Financial services companies need a reliable, secure, and flexible platform in order to build toward the future. Red Hat has the expertise to help you modernize so that you can meet customer demands while reducing costs and risk.

Developer Experience has become one of the biggest success drivers for large-scale distributed platforms. Randeep Khaund and Eddy Krumholz from Deloitte recognized this pattern early on and decided to invest in this area many years ago. This session will explore how Deloitte approaches developer experience, leveraging Red Hat® Developer Hub - as well as a collaborative approach for application modernization.
 
You will also hear about real-world implementations in the financial services industry and how they were used to scale platform usability and application modernization in a highly regulated environment.

Building developer experience, collaboration, and self-service IDPs at an enterprise level

Join us for a 10-minute Coffee Break while we explore AI in transforming the insurance industry. Learn how insurance companies are using AI and where they are going in the future of digitization of operations and services while looking at the opportunities and challenges involved. We’ll explore the digital transformation journey for insurers, offering insights into practical strategies and tools that can support sustainable AI adoption.

Join us as we address:
- The transition from initial GenAI interest to sustained, scalable implementation. 
- The necessity of a multi-year approach to achieve significant ROI from AI systems.
- How to develop comprehensive governance and control models to ensure ethical, fair, and compliant AI usage. 
- The importance of transparency, accountability, and adherence to state laws and regulations to maintain a secure and equitable system.

AI Revolutionizing Financial Services and Insurance

The financial fraud threat landscape is vast and constantly evolving, with new attack methods continuously testing the limits of financial institution protection systems. Current AI and ML models, which primarily rely on historical or internal data, have significant weaknesses. They often fail to keep up with the innovative tactics of fraudsters, leaving institutions vulnerable.

To counteract this, financial institutions can leverage synthetic data to enhance their defenses and better understand and respond to evolving fraud and money laundering schemes. Synthetic data helps simulate a wider range of scenarios, providing insights that internal data alone cannot offer. Consistent communication and intelligence data sharing between institutions are crucial for preventing or limiting the impact of financial fraud. Such collaboration enables institutions to stay informed about emerging threats and adapt their defenses accordingly.

Investing wisely in AI systems is essential. Financial institutions must allocate their budgets and resources to develop the most effective AI systems for fraud detection and protection, both now and in the future. This includes embracing synthetic data tools and fostering cross-industry cooperation. As the volume and cost of financial crime threats rise, banks and other financial services must move beyond reliance on internal structures. Instead, they should adopt the latest technology and collaborate more openly to detect and deter attacks.

AI and Synthetic Data: Fighting Financial Fraud and Protecting Customers

Fraudsters are exploiting the vulnerabilities of increasingly interconnected economies, leaving banks facing damaged customer relationships, intense regulatory scrutiny, and heightened reputational risk. 

Near-instant payment settlement leaves little time for traditional fraud detection procedures, adding to a costly and growing challenge. While human analysts are grappling with a complex payments landscape, suspicious activities are staying under the radar of legacy rules-based systems. Artificial intelligence is becoming paramount to improving detection of fraudulent transactions, yet low-quality data is hampering model performance. 

In a bid to improve the accuracy of their AI models, innovative banks are turning to secure intelligence sharing on fraudulent behaviours with external partners. Federated learning is capable of preserving customer privacy while enabling competing banks to share intelligence on bad actors, bolster machine learning models, and reduce operational costs. 

Hosted by The Banker in partnership with Red Hat, this webinar convened leading technology and banking executives leveraging AI to fight financial crime. Chaired by Elizabeth Lumley, Deputy Editor of The Banker, our panel explored use cases for automation in fraud detection, while spotlighting advantages of federated learning for the payments industry and beyond.

Combatting Payment Fraud with AI: A Webinar with The Banker

Learn the fundamentals about OpenShift Virtualization and what you need to understand when getting started.

OpenShift Virtualization 101: Fundamentals and Getting Started

Payments continue to change and expectations are expanding rapidly. Staying on top of it all is a challenge we can help with. Join payments expert, Ramon Villarreal, to take a look forward so you can better meet future demand.

In this Exec to Exec, get:

- Insights on how successful firms are using technology. 
- Trends that impact the future of payments.
- Guidance for setting yourself up for the future.

Watch now.

Exec to Exec: The future of payments

Creating new automation workflows faster based on ServiceNow ITSM has been very beneficial for customers as teams were freed from hours of manual efforts and could use a single source of truth in the ServiceNow configuration management database (CMDB). With many benefits, knowing how to get starting is key.

In this Quick Code Idea, Joon will provide insight into: 
  - Why you’d want to use Ansible with ServiceNow.
  - How to get started setting this up.
  - Things to think about before getting started.

Watch now.

Quick Code Ideas: Using Red Hat® Ansible® with ServiceNow

The constant demand for new applications hasn’t slowed, and will likely grow with the public's understanding of AI capabilities. They will expect more, and in less time. 

In this Quick Code Idea, get:
  - Insight on what OpenShift AI  and GitOps offers.
  - The benefits of using them together.
  - How to get started OpenShift AI and GitOps together.

Watch now.

