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How to Manage Risk with Modern Data Architectures

Cloudera

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

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How A Data Catalog Enhances Data Risk Management

Alation

Alation joined with Ortecha , a data management consultancy, to publish a white paper providing insights and guidance to stakeholders and decision-makers charged with implementing or modernising data risk management functions. The Increasing Focus On Data Risk Management.

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Back to the Financial Regulatory Future

Cloudera

From stringent data protection measures to complex risk management protocols, institutions must not only adapt to regulatory shifts but also proactively anticipate emerging requirements, as well as predict negative outcomes.

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Discovering Data Monetization Opportunities in Financial Services

Cloudera

In order to monetize their data while still respecting the privacy of their customers, these firms must implement robust data protection measures and adhere to relevant regulations.

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

Amazon Redshift features like streaming ingestion, Amazon Aurora zero-ETL integration , and data sharing with AWS Data Exchange enable near-real-time processing for trade reporting, risk management, and trade optimization. Ruben Falk is a Capital Markets Specialist focused on AI and data & analytics.

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BI Data Lineage Solutions: Your Trusted Guide For Success

Octopai

It required banks to develop a data architecture that could support risk-management tools. Not only did the banks need to implement these risk-measurement systems (which depend on metrics arriving from distinct data dictionary tools), they also needed to produce reports documenting their use.

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4 Steps to Data-first Modernization

CIO Business Intelligence

From a policy perspective, the organization needs to mature beyond a basic awareness and definition of data compliance requirements (which typically holds that local operations make data “sovereign” by default) to a more refined, data-first model that incorporates corporate risk management, regulatory and reporting issues, and compliance frameworks.