Is Your Financial Institution Maximizing Customer Spending Insights?

Relying solely on internal data for expense tracking limits your understanding of customer spending patterns. External data offers a more comprehensive view, enabling financial institutions to provide better insights and personalized advice.

Why External Data Is Key for Expense & Budget Management

By incorporating external data—such as transaction records from credit cards, payment platforms, and bank accounts—financial institutions can:

Offer personalized financial advice tailored to customers’ actual spending habits
Improve fraud detection and reduce risks with more complete data
Enhance budgeting tools by providing better expense visibility and forecasting
Drive revenue growth through better-targeted financial products

How It Works

🔹 Detect anomalies in spending patterns with advanced Anomaly Detection algorithms
🔹 Predict future spending habits using Regression Analysis and Tree-Based Models
🔹 Correlate market indicators with customer spending to provide proactive advice

Real-World Impact: Data-Driven Financial Insights in Action

In one case, Personal Capital used a holistic view of customer financial data to offer actionable insights on spending, saving, and investing. As a result, the company grew its Assets Under Management (AUM) to nearly $13.5 billion by mid-2021, with clients seeing significant improvements in managing their finances.

Maximize Financial Insights with Data-Driven Solutions

Ready to unlock deeper insights into your customers’ financial behaviors? By integrating external data, financial institutions can empower their clients to make smarter financial decisions and improve their overall experience.

📩 Want to learn how data can transform your expense management? Let’s talk!

[Contact Us]

Live Webinar

Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!