Remove Finance Remove Modeling Remove Risk Management
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Risk Management for AI Chatbots

O'Reilly on Data

Doing so means giving the general public a freeform text box for interacting with your AI model. Welcome to your company’s new AI risk management nightmare. ” ) With a chatbot, the web form passes an end-user’s freeform text input—a “prompt,” or a request to act—to a generative AI model.

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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. Right now, Big Data tools are continuously being incorporated in the finance and banking sector. Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations.

Big Data 145
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The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

An exploration of three types of errors inherent in all financial models. At Hedged Capital , an AI-first financial trading and advisory firm, we use probabilistic models to trade the financial markets. Finance is not physics. Perhaps finance is harder than physics. All financial models are wrong.

Modeling 207
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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.). Image by Ben Lorica.

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Cloud analytics migration: how to exceed expectations

CIO Business Intelligence

Robust cloud cost management tools and practices that foster collaboration between IT, finance, and business units can help ensure alignment and effective optimization of cloud investments,” notes Morris. Their collaboration enables real-time delivery of insights for risk management, fraud detection, and customer personalization.

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The Role of Model Governance in Machine Learning and Artificial Intelligence

Domino Data Lab

All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. As such, model governance needs to be applied to each model for as long as it’s being used.

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Global AI regulations: Beyond the U.S. and Europe

CIO Business Intelligence

Importantly, where the EU AI Act identifies different risk levels, the PRC AI Law identifies eight specific scenarios and industries where a higher level of risk management is required for “critical AI.” The UAE provides a similar model to China, although less prescriptive regarding national security.