Remove Finance Remove Risk Remove Testing
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Risk Management for AI Chatbots

O'Reilly on Data

Welcome to your company’s new AI risk management nightmare. Before you give up on your dreams of releasing an AI chatbot, remember: no risk, no reward. The core idea of risk management is that you don’t win by saying “no” to everything. Why not take the extra time to test for problems?

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How Big Data Impacts The Finance And Banking Industries

Smart Data Collective

Typically, this approach is essential, especially for the banking and finance sector in today’s world. Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. Big Data provides financial and banking organizations with better risk coverage.

Big Data 145
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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

Should we risk loss of control of our civilization?” If every company had a different way of reporting its finances, it would be impossible to regulate them. And they are stress testing and “ red teaming ” them to uncover vulnerabilities. Disclosures should not be limited to the quarterly and annual reports required in finance.

Metrics 357
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Accelerating AI for financial services: Innovation at scale with NVIDIA and Microsoft

CIO Business Intelligence

Financial institutions have an unprecedented opportunity to leverage AI/GenAI to expand services, drive massive productivity gains, mitigate risks, and reduce costs. GenAI is also helping to improve risk assessment via predictive analytics.

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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

This post explores how Iceberg can enhance quant research platforms by improving query performance, reducing costs, and increasing productivity, ultimately enabling faster and more efficient strategy development in quantitative finance. Also, the time travel feature can further mitigate any risks of lookahead bias.

Metadata 107
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Structural Evolutions in Data

O'Reilly on Data

” Web3 has similarly progressed through “basic blockchain and cryptocurrency tokens” to “decentralized finance” to “NFTs as loyalty cards.” You can see a simulation as a temporary, synthetic environment in which to test an idea. “Here’s our risk model.

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

O'Reilly on Data

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.