Quick Code Ideas: OpenShift® GitOps and OpenShift® AI together

Join us for a 10-minute Coffee Break to learn about transformative changes occurring in the financial trading sector. We’ll discuss the adoption of cloud computing, big data analytics, and blockchain technology, which are collectively reshaping trading infrastructure. We’ll cover how these innovations enhance trading speed, reliability, and security, enabling firms to handle higher volumes of transactions more efficiently. We’ll highlight the role of real-time data analytics in providing actionable insights, improving decision-making processes, and optimizing trading strategies to capitalize on market opportunities.

Join us to learn about: 
- The implications of these modernizations on regulatory compliance and market integrity. 
- The need for updated regulatory frameworks to keep pace with technological advancements, ensuring that new trading practices remain transparent and fair. 
- The impact of modernization on market accessibility, noting that advanced trading technologies are democratizing access to financial markets, allowing a broader range of participants to engage in trading activities. 
- The future of trading modernization, driven by artificial intelligence, machine learning, and other emerging technologies, which are expected to further enhance the efficiency and effectiveness of financial markets.

Modern Trading Platforms using AI

Explore the transformative power of AI in improving customer experiences, optimizing operations, and mitigating risks. Although AI has immense potential, navigating challenges such as data locality, regulatory compliance, privacy concerns, and the rapidly evolving AI landscape requires careful consideration.

The webinar will be led by industry experts, including Aric Rosenbaum, Chief Technologist on Red Hat’s Global Financial Services team, and Noreen Hansen, Senior Vice President, Hybrid Cloud & Infrastructure Practice, NTT Americas Inc. They will provide a comprehensive review of architectural approaches to AI, giving attendees valuable insights to make informed decisions tailored to their unique use cases.

This is an opportunity to explore the intersection of AI and hybrid environments and discover strategies for harnessing the full potential of AI while overcoming obstacles.

AI in a Hybrid World

Imagine if your teams had extra time to innovate to get you an edge over other financial firms, as you exceed customer expectations.

In this Exec to Exec, we’ll discuss how to:

  - Decrease potential risk.
  - Gain cost optimizations.
  - Steps to take to gain benefits.

Watch now.

Exec to Exec: How automation impacts the business teams in a financial firm

This webinar offers an introduction to software-defined memory, the integration with Red Hat OpenShift, enhanced AI performance and efficiency, and future trends.

Faster AI with Software Defined Memory

Modernization is not a destination and no two modernizations are the same. Knowing what steps to take to set up your organization for continued success is key to modernization.

In this Exec to Exec, get:
  - Why your teams should transform at your pace, not your vendors.
  - Insight on how firms are successfully modernizing.
  - Tips and tricks for modernizing your core banking system.

Watch now.

Exec to Exec: Diving deeper into a modern core banking system

The fast growth of AI innovations and hybrid cloud complexity plus the need for controls around governance and compliance policies has become more critical. Automating Policy as Code (PaC), works to ensure organizations, especially highly regulated industries like financial services, can reduce their risk and more effectively enforce policies.

In this Quick Code Idea, Roger will discuss:
  - The impact of PaC. 
  - How to automate PaC with Red Hat Ansible.
  - Tips to using Red Hat Ansible for PaC.

Watch now.

Quick Code Ideas: Policy as Code with Ansible®

Generative AI on its own won’t help you reach your AI goals. You need to be able to train it, prompt–tune and fine tune it. If done well, it can help with many aspects of your business, including onboarding new customers faster. 

In this Exec to Exec, we’ll discuss:

- What you should expect from AI in financial services.
- How your business and technical teams can use GenAI today.
- What guardrails you should consider for your future strategy.

Watch now.

Exec to Exec: How to use GenAI in your firm without losing control

How can you save time? How can we get this done faster? If those sound familiar, Red Hat® Ansible® can help. Dig a little deeper into the impact of Ansible with load balancers and DNS. 

In this Quick Code Idea, see:
  - What to expect with Ansible®.
  - Tips and tricks for load balancers and DNS.
  - Breaking down tasks to find quick success.

Watch now.

Quick Code Ideas: Deeper dive on Ansible®, load balancer and DNS

Manual tasks can be exhausting, especially when they don’t have to be manual. Red Hat® Ansible® provides the tools you need to automate the configuration and maintenance of load balancers and DNS records.

In this Quick Code Idea, see:
  - How to approach automating your load balancer and DNS.
  - The benefits you can expect.
  - Tips and tricks from taking this approach.

Watch now.

Quick Code Ideas: Using Red Hat® Ansible® to update load balancer and DNS

As financial institutions become more sophisticated in their use of Generative AI and move from experiments to production-grade enterprise deployments, they face several challenges including:

- A rapidly changing landscape of small to large LLMs including commercial and open-source LLMs utilizing RAG applications
- Data gravity, regulations, and security force firms to target multiple deployment environments including on-prem, private, and public cloud
- Others: Lack of accuracy, data response hallucinations, high latency, low scalability, and high costs
To seamlessly produce and deploy, they need to have a flexible and scalable end-to-end approach. Red Hat and 

NVIDIA work together to enable an end-to-end solution built on OpenShift AI and NVIDIA AI Enterprise, including NIM, that enables enterprises to maximize efficiency and performance and maximize their AI investment.

Unlock the Secrets: Successful Gen AI in Financial Services

Artificial Intelligence

Generative AI

Financial Services

Banking

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Unlock the Secrets: Successful Gen AI in Financial Services

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