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Financial ServicesSolution

Loan Conversion Optimization

Lift loan booking rates with better targeting and a personalized contact strategy across the funnel (conversion, not approval speed).

Solution
Machine Learning
Industry
Financial Services
Company Size

Solution Overview

industry
Financial Services
solution
Machine Learning
technologies
Azure Machine Learning, Microsoft Fabric, Dynamics 365 Customer Insights, Power Automate, Power BI, Azure OpenAI

The Challenge

Banks generate large volumes of pre-approved and pre-qualified offers that never convert, because targeting is based on eligibility rather than intent and outreach happens on the campaign calendar rather than at the moment the customer is receptive.

  • 1Pre-approved offers targeted on eligibility rather than intent, so most never convert
  • 2Outreach timed to the campaign calendar, not to customer receptiveness
  • 3Application abandonment mid-journey with no systematic recovery

Our Approach

CloudGate models conversion likelihood across the full funnel (offer response, application start, completion and drawdown), and recommends the contact strategy, channel and timing most likely to convert each customer.

  • Conversion-likelihood scoring at each funnel stage from offer to drawdown
  • Optimal contact timing and channel recommendation per customer
  • Automated recovery journeys for abandoned applications

Typical Outcomes

+15-30%
LOAN BOOKING RATE
Optimized
CONTