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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. Before you give up on your dreams of releasing an AI chatbot, remember: no risk, no reward. That doesn’t sound so bad, right? So, what do you do?

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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 7: The Impact of COVID-19 on Financial Services & Risk Management

bridgei2i

Episode 7: The Impact of COVID-19 on Financial Services & Risk. Management. The Impact of COVID-19 on Financial Services & Risk Management. One, more customer facing – So customer needs to see a seamless digital experience across his/her journey and interaction with the bank.

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Future-Proofing Your Business with Hyperautomation

CIO Business Intelligence

They enable greater efficiency and accuracy and error reduction, better decision making, better compliance and risk management, process optimisation and greater agility. It requires careful analysis of all processes, and in many cases changes to how individual process operate and interact.

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Generative AI use cases for the enterprise

IBM Big Data Hub

The compact design and touch-based interactivity seemed like a leap into the future. For example, organizations can use generative AI to: Quickly turn mountains of unstructured text into specific and usable document summaries, paving the way for more informed decision-making. Automate tedious, repetitive tasks.

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Examples of IBM assisting insurance companies in implementing generative AI-based solutions  

IBM Big Data Hub

This approach can accelerate speed-to-market by providing enhanced capabilities for developing innovative products and services, facilitating business growth and improving the overall customer experience in their interactions with the company. Customer engagement Providing insurance coverage involves working with numerous documents.

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Integrating Data Governance and Enterprise Architecture

erwin

It documents your data assets from end to end for business understanding and clear data lineage with traceability. Data governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. They are used in everything from robotics to tools that reason and interact with humans.

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