Big Data - The Answer to Financial "Systemic" Risks
The Financial Institution’s Challenge
After the financial crisis of 2008, several reforms were put into place to reduce the “systemic risk” a bank poses to the global financial system. For the bank's’ business lines, regulators, and participants, the goal was unanimous - never again could a bank be allowed to become “too big to fail”.
Guiding policies and procedures were introduced to measure the exposure to possible losses, after which remediating action would be taken against those risks, or the cash and/or collateral kept in reserve would be increased. However, measuring a bank’s exposure and reconciling information across the data silos within the organization was a daunting challenge.
Our client’s derivatives business faced challenges ranging from adapting to the complexity and the high cost associated with their trillion-dollar business.
The Clovity Solution
Clovity leveraged it's Studios’ deep understanding of IoT, Mobility, AI-based decision making, Core Banking, and CRM to roll-out a system that automated tracking, push notifications, and real-time changing of credit limit.
Clovity’s engagement began with defining the design, and graduated to building and maintaining the data architecture that fully supported the financial institution’s risk data aggregation capabilities and risk reporting practices, not only in normal times but also during a crisis.
Clovity built and deployed a new operational data store based SQL database with XML support and integrated trade data without ETL delays. We also streamlined the workflow processes, while optimizing the existing architecture.
The new platform allowed large user groups to perform structured and one-off queries over data. The system integrated with multiple applications and made it easier to accommodate newer changes to the structure of trades without downtime or schema changes. Furthermore, it ensured a streamlined end-to-end trade settlement process. The end result was a successful and growing deployment that powered more trades than before and facilitated updates in data in nanoseconds.