The Challenge
- Mapping and creating relationships between volumes of financial risk (credit, market, liquidity) and operational risk data at the asset level is ever harder than before with increased level of sheer complexity.
- Decisions are made by financial risk function and operational risk function in silos using different platforms exposing the company to higher fines, increased risk and regulatory scrutiny.
The Solution
- An Enterprise Risk Management platform that leverages artificial intelligence which can take financial risk (credit, pricing, market, capital & liquidity risk) data feeds and generate concepts that can then be mapped to operational risk data providing visibility into where risks intersects and compounds so that the executives and functional managers can make timely and intelligent decisions.
- Most of the CRO’s are not even familiar if such a technology exists. Yes, it does and it can be Nirvana for CRO’s.
Value
- Automating the relationships between operational and financial risks will enable growth, reduce fines and better compliance risk monitoring.
- Technology and automation can be used to enhance risk management processes and enable the organization to go beyond compliance and enable growth- As the industry is transforming banks need to innovate and implement an integrated risk management capability.
A Case Study
Challenge:
- A $30B plus bank had disjointed and disparate systems. A standalone policy and procedure management system, a standalone enterprise content management system, used archer for action and task management, used excel for risk management, but planning to use metrics stream platform for risk assessment. There was no regulatory management system so there was no way to find out which internal controls are mapped to which regulations. There was no process to map customer and financial data to operational risk data to give better insight.
- Further, this bank had poor data architecture and management which not only prevented them from predicting risk and proactively addressing issues but rather put them to be reactive, forcing everyone to be in fire-fighting mode. The end result of this is chaos, increasing employee dis-satisfaction, increased defect metrics and could impact brand and reputation.
Solution:
- The solution designed for this bank was to have an integrated enterprise risk management system which included a regulatory change management system that was mapped to policies and procedures, content management system and internal controls. Policies and procedures were concept mapped with the regulations so that whenever regulations or polices changed, a user base would get alerts and tasks assigned to them using artificial intelligence technology. Operational risk assessment and action plans were also part of the integrated system.
- The system was configured to read financial and customer risk data either directly through a feed or through a common input interface where no feed is available. This data will then be processed via a rules engine and mapped to operational risk data. This data was exposed through the use of analytics, reports and real-time dashboards to provide better insight to make solid business decisions at the asset and customer level.
