Your financial institution will fail at AI if you don’t have a data strategy

As national financial institutions move swiftly to integrate AI more deeply into their businesses, mid-sized and community institutions risk finding themselves at a competitive disadvantage with their larger peers. This is especially significant given that AI has moved beyond yesterday’s chatbots to offer deep insights into your customers, prospects and business operations.

However, before setting out to catch up with your competitors on AI, you first need to implement a data strategy — or your AI efforts are likely doomed. Keep reading to find out why, plus how to get started.

Why do financial institutions need a data strategy to get results from AI and technology?

While financial institutions have typically been cautious to implement a data strategy due to compliance concerns, you can’t implement AI or other modern technology without one. No data strategy means nothing for AI to draw on, plus disconnected systems that limit visibility inside your business and stymie automation, slower reporting and even cybersecurity risks.

• No data foundation for AI: Layered on top of a modern data foundation, AI can give you a much deeper understanding of what’s happening inside your institution, gather customer insights and store institutional knowledge. But without that foundation, AI is largely useless.
• Disconnected systems: It’s not just AI, either — without a data foundation like a data lakehouse (a central repository for all your structured and unstructured data), your institution can’t integrate your core systems like lending, HR and operations, which makes it easier to share information, gain visibility and automate tasks.
• Limited visibility: If you don’t have a data foundation in place, you’ll have no real ability to use AI tools to get a better understanding of what’s happening inside your business by analyzing your data for trends, patterns or risks.
• No automation: Without integrated systems, you can’t automate many of the routine tasks your team currently does to collect and analyze data from your systems.
• Slower reporting: Disconnected systems also translate to slower reporting, as a lack of automation means your team will continue to be stuck filling out spreadsheets.
• Cybersecurity risks: If you don’t have a data strategy, you’re likely still running on-premise systems rather than modern cloud-based alternatives. This represents a distinct cybersecurity threat, as cloud-based systems are almost always more secure and up to date than anything on-prem.
  

How does implementing a data strategy strengthen your financial institution?

By implementing an effective data strategy, you open the door to using AI far more deeply and effectively to understand your business and your customers. You’re also able to integrate your core systems and develop readily accessible institutional knowledge within your organization, plus save money, strengthen compliance and make better use of your unstructured data.

Gain deeper member and prospect insights

AI can help you better understand your customers, members or prospects. For example, an AI tool can combine insights from your own data plus sources like ZoomInfo to help you tailor a loan offering to the specific customers who are most likely to be interested, while freeing up your team to spend more time having these kinds of conversations.

Preserve institutional knowledge

Your institution, like most, probably relies heavily on the institutional knowledge of long-serving leaders or employees. But what happens when those individuals retire? Right now, you risk losing all those years of experience and insight, but AI can help capture that wisdom and make it accessible to your entire team.

Save time with automation

With a strong data foundation allows you to automate large amounts of data collection work that you may have previously been doing in spreadsheets. This allows your team to spend time on more valuable, higher-level work like forecasting, scenario planning or financial modeling.

Reduce IT expenses

If the phrase modern data strategy sounds expensive, consider that it could actually save you money. Implementing a data strategy means moving your systems and data storage into the cloud, which frees you from the costs of maintaining servers, keeping them updated and other related IT efforts.

Strengthen regulatory compliance

While financial regulators have been mostly quiet on the subject of AI, don’t expect their reticence to be permanent. Taking deliberate, thoughtful action today to implement a cohesive data strategy will allow you to demonstrate good faith to regulators tomorrow and justify whatever decisions you’ve made once future regulatory guidance is announced.

In addition to being a compliance area, AI is also a powerful compliance tool. Use it to compare your current controls and compliance policies with regulatory guidance and watch for signs of fraud in your general ledger or reporting.

Glean business insights from unstructured data

Finally, a data foundation like a data lakehouse allows you to generate business value from the massive pile of unstructured data that your team and customers create during normal everyday operations. Unstructured data includes documents, PDFs, emails, videos and audio recordings — and AI can look through it to find patterns or answer complicated organizational questions.

For example, you could analyze lending trends by ZIP code to assess your compliance with fair lending rules and then follow that up by asking AI to take a look at your lending policies to discover whether a policy roadblock is responsible for discrepancies in loans issued. By doing so, AI could save you both from a compliance issue and identify a growth opportunity in one go.

What are the key steps for financial institutions to implement a strong data strategy?

Implementing an effective data strategy is an ongoing effort. But to start, financial institution leaders should:

1. Develop a data-driven organizational mindset

Many mid-sized or community financial institutions don’t operate with a data-driven mindset. However, adopting one is a key part of adapting to today’s business climate, so while you don’t have to do it overnight, it is essential to start.

2. Do a maturity assessment with a technology advisor

Meet with a technology advisory firm to do a maturity assessment so you can establish where your institution stands right now. During this process, you’ll look for specific pain points that are holding your team back or hampering your operations.

3. Create a strategic data and technology roadmap

With your technology advisor, develop a long-term data and technology roadmap to solve your pain points by implementing a modern data strategy. Key steps could include creating a data lakehouse, moving onto a cloud-based CRM or implementing new AI tools to glean insights from your data.

How Wipfli can help

We advise financial institutions on how to strengthen performance, compliance and growth. Let’s talk about how your goals and how strategies like a modern approach to data and AI can help you achieve them. Start a conversation.

Let’s make your institution stronger

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