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Customer relationship management is at the heart of any business activity, and financial services are no different. Since the early days of computers and databases, finding and storing information about your customers was one of the prime objectives of any IT function within an organization. But there’s a problem: Capturing. For most businesses, their knowledge of their customers is largely external and needs to be “captured” before it can be stored. What they do, what they like, what they are trying to achieve, plus all the other relevant metrics.
But banking is different. The vast majority of customer data already sits within the financial institution. This doesn’t mean that a CRM is not needed, but it should behave in a fundamentally different way than it does in any other industry. Sadly, though, this is not the case.
Today, most banks use elaborate CRM platforms that are built to serve numerous use cases with complex workflows, lots of different roles, and an array of objectives they’re meant to address. Those platforms often also share the same philosophy of building a customizable platform around a sales funnel, so that the needs of all industries and all types of customers can be met.
The key word here? Customization. Yes, they can add account balances. They can add a credit score. They can even look at more elaborate metrics. But they are not natively dealing with financial data – and, in fact, they are not even built to work effectively with internal data.
Most of those systems are still built to cater to the “capture” part of the whole operation. This never made sense for banks and now it is likely to be even more dangerous. Why? Like many other things today, the answer comes back to AI. The new AI models are data-hungry, and in most cases they turn to the databases that underpin CRMs for knowledge about the customer. But this is wrong. As we’ve alluded to in this article, the true insight doesn’t lie in the CRM. In fact, the CRM isn’t anywhere near as important for financial institutions as it is often suggested. Yes, you need to keep track of the customer file of course, you need to record conversations, and yes, outbound is a real thing. But most of that already exists. What’s missing is a deeper insight, bringing the data together, making sense of it and executing on it. And somewhere down that journey, a powerful enabler for AI can be found as well.
At Bud, we focus on the data. What banking has been built on, before it was obscured by generic tools overindexing on flows transplanted from other industries, and overcomplicated by endlessly customized platforms.
What does this mean for banking intelligence? It is not going to be advanced by iterating on traditional CRM platforms. It needs a complete overhaul, starting with the troves of data financial institutions process already, but are currently relegated to core banking platforms that often are a separate silo from CRM. There is no need for that. In fact, quite the opposite. Insights based on financial data, consolidated, analyzed, and enriched, should be the cornerstone of any activity that aims to understand the customer and build better relations. We believe that you can get much better results if you focus on that, versus yet another expensive iteration of your CRM.
We have already equipped the Bud platform with tools that accomplish many of the CRM’s objectives, and this is only the beginning of the journey. With relentless focus on the data, skills to get it flowing from different sources, and experience in enabling AI models and agents on top of that, we are in the position to look at building customer relations and achieving goals that are still elusive to most existing CRM systems.
Ultimately, banking intelligence in the age of AI must be built on data that truly reflects each individual customer. And that data is not in the CRM – it’s in the core banking platform, in the payments gateway, in the ledger record. Combined, maybe even with a bit of account aggregation, it can be sprinkled with some CRM-generated insights; but only as an add-on. We’re not arguing that CRMs are not needed at all. But we believe that most of the implementations out there do not realize the true potential of data, and often don’t even attempt to.
The final argument for taking a different path is AI. CRM captured information is inferior both in volumes and in quality to financial data. Call logs rarely tell a story as deep as a year of transactions. Clicks on a website are not the same as buying decisions. Eyeballs on a mortgage banner do not match regular loan repayments. When AI models need to understand a customer, feeding them CRM data is like giving a doctor a patient’s appointment history instead of their medical records. The signal is there. It’s just in the wrong place.
Banking intelligence should be built on banking data. If CRMs cannot accomplish that, perhaps something better is needed.
Ready to see what banking intelligence looks like when it starts with the right data? Talk to Bud today.
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