The quick-win data blueprint every mid-tier bank needs

MAY. 22, 2025

3 Min Read

Lumenalta

Mid-tier banks can’t afford to stand still. Customers now consider a seamless digital experience a basic requirement when choosing a bank.

Yet many of these institutions remain tied to aging core systems that silo critical data and force teams to perform tedious manual work just to extract basic insights. Business leaders may be pushing for advanced analytics and AI initiatives, but IT teams are hamstrung by the limitations of legacy technology. Competition from fintechs and large banks is rising, and regulators now demand stronger risk controls and reporting. Waiting years for a full core replacement isn’t an option.

Key takeaways

1. Mid-tier banks cannot wait for a full core system replacement to pursue innovation – customer expectations and regulatory pressures demand faster improvements now.
2. Aging core platforms keep data locked in silos and eat up IT budgets, making big-bang upgrades impractical for mid-size institutions. Targeted data platform modernization offers a smarter alternative.
3. Cloud-based data integration (for example, a data fabric architecture) breaks down silos and gives banks instant access to unified information, enabling quick wins like real-time fraud detection and personalized offers.
4. Quick-win data projects deliver rapid ROI (in months rather than years) and build stakeholder confidence, so IT teams can secure funding and support for further analytics and AI initiatives.
5. A pragmatic “surround and augment” approach – modernizing the data platform around the legacy core – lays the groundwork for broader AI adoption and long-term growth, without the high risk and cost of a core overhaul.

The good news is there’s a pragmatic way forward: modernize the data platform to unlock quick wins, AI readiness, and timely insights, all without a costly “rip and replace” of core systems.

Mid-tier banks can’t wait for core replacements to innovate

Legacy core banking platforms have become a primary roadblock to innovation. Over half of banks say their outdated core systems are the biggest barrier to digital progress. These aging platforms trap customer and transaction data in silos and slow down everything from onboarding new clients to generating basic reports. Mid-size banks also lack the deep pockets of industry giants. Institutions under $100 billion in assets dedicate roughly one-third of their IT budgets to new development and innovation, a fraction of what the largest players invest.

JPMorgan Chase, for example, pours about $12 billion annually into digital initiatives. This budget reality makes a full core overhaul impractical for many mid-tier institutions. Yet the pressure to improve is intensifying. Big banks and fintech startups are rolling out AI-powered services and personalized digital offerings that continually raise the bar for customer expectations. Regulators are likewise pushing for better data transparency, real-time reporting, and stronger risk controls – areas where legacy infrastructure falls short. Mid-tier institutions simply cannot afford to wait years for a core system replacement while these forces accelerate. They need a way to deliver new capabilities much sooner.

“These data initiatives deliver tangible wins fast, often yielding returns in months rather than years.”

Modernizing the data platform delivers quick wins at low risk

Instead of waiting on an expensive core overhaul, mid-tier banks are finding success with targeted data platform upgrades. Integrating siloed information into a cloud-based repository, creating a unified data fabric, breaks down data barriers while leaving the core system intact. This approach minimizes risk and disruption yet unlocks immediate improvements. Teams gain on-demand access to a wealth of previously isolated data, enabling new analytics and AI use cases right away.

Unified customer view: Consolidate data from all products and channels into a single source of truth, giving staff a 360-degree view of each customer.
Real-time fraud detection: Apply machine learning to integrated transaction streams to instantly flag suspicious activity so fraud can be stopped in real time, reducing losses.
Personalized product offers: Use comprehensive customer profiles to tailor credit, savings, or investment offers to individual behaviors and preferences, boosting uptake and satisfaction.
Automated compliance reporting: Quickly generate accurate regulatory reports and risk metrics from centralized data, cutting out tedious manual effort and ensuring regulators receive timely information.
Self-service analytics: Provide business teams with secure access to a cloud data warehouse (for example, using Snowflake) where they can query and visualize data directly, speeding up insights.

These data initiatives deliver tangible wins fast, often yielding returns in months rather than years. Early successes not only improve efficiency and customer outcomes but also build confidence among stakeholders. When executives see quick results with minimal risk, they are more willing to support further projects, creating a positive cycle of innovation.

Building an AI-ready data foundation without a core overhaul

Integrate and centralize siloed data

Building an AI-ready platform starts with breaking down data silos. Mid-tier banks can aggregate information from core banking, CRM, payments, and other systems into one cloud-based repository, creating a “single source of truth” everyone can rely on. Data flows continuously from legacy systems into the cloud via real-time pipelines or APIs, keeping the repository up to date without disrupting the core. With all critical information under one roof, the bank gains a holistic view of customers and operations.

Embrace a scalable cloud architecture

Modern data foundations are typically built on cloud infrastructure for flexibility and scale. A cloud-based data warehouse or lake can efficiently handle massive datasets and high-volume processing capabilities that aging on-premise cores lack. Some banks also adopt a data fabric approach to link legacy systems with cloud applications in one cohesive architecture, allowing rapid deployment of new data-driven features on top of core systems while ensuring the platform can scale as demand grows.

Improve data quality and governance

Consolidating data is only helpful if it’s trustworthy. Banks must cleanse data and enforce strong governance, standardizing definitions, automating quality checks, and tightly controlling access to sensitive information. Higher data quality ensures accurate reports for regulators and provides reliable fuel for analytics and AI. Robust governance keeps the data platform secure and compliant as it grows.

Layer analytics and AI capabilities on top

With a clean, unified data foundation in place, the bank can start turning raw data into insight. Instead of rewriting core applications, teams build analytics tools and AI models that tap into the centralized dataset. For example, a machine learning model for credit risk or customer churn can be developed using the cloud data and its insights fed into front-line systems without touching the core software. This incremental approach enables quick experimentation and ongoing value delivery. Relatively few banks have fully embedded AI into their core systems so far (only about 32% have), but 39% plan to implement AI within the next year. This strategy lets a mid-tier bank join the AI wave now instead of waiting years for a core replacement. New data-driven services roll out faster, giving the organization a competitive edge and a preview of what a fully AI-powered bank can achieve.

“This strategy lets a mid-tier bank join the AI wave now instead of waiting years for a core replacement.”

Quick wins now pave the way for AI-fueled growth

Each successful data project not only solves a tactical issue but also creates the foundation for long-term strategic value. Quick wins show measurable outcomes quickly, helping IT leaders align internal stakeholders while proving the business case for deeper investment. When teams see progress within weeks or months, momentum builds and confidence grows across departments, from compliance to customer experience. This creates space to prioritize the next initiative without waiting for annual budget cycles or complex vendor negotiations.

As data initiatives accumulate, they gradually unlock more advanced capabilities. A bank that begins with fraud detection or reporting automation can then scale into AI-driven customer insights or predictive credit scoring. What starts as operational efficiency evolves into smarter product delivery and entirely new service models. Institutions that take this incremental route consistently outperform peers who wait for large-scale replacements. The sooner quick wins are secured, the faster a bank can establish a compounding advantage.

How Lumenalta supports pragmatic data platform modernization for mid-tier banks

Building on the momentum of early quick wins, many mid-tier banks reach a point where scaling their data modernization strategy requires practical guidance and technical alignment. At this stage, Lumenalta works with internal teams to identify the next logical steps, often focused on consolidating data pipelines, simplifying access, or improving governance without reengineering the core platform. This allows IT leaders to stay focused on outcomes like faster time to insight, more responsive risk reporting, or rolling out new analytics use cases faster than traditional methods allow.

Our approach is grounded in co-execution rather than large-scale implementation. We align with IT and business teams to shape iterative projects that improve data usability, resilience, and AI-readiness with minimal disruption. With each cycle, banks gain more control over their data assets, build trust across departments, and unlock new efficiencies. This methodical, outcome-focused strategy gives CIOs the confidence to scale innovation over time while keeping risk low and results visible.

Table of contents

Mid-tier banks can’t wait for core replacements to innovate
Modernizing the data platform delivers quick wins at low risk
Building an AI-ready data foundation without a core overhaul
Quick wins now pave the way for AI-fueled growth
How Lumenalta supports pragmatic data platform modernization for mid-tier banks
Common questions

Our Approach