Data and AI Make Digital Transformations Possible

These are the data, customer experience (CX) and artificial intelligence (AI) stories we’re reading here at Finalytics.ai this week.

AI Comes To The Rescue

As core banking technology ages, banks and credit unions have another reason to transform their business models. The mainframe systems that support these organizations are incredibly resilient; for better or worse,  this has allowed them to delay upgrades. Roberto Torres has an article in CIO Dive that examines the issues and possible ways to address them.
We were excited to read about how AI can help. Torres writes,

Much like other aspects of IT, maintaining software applications is a task where AI can be of assistance. If the only feasible option is keeping the application running on the mainframe, AI tools could help understand the code base, improving maintenance.

Transformation Isn’t Just Putting on Make Up

On the topic of aging banking infrastructure, Sue Laws, SVP Business Solutions at Temenos, has a blog entry reminding us that digital transformation is more than just “putting lipstick on legacy infrastructure.”
Laws warns:

Financial institutions recognize that their customers now want and expect services that are convenient, relevant, seamless, frictionless, personalized, and instant. What many of these organizations fail to recognize (or fail to understand) is the substantive cultural and technological shift that has to occur to transform these expectations from marketing buzzwords to mission-defining strategies.

Business-Led Technology and Data Platforms for Digital Transformation

The Boston Consulting Group (BCG) has a must-read (and watch) blog entry discussing the six factors required to have a successful digital transformation. We particularly focused on the sixth factor “create a business-led technology and data platform.” This is one area that we advocate for with our clients and agree with BCG, along with the rest of their points. They write,

Successful CIOs reinforce that the technology and data platforms must be designed around business priorities. They then implement them using best practices for modularity, flexibility, and scalability, with continuous learning and delivery. Deployment is carried out in frequent release cycles in order to adapt to changing business needs and deliver value incrementally.

How is Banking Like Cycling

We enjoyed Simon Walsh’s article in CIO titled “What the Tour de France can teach us about digital transformation.” Walsh, NTT Ltd CEO for the Americas, explores what makes the Tour De France one of the most successful sporting events  and outlines five technology lessons that can be gleaned.

• Track everything of value
• Increase end-to-end visibility
• Combine services to advance your business objectives
• Embrace agility
• Leverage data to provide real-time analytics

We zeroed in on the last item. Walsh says,

With the amount of data generated by all organizations continuing to explode, they need to tap into the power of modern AI and ML systems to expose insights that can be used to evolve organizational strategy, directing their innovation efforts and ensuring that they’re able to continually evolve their customer experience.

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