Client: Europe-based global financial services company
Industry: Financial Services
Key Products & Services: Wealth management, asset management, and investment banking services for private, corporate, and institutional clients
Area of Operations: Worldwide
The client was functioning on a data model that had only the network team onboarding the agent banks’ nostro accounts. The model did not involve the credit risk team, which is responsible for calculating capital requirements built on the internal ratings-based (IRB) approach. The client assessed the risk for each account, and held a minimum amount of capital as risk-weighted asset (RWA) to reduce the risk of insolvency.
The Finance team of the client issued a warning report to the management, stating that the RWA amount was about to breach the regulatory limit (20% for all OCED banks). The bank had used up a huge amount of capital as RWA as many of its nostro accounts had defaulted 100%.
The client approached Wipro to seek help in identifying the root cause of higher RWA.
Wipro focused on improving the RWA through static data remediation. Wipro’s Change team at the Bank formed a joint task force comprising of representatives from Finance, Credit Risk and Group Operations (Data Service Line, Network Management and Securities Settlements). The task force investigated the scenario to find the process gap in the existing data model. They found that the nostro accounts were being opened in the system with dummy client identifiers causing default RWA of 100% ( unmapped exposures).
Wipro’s timely analysis and resolution to the problem helped the bank bring down the RWA charge from 100% to respective risk percentage based on the internal ratings given by the Credit Risk team. This helped release $2.6 Billion in capital during 2016-17, which had previously been set aside to offset the higher level of risk.
“Wipro team reduced the RWA (over USD 2.6 Billion) requirement of the bank. This was enabled through a series of high impact project streams to remediate legacy account hierarchies by bringing together various divisions (Finance, Credit Risk, Network management, Settlements, Data service line) of the bank. A new data model was put in place to address the root cause.“
Sandeep Rajkumar, Account Head, Data Operations, Wipro Ltd