12 Jan 2021
Artificial intelligence (AI) is a key component in the successful digital transformation of financial institutions. Deriving insights from data through machine learning plays a key role in accelerating business and mission critical initiatives, such as predicting risk, fighting financial crime or improving customer interaction.
To get the most from their investment in machine learning, AI leaders must focus on the entire machine learning workflow, and enable frictionless interaction between collaborating groups: data scientists, developers, operators. They must also monitor, observe and continuously adapt their models and meet challenges such as high reliability, regulatory compliance, and security.
In this session, we will share Red Hat’s experience of helping financial institutions build intelligent applications and discuss strategies to help you maximize the work you’ve done to deploy AI ML:
- Adopting end-to-end workflows that seamlessly apply machine learning as a business value multiplier in areas such customer experience, fighting financial crime, or risk assessment
- Applying cloud-native technologies to accelerate the implementation of machine learning processes and reduce operational overhead
- Adopting a hybrid cloud strategy for reliability and scalability, while meeting security and regulatory compliance requirements
- Jump-starting analytics work by combining cloud-scale deployments with the freedom of on-demand, scalable and secure analytics environments
Learn more at openshift.com
To get the most from their investment in machine learning, AI leaders must focus on the entire machine learning workflow, and enable frictionless interaction between collaborating groups: data scientists, developers, operators. They must also monitor, observe and continuously adapt their models and meet challenges such as high reliability, regulatory compliance, and security.
In this session, we will share Red Hat’s experience of helping financial institutions build intelligent applications and discuss strategies to help you maximize the work you’ve done to deploy AI ML:
- Adopting end-to-end workflows that seamlessly apply machine learning as a business value multiplier in areas such customer experience, fighting financial crime, or risk assessment
- Applying cloud-native technologies to accelerate the implementation of machine learning processes and reduce operational overhead
- Adopting a hybrid cloud strategy for reliability and scalability, while meeting security and regulatory compliance requirements
- Jump-starting analytics work by combining cloud-scale deployments with the freedom of on-demand, scalable and secure analytics environments
Learn more at openshift.com
- 1 participant
- 26 minutes
12 Jan 2021
Presented at AI Summit NYC 2020.
With the burgeoning number of digital financial products and services, financial crime, including fraud - starts posing new challenges to financial institutions. Many Banks are looking to artificial intelligence and machine learning as tools to fight fraud. Harnessing AI ML, banks can streamline data harvesting for deeper insight-driven business decisions and rules to address financial crime.
Learn how to augment batch and real-time transaction processing with analytics and machine learning models derived from data. We'll also show how it can be further integrated into business processes such as investigations and suspicious activity reporting in an efficient, scalable, and elastic manner, using a combination of open source technologies and hybrid cloud platforms.
Learn more at openshift.com
With the burgeoning number of digital financial products and services, financial crime, including fraud - starts posing new challenges to financial institutions. Many Banks are looking to artificial intelligence and machine learning as tools to fight fraud. Harnessing AI ML, banks can streamline data harvesting for deeper insight-driven business decisions and rules to address financial crime.
Learn how to augment batch and real-time transaction processing with analytics and machine learning models derived from data. We'll also show how it can be further integrated into business processes such as investigations and suspicious activity reporting in an efficient, scalable, and elastic manner, using a combination of open source technologies and hybrid cloud platforms.
Learn more at openshift.com
- 1 participant
- 10 minutes
5 Jan 2021
Presented at AI Summit NYC 2020:
Now, more than ever, banking customers are making use of digital channels for communication, payments, and any number of transactions. Maintaining a consistent 360-degree view of the customer is critical to ensure a positive customer experience in this new dynamic. Join us for this demo to learn how to use event-driven architectures, AI and ML, and rules-based decision management to dramatically improve experience with your business.
Presented by Sadhana Nandakumar
Now, more than ever, banking customers are making use of digital channels for communication, payments, and any number of transactions. Maintaining a consistent 360-degree view of the customer is critical to ensure a positive customer experience in this new dynamic. Join us for this demo to learn how to use event-driven architectures, AI and ML, and rules-based decision management to dramatically improve experience with your business.
Presented by Sadhana Nandakumar
- 1 participant
- 10 minutes
5 Jan 2021
Presented at AI Summit NYC 2020.
Business leaders desire data-driven insights to help improve customer experience.
Data engineers, data scientists, and software developers desire a self-service, cloud-like experience to access tools/frameworks, data, and compute resources anywhere to rapidly build, scale, and share results of their projects to accelerate delivery of AI-powered intelligent applications into production.
This keynote will provide a brief overview of the AI/ML use cases, required capabilities, and execution challenges. Next, we will discuss the value of open hybrid cloud powered by Kubernetes to help fast track AI/ML projects from pilot to production, and accelerate delivery of intelligent applications to the enterprise. Finally, the session will share real-world success stories from various industries globally.
Presented by Abhinav Joshi of Red Hat
Business leaders desire data-driven insights to help improve customer experience.
Data engineers, data scientists, and software developers desire a self-service, cloud-like experience to access tools/frameworks, data, and compute resources anywhere to rapidly build, scale, and share results of their projects to accelerate delivery of AI-powered intelligent applications into production.
This keynote will provide a brief overview of the AI/ML use cases, required capabilities, and execution challenges. Next, we will discuss the value of open hybrid cloud powered by Kubernetes to help fast track AI/ML projects from pilot to production, and accelerate delivery of intelligent applications to the enterprise. Finally, the session will share real-world success stories from various industries globally.
Presented by Abhinav Joshi of Red Hat
- 1 participant
- 20 minutes
