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AI technology is changing the banking industry, with the potential to save $300 billion and improve productivity by 5% by 2025. While some banks have adopted AI solutions, many are still figuring out how to use this technology. Learn how automation in customer service and fraud detection are making a difference, showing great results and returns. This article looks at real success stories, the costs and benefits of using AI, and the common mistakes that can hinder projects. Join us as we explore the future of AI in banking and find out strategies for success.

A new era is emerging where finance dissolves into the background, orchestrating actions on our behalf rather than demanding our constant attention. Advances in AI, hyper-personalization, and event-driven architectures are shifting the role of money from a standalone function to a “living” layer that works in real time to support individual and business goals. In this evolving landscape, loans get approved mid-transaction, cash flow is optimized automatically, and sustainability initiatives are funded seamlessly—each triggered by contextual cues rather than reactive, manual steps. It’s a vision of finance that’s as effortless as it is powerful, liberating people and organizations to focus on innovation, growth, and impact.

Revolutionizing Customer Engagement with Event-Driven Architecture In a rapidly evolving digital landscape, customer engagement has become more critical

Nenad Crnčec June 6, 2024 12:31 pm No Comments 5 minutes Enhancing Software Architecture with vFunction: Insights from

Before we get to era when a bank will be a single computer driven by market and tech trained AI, there are a few steps on the way we should give a moment to. Technology today provides possibility to offer much more individual approach to customers, but we don’t see much in practice, yet. Personalized banking isn’t just customized user interface on mobile and internet banking. There must be personalized product offer with it to create absolute unique customer experience. In this journey the main obstacle was regulation. But is technology ready to support it? How long banks can wait before Big tech come into game with their own personalized banking solutions and take the lead?

As the banking and fintech industries continue to evolve and embrace digital transformation, the importance of data architecture cannot be overstated. A well-designed data architecture is the foundation for efficient and effective data management, allowing organizations to leverage data for business insights and decision-making. However, designing data architecture in legacy banking and fintech systems can be a complex and challenging task. From data strategy development to data security and compliance, scalability and flexibility to real-time data processing, there are several essential steps to consider. This blog post will explore each of these steps in detail, providing valuable insights and best practices for designing data architecture in legacy banking and fintech systems. Whether you are a data architect, IT professional, or business executive, this blog will serve as a comprehensive guide to help you navigate the complexities of data architecture in the banking and fintech industry.