Data + Tech Together:
How Banks Serve Clients Better with AI
AI use in financial services is no longer experimental. Most organizations report regular generative AI use, and investment is rising fast. This post distills the key points from our recent Fireside chat with TAZI’s CEO & Co-Founder Zehra Cataltepe and Jamie Scott Berniker, former Banking Executive & advisor on Growth and Client Experience. You will get a clear view: where to start, how to measure results, and how to stay compliant.
“Design outreach that is timely, relevant, and engaging. Make customers feel you are looking out for them, not pushing a sale.” — Jamie Scott Berniker
“Tie models to clear actions and KPIs. Document decisions. Keep explanations visible so teams can take responsibility.” — Zehra Cataltepe
Start with client impact, not tools
Use cases that help customers win also help your bank. Pick one “moment that matters” and design the flow from signal to action and outcome. Examples you can deliver quickly:
- Dormant or at-risk account outreach with next best action
- Complaint triage and ownership for faster resolution
- Advisor prompts based on life-event signals
See how we approach banking outcomes on our Banking and Client Acquisition pages.
Why this order works
You avoid long detours in data plumbing. You focus your AI readiness checklist for financial services on the minimum viable data set that proves lift. You also reduce privacy risk by starting with data-minimized patterns.
What works, and what does not
Works: anomaly detection for fraud prioritization, Voice-of-Customer summarization, advisor-ready recommendations, targeted retention and deposit triggers.
Does not work: black-box scores with no explanation, “data lake-first” projects, chatbots without workflow handoff, pilots without a single KPI or owner.
Reusability matters. Create simple templates for the KPI, data, and frontline actions. Then reuse them in next use cases.
Data quality and governance you actually need
Poor data quality is expensive and slows delivery. Treat quality as part of the work, not a separate project. Use a short, repeatable checklist: required fields, recency, join keys, redaction rules, and a sampling plan. Gartner has long flagged the cost of bad data, which is why you should bake quality checks into weekly review cycles.
Create an audit-ready “mini governance pack” per use case:
- Model card with purpose, data sources, features, and known limits
- Data lineage and retention
- Control checklist for privacy and access
- Explainability panel visible to business, risk, and audit
Regulators are elevating transparency expectations. The EU AI Act formalizes explainability and transparency for higher-risk systems, which makes your documentation and model explanations even more important.
For a sense of how we operationalize explainability and Adaptive AI, visit our Gartner recognitions page.
A simple 30/60/90 plan
Day 0 to 30: Access data, define one business KPI, ship first insights to a small user group. Keep PII out if you can.
Day 31 to 60: Embed insights in the workflow. Log user actions. Compare cohorts.
Day 61 to 90: Report lift versus baseline. Decide to scale. Queue the next two use cases using the same pattern.
This cadence fits today’s environment. McKinsey’s State of AI 2025 reports that nearly two-thirds of organizations have not started scaling AI across the enterprise, and only 39% report EBIT impact at the enterprise level
Why investment is accelerating
Banks are scaling AI to improve customer experience and productivity. According to PwC’s 2024 report, “Graph LLMs: The Next AI Frontier in Banking and Insurance Transformation,” bank spending on generative AI is projected to grow from about 6 billion dollars in 2024 to around 85 billion dollars by 2030. This reflects a shift from pilots to production. For leaders, standardize your data governance in banking and DataOps best practices for banks now, not later.
Watch the full Fireside Chat below.
Also, see here 4 Financial Services Use Cases about AI, Business & Compliance KPI’s
FAQ
What is a minimum viable data set for a first use case?Only the fields needed to trigger one action and measure one KPI. Start small to move faster.
How do I keep personalization non-creepy?
Ask short, tailored questions and let customers confirm context before you act. Log consent and make value clear.
How do I protect customer data privacy? Where do I host the models and data?
Keep data and models behind your firewall when possible. Limit external transfers. Use clear access controls and logs.
Which KPI should I pick first?
Choose one outcome tied to revenue or cost. Examples. churn reduction, faster complaint resolution, or deposit growth.
How do I avoid pilot purgatory?
Pre-agree success thresholds at kickoff. Assign an executive sponsor and a frontline owner. Set weekly reviews.