financial models and computing capacity to generate risk and other parameters.

These factors came to the fore during the 2008 global financial crisis, during which several institutions found their risk and finance computation and aggregation frameworks seriously lacking. Subsequent to the crisis, regulators and institutions alike have put strong emphasis on the importance of harmonised data