The Challenge

  • Maintaining volumes of data and keeping levels of consistency between financial risk(credit, pricing, market, capital & liquidity risk) and operational risk is ever harder than before with the increase in regulatory requirements. Hence, increasing the 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 and increasing risk. This happens every day!
  • Risk management functions are not integrated and connected, creating a challenge in decision-making across the organization.

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 internal requirements, regulations and operational risk providing visibility in an analytics dashboards and reporting the board, senior executives and functional managers to make timely and sound 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

  • Integrated operational and financial risk management will enable growth, reduce fines and enable you to make less mistakes resulting in higher margins.
  • Better compliance risk monitoring with greater automation, fewer manual processes, full population monitoring and increased flexibility to adapt to new and changing regulatory requirements.

A Case Study

Challenge:

  • One of the largest lending institution had disjointed and disparate systems. A separate document management system, a standalone enterprise content management system, used archer for action and task management, used excel for risk management, but planning to use archer’s 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 lending institution 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, increased employee dis-satisfaction, increased defect metrics and could impact brand and reputation.

Solution:

  • The solution designed for this lending institution 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.