Capital market participants need solutions that provide timely, accurate and consistent information to support real-time decision-making with regard to risk and capital flows. It is vital for organizations to move towards an integrated, holistic and analytically rigorous model of risk and capital management, built on a unified support infrastructure.
Technology solutions in the Risk and Regulatory Compliance area need to offer the following advantages:
Continually updated and upgraded systems that are current and in step with changes in the market and business environment as well as multi-jurisdictional regulatory frameworksEnable tracking of changes to capital restrictions and governance and holding structuresManage regulatory-driven moves towards risk measurement standardization, particularly at an enterprise-wide level and in regulatory capitalAllow management of interdependencies at the client level to ensure relationships are not disruptedIntegration to include risk appetite considerations, capital planning/budgeting and adequacy assessments, resolution planning, stress testing and liquidity risk managementEnable model and analytics improvement for individual risk types
To support our clients in real-time business and strategic decision-making, Iris offers a complete techno-functional services portfolio (including business analysis, application development, advanced analytics, data and testing, etc) in credit/counterparty, market and operational risk areas.
Iris also works with increasingly sophisticated models and data analytics infrastructure to manage big data for risk management purposes, including traditional data sources such as internal bank data and market reference data.
Iris has built up years of experience with solutions that cover the intersection of risk, finance, technology and data. We are able to provide clients support across all areas of the systems development life cycle to develop holistic and rigorous technology solutions. Our solutions help create better transparency and flow of information even in ecosystems with potentially fragmented data/systems, inconsistent models and control mechanisms.