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Middle East tech leaders explore AI’s role in modern risk management

CIO Business Intelligence

In today’s fast-paced digital environment, enterprises increasingly leverage AI and analytics to strengthen their risk management strategies. By adopting AI-driven approaches, businesses can better anticipate potential threats, make data-informed decisions, and bolster the security of their assets and operations.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

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Data Scalability Raises Considerable Risk Management Concerns

Smart Data Collective

As such, you should concentrate your efforts in positioning your organization to mine the data and use it for predictive analytics and proper planning. The Relationship between Big Data and Risk Management. Tips for Improving Risk Management When Handling Big Data. Vendor Risk Management (VRM).

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

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CDOs and CDAOs: Rethink your role or fade away

CIO Business Intelligence

Out of the back office The first wave of CDOs and CDAOs focused on back-office tasks such as data governance, data quality, and data management, but people in the positions now need to become more visible by showing how they bring value to the business, Duncan says.

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What is BCBS 239 Compliance?

Octopai

BCBS 239 is a document published by that committee entitled, Principles for Effective Risk Data Aggregation and Risk Reporting. The document, first published in 2013, outlines best practices for global and domestic banks to identify, manage, and report risks, including credit, market, liquidity, and operational risks.

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Advance top 2025 data initiatives with analyst firm-recognized erwin by Quest

erwin

Data intelligence software is continuously evolving to enable organizations to efficiently and effectively advance new data initiatives. With a variety of providers and offerings addressing data intelligence and governance needs, it can be easy to feel overwhelmed in selecting the right solution for your enterprise.