AI Articles and Analysis about Risk management

Risk management is the identification, assessment, and prioritization of risks (defined in ISO 31000 as the effect of uncertainty on objectives) followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events or to maximize the realization of opportunities.", "uri": "https://en.wikipedia.org/wiki/Risk_management

Driving Partnerships and Prioritization Strategies for AI in Financial Services-1-min
Case management
Finance
Claim management
Financial services
Compliance
Data auditing
Data collection
Data integration
Data management
Enterprise resource planning
Investment
Risk management

Driving Partnerships and Prioritization Strategies for AI in Financial Services – with Julie Winkler of CME Group

Where new, emerging capabilities in AI mean new opportunities for financial institutions – especially those that operate financial exchanges, where data and predictive analytics are paramount – the palpable risk of failure in AI adoption initiatives can prove a significant hurdle. 
AI Opportunities for
Life Sciences R&D-1
"Clinical support
Healthcare
Automation and robotics
Pharma
Business intelligence and analytics
Claim management
Compliance
Customer analytics
Customer analytics
Data analysis
Data auditing
Data collection
Data integration
Data management
Discovery
Drug discovery
Enterprise resource planning
Generative AI
Large Language Models
Large language models
Marketing and advertising
Predictive analytics and forecasting
Research and development
Risk management

AI Opportunities for Life Sciences R&D – with Andrew Bolt of Deloitte

This interview analysis is sponsored by Deloitte and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
02 – Streamlining Data-Driven Decision-Making in Customer Experiences
for Financial Services@1x-min
Business intelligence and analytics
Finance
Case management
Financial services
Claim management
Insurance
Claim management
Marketing
Compliance
Media
Customer analytics
Customer experience
Customer service
Data analysis
E-commerce
Enterprise resource planning
Generative AI
Intelligent assistant
Investment
Large Language Models
model development
Recommendations
Report generation
Risk management

Streamlining Data-Driven Decision-Making in Customer Experiences for Financial Services – with Ciprian Porutiu of Marsh McLennan and Mike Borrelli of Uniphore

This interview analysis is sponsored by Uniphore and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
The Implications of Generative AI on Investment Banking – v.1
Business intelligence and analytics
Finance
Compliance
Financial services
Customer analytics
Government
Data analysis
Law enforcement
Data auditing
Data collection
Data integration
Data management
Enterprise resource planning
Generative AI
Investment
Large Language Models
Risk management

The Implications of Generative AI on Investment Banking – with Andrea Haskell and Val Srinivas of Deloitte

This interview analysis is sponsored by Deloitte and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
The Future of Customer Interactions in Financial Services
Business intelligence and analytics
Case management
Claim management
Claim management
Compliance
Conversion optimization
Customer analytics
Customer analytics
Customer experience
Customer service
Data auditing
Data collection
Data integration
Data management
Enterprise resource planning
Generative AI
Help desk
Intelligent assistant
Large Language Models
Market segmentation
Marketing and advertising
model development
Risk management

The Future of Customer Interactions in Financial Services – with Ivan Edwards of Cadence Bank and John Thomas of Uniphore

This interview analysis is sponsored by Uniphore and was written, edited and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
Financial Services Challenges from a Data Perspective – with Bill Waid of FICO@2x-min
Case management
Claim management
Compliance
Customer experience
Customer service
Data auditing
Data collection
Data entry
Data integration
Data management
Enterprise resource planning
Generative AI
Help desk
Intelligent assistant
Investment
Knowledge management
Large Language Models
model development
Natural Language Processing
Process automation
Report generation
Risk management
Training and development

Financial Services Challenges from a Data Perspective – with Bill Waid of FICO

Originally founded as Fair, Isaac and Company, FICO is a data analytics company founded in 1956. Based in Bozeman, Montana, the company is focused on credit scoring services, most notably its FICO score, which measures a person's creditworthiness. 
001 – Embedded Analytics in Banking – Two Use Cases
Business intelligence and analytics
Data analysis
Data integration
Data management
Enterprise resource planning
Generative AI
model development
Natural Language Processing
Predictive analytics and forecasting
Risk management
Search engine

Embedded Analytics in Banking – Two Use Cases

Traditional business analytics has undergone significant changes thanks to the internet revolution, cloud computing and the shift to self-service analytics, contributing to the recent rise of embedded analytics in business operations. Embedded analytics involves embedding and integrating visualizations, dashboards, reports or predictive analytics in everyday business applications. 
Strategic Adoption of Large Language Models and Generative AI – with Asif Hasan of Quantiph@2x
Case management
Data collection
Data entry
Data management
Discovery
Drug discovery
Employee engagement
Enterprise resource planning
Knowledge management
Legal research
Machine learning infrastructure
Process automation
Research and development
Risk management
Software development

Strategic Adoption of Large Language Models and Generative AI – with Asif Hasan of Quantiphi

As nearly every American household has realized over the last year, large language models combined with generative AI abilities pose tremendous challenges and opportunities for enterprises of every shape and size. Just ask anyone who has heard of ChatGPT.