Data is the new currency: What stops banks from attaining their full potential?

Banks today sit on a wealth of customer data. Yet, despite this treasure trove, growth is stagnating. Why? The challenge isn’t the lack of data but how it’s leveraged. Insights are often trapped in silos, not actionable in real time, leading to delayed decisions and missed opportunities.

This blog explores why data fragmentation makes AI adoption in banking a challenge, how AI can be optimized to drive real-time decisions, and how banks can overcome these barriers to unlock significant growth across deposits, lending, and customer lifetime value.

TL;DR Summary:

Banks have vast amounts of customer data, but growth is stalling due to data fragmentation and the inability to turn insights into timely action. This blog explores how AI can help overcome these challenges by unifying data, enabling real-time decision-making, and driving growth in deposits, lending, and customer loyalty. It highlights the importance of data quality, real-time insights, and personalized customer engagement to unlock meaningful results and increase profitability.

The Need for Data-Driven Innovation in Banking

In today’s competitive landscape, banking executives are under constant pressure to deliver growth without increasing risk or operational costs. Artificial Intelligence (AI) is frequently touted as the solution.

However, many institutions are discovering that the real obstacle isn’t the availability of data or sophisticated algorithms – it’s the ability to translate insights into action consistently, particularly across diverse customer journeys.

Data is often siloed, fragmented, or of insufficient quality, creating challenges for decision-making. According to The Financial Brand, 47% of financial institutions report operating with heavily siloed data, and 40% acknowledge that poor data quality directly impedes decision-making.

When the foundation is unreliable, even the most advanced AI models yield inconsistent or misleading results.

This fragmentation results in a predictable cycle:

1. Banks invest in expensive analytics initiatives.
2. Insights are buried within dashboards.
3. Frontline teams, decision-makers, and channels fail to act on these insights.
4. Growth stagnates despite data investment.

The key to breaking this cycle lies in transforming how banks utilize their existing data to drive real-time engagement, improving agility, customer engagement, and overall business growth.

Understanding the Barriers to Data-Driven Decision-Making in Banks

Data growth in banks has outpaced the systems built to harness it. As banks evolved, new technologies and regulatory requirements led to more data sources, making it harder to integrate and act upon. Here’s why this persists:

• Multiple systems store customer data in various formats, creating inconsistent views.
• Mergers and acquisitions often result in incompatible platforms and data silos.
• Legacy processes hinder real-time decision-making due to outdated schemas.
• Additional compliance layers slow down modernization efforts.

Deloitte’s 2024 survey found that 92% of leaders state the data they need is either unavailable or takes too long to access. Meanwhile, 88% report issues with data integration, and 81% experience trust and quality problems. This makes it evident: banks aren’t lacking data, but rather they are struggling with actionable data.

The Cost of Data Fragmentation: Missed Opportunities for Real-Time Action

Banks generate valuable insights through AI, but these often remain unexploited. Here’s a typical scenario:

1. Analytics teams identify high-risk customers or product upsell opportunities.
2. Insights are recorded or placed in a dashboard.
3. Relevant teams (e.g., relationship managers) are expected to act.
4. Execution is delayed or inconsistent.
5. By the time action occurs, customer intent has changed, and the opportunity is lost.

This delay prevents banks from engaging customers in the moment, weakening deposit growth, reducing loan conversions, and diminishing loyalty. The competitive advantage lies in transforming insights into actions instantly and on a hyper-personalized level, delivering meaningful growth for banks.

Key Insights That Drive Bank Growth

For AI to drive business growth, banks must focus on these four critical questions:

1. How can banks trust data for decision automation?
   Clean, structured, and unified data forms the backbone of trust in automated decision-making. More tools won’t solve the problem if the foundation is shaky.
2. Which insights should banks act upon?
   Focus on actionable insights that lead to tangible outcomes, like higher engagement, faster lending decisions, or increased deposits. Insights tied to business KPIs become growth drivers.
3. Can insights be acted on in real time?
   The secret to growth lies in momentum – acting in real time as customers engage. Whether it’s a balance check or an abandoned loan application, these moments must prompt immediate action.
4. How can banks ensure cross-functional adoption?
   The right insights must be embedded into channels, not left in dashboards for interpretation. Real-time action demands real-time integration.

From Insight to Action: A Practical Framework for Executives

To optimize data use, executives can follow this simple 4-step model:

1. Address Fragmentation First
   Prioritize cleaning and unifying the most impactful data sources, like transactional and behavioral signals, customer preferences, and product holdings.
2. Identify Insight Moments
   Pinpoint high-intent signals, such as a salary deposit or credit card reward browsing. These are critical moments when customers are most likely to take action.
3. Choose the Right Action Logic
   Tailor the response: Send proactive nudges, offer personalized products, or suggest financial wellness tips based on insights.
4. Deliver Action Through the Right Channel
   Consider customer comfort and urgency. Use mobile push for real-time nudges, emails for structured offers, and dashboards for complex cases.

Transforming Fragmented Data into Real-Time Business Impact: Real-World Scenarios

Here’s how real-time data intelligence can create significant business outcomes:

• Transaction: International flight booked using a card
  Action: Send a 1-tap offer for travel insurance or discounted hotel bookings immediately after the transaction.
  Impact: Increased cross-sell and card loyalty.
• Behavior: Consistent monthly rent payments detected
  Action: Offer a personalized message suggesting home loan eligibility.
  Impact: Faster lending conversions.
• Behavior: Frequent checking of credit card rewards
  Action: Trigger a promotion for bonus points in a category the customer recently explored.
  Impact: Higher wallet share and customer activation.

These personalized actions can be automated in real-time, ensuring that banks don’t just respond but proactively engage with customers when their intent is highest.

Leveraging Technology to Scale Real-Time Decisions

For banks to capitalize on real-time insights, they need a robust technological framework:

• Unified engagement data layer
• Real-time signal detection
• Adaptive decision logic
• Multi-channel orchestration
• Continuous feedback and learning

This combination of technologies enables operational intelligence, turning fragmented data into cohesive, actionable insights that can be executed in real time.

The VARTA Advantage: A Unified Solution for Real-Time Banking Decisions

The challenge many banks face is that critical customer data is dispersed across systems, making it difficult to act on in real time. VARTA solves this by providing a unified engagement layer that makes transactional and behavioral data usable for automated decisioning.

VARTA detects signals as they occur – whether it’s a travel plan, rent payment, or a shift in financial behavior – and selects the most appropriate action. It ensures timely engagement through the optimal channel, avoiding delays or missed moments.

This integrated approach moves banks from mere data analysis to actionable intelligence that delivers real-world business outcomes.

Conclusion: Leading the Future of Banking with AI-Driven Growth

In the modern banking landscape, growth doesn’t come from more data. It comes from how quickly and effectively you turn insights into action at the right moment. Banks that master this process will lead the charge in deposits, lending, and long-term customer loyalty.

Ready to see how VARTA can drive real-world growth in your institution? Book a session with our team today.

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