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Optimizing Risk and Exposure Management – Roundtable Highlights

Cloudera

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. At Cloudera, we believe in the untapped opportunity presented by data and AI, too.

Risk 100
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Transforming FSI in ASEAN with Cloud Analytics

CIO Business Intelligence

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. Particularly in Asia Pacific , revenues for big data and analytics solutions providers hit US$22.6bn in 2020 , with financial services companies ranking among their biggest clients.

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How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

Cloudera

With AI, financial institutions and insurance companies now have the ability to automate or augment complex decision-making processes, deliver highly personalized client experiences, create individualized customer education materials, and match the appropriate financial and investment products to each customer’s needs.

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7 Advantages of Using Encryption Technology for Data Protection

Smart Data Collective

million penalty for violating the Health Insurance Portability and Accountability Act, more commonly known as HIPAA. However, according to a 2018 North American report published by Shred-It, the majority of business leaders believe data breach risks are higher when people work remotely.

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Machine Learning Project Checklist

DataRobot Blog

Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. For a credit risk model, the target could be defined as “fully repays loan” or “payments in first 2 years are current” or or “collateral is repossessed.”. Define project scope.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

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The art and science of data product portfolio management

AWS Big Data

At the same time, unstructured approaches to data mesh management that don’t have a vision for what types of products should exist and how to ensure they are developed are at high risk of creating the same effect through simple neglect. When do new data products get created, and who is allowed to create them?