Community banks have plenty of customer data. The harder problem is turning that information into timely decisions before a customer walks away. A new Celent case study examines how Connecticut-based Union Savings Bank is using NGDATA’s AI Hub to identify customers at risk of leaving, determine why they may be at risk and trigger targeted engagement—without replacing its core banking system or building a new data lake.

For large banks, deploying artificial intelligence can mean building expansive data platforms, integrating new infrastructure and investing heavily in specialized technology teams. Union Savings Bank (USB) is taking a more incremental route: use the customer data it already has and turn it into actionable signals.

A new Celent Solution Brief, Targeted Customer Engagement in Retail Banking, examines how the $3.3 billion community bank in Danbury, Connecticut, uses NGDATA’s Intelligent Engagement Platform (IEP) and AI Hub to identify customer relationships at risk, understand the reasons behind potential attrition and determine what action the bank should take next.

The approach addresses a familiar problem in retail banking: identifying customers likely to leave is useful, but prediction alone does not retain them.

USB’s implementation connects predictive analytics with targeted customer journeys. When the platform detects warning signals associated with potential attrition, it can trigger personalized retention or promotional outreach intended to address the customer’s circumstances.

That moves AI from a reporting tool into an operational layer for customer relationship management.

According to the Celent brief, USB achieved a 74% churn catch rate, a 79% Top-3 next-best-product recommendation accuracy for retention, and 90% retention from targeted Certificate of Deposit (CD) renewal campaigns.

Those figures are reported in the vendor’s announcement based on the Celent study, rather than representing independently audited industry benchmarks. Still, they illustrate the type of outcome financial institutions are seeking as AI moves deeper into retail banking.

The distinction between predicting churn and acting on it is increasingly important.

A conventional analytics system might tell a bank that a customer has a high probability of leaving. A more integrated AI system can combine that prediction with customer, account, transaction and product information to recommend an intervention.

For example, a customer approaching the maturity of a CD might receive a targeted renewal offer. Another customer displaying different behavioral signals could enter a retention journey designed around a different product or service.

The objective is not simply to send more marketing messages. It is to make customer engagement more relevant to the customer’s financial situation.

Frank Sottosanti, SVP and Director of Brand & Innovation at Union Savings Bank, said NGDATA’s predictive models have helped the bank identify retention opportunities and recommend next-best products.

That use case reflects a wider transformation in banking technology. AI is increasingly being applied to customer intelligence, personalization, fraud detection, credit decisioning and service automation. The challenge for many regional and community institutions is that their technology environments were not designed around modern AI workloads.

NGDATA’s proposition is therefore partly about infrastructure strategy.

The Intelligent Engagement Platform operates within a financial institution’s existing technology environment, activating customer, account, product and transaction data. AI Hub then applies predictive models to that information rather than requiring the bank to replace its core banking system or first construct a large centralized data lake.

This matters for smaller financial institutions competing with technology-heavy national banks.

Celent banking analyst Michael Bernard described the platform as allowing community and regional institutions to “leapfrog” toward capabilities traditionally associated with larger banks.

That does not mean smaller banks suddenly acquire the same technology footprint as JPMorgan Chase, Bank of America or Wells Fargo. Instead, the model suggests that targeted AI capabilities can be layered onto existing infrastructure without requiring a wholesale modernization project.

Explainability is another important part of the implementation.

NGDATA says its platform can identify the factors contributing to an individual churn prediction in plain language. For banking teams, that can make an AI recommendation easier to evaluate than an opaque probability score.

This is particularly relevant when AI outputs influence customer communications. Marketing and digital teams need to understand why a customer has been flagged before deciding whether an intervention is appropriate.

The architecture also creates a feedback loop. Customer interactions and measured outcomes can feed subsequent predictions and actions, allowing the system to learn from how customers respond.

For enterprise banking teams, the strategic implication is clear: the value of customer AI increasingly depends on connecting prediction, decision and execution.

Banks already possess enormous volumes of customer information. The competitive advantage may come less from collecting another layer of data and more from making existing information useful at the moment a decision needs to be made.

That is particularly relevant to deposits. With banks competing for stable funding and customers increasingly able to compare rates and financial products digitally, preventing attrition can be as important as acquiring new accounts.

The same infrastructure can potentially support broader relationship expansion, including next-best-product recommendations and personalized financial engagement.

NGDATA CEO Doug Gross argues that community and regional banks can compete on relationships by activating decades of customer and transaction data.

The idea represents a pragmatic alternative to the “build everything from scratch” approach to banking AI. Rather than treating artificial intelligence as a separate technology program, banks can embed predictive intelligence into existing customer-engagement workflows.

USB’s deployment is therefore less about introducing another AI chatbot or generative-AI interface. It demonstrates a more operational use of banking AI: identifying customer risk, explaining the drivers behind it and giving teams an actionable next step.

As financial institutions continue experimenting with AI-powered customer intelligence, that distinction could become increasingly important.

The banks that benefit most may not necessarily be those with the largest AI budgets. They may be the institutions that can connect their existing data to decisions that directly affect deposits, retention and customer lifetime value.

Market Landscape

Retail banking AI is moving beyond experimentation toward measurable applications in customer retention, personalization and relationship growth.

The broader market is also shifting toward AI systems that can act on predictions rather than simply generate analytics. For banks, this means connecting customer data, predictive models, marketing automation and core banking infrastructure into a single operational workflow.

That shift is particularly relevant to community and regional banks. They often have substantial historical customer and transaction data but fewer resources than large national institutions to undertake major technology replacements.

NGDATA’s approach reflects the growing market for AI overlays and intelligent engagement platforms that work with existing financial infrastructure.

The competitive environment includes large technology platforms from companies such as Microsoft, Google, Salesforce and Adobe, alongside specialized banking technology providers. The differentiator is increasingly not whether a platform offers AI, but whether it can translate financial data into explainable decisions and measurable business outcomes.

For banks, the business case is also becoming more granular. Rather than evaluating AI solely on productivity gains, executives can measure outcomes such as deposit retention, product penetration, customer lifetime value and campaign conversion.

Top Insights

• Union Savings Bank is using NGDATA AI Hub to detect customer attrition risk and trigger targeted interventions without replacing its core banking infrastructure.
• Celent reports a 74% churn catch rate, showing how predictive customer intelligence can translate into measurable retention opportunities for regional financial institutions.
• NGDATA combines customer data activation, predictive models and explainable AI, helping banking teams understand why relationships are at risk.
• Targeted CD renewal campaigns reportedly achieved 90% retention, demonstrating how AI can connect customer signals directly to deposit-growth strategies.
• The deployment reflects a broader banking shift from AI experimentation toward measurable applications in retention, personalization, deposits and Customer Lifetime Value.

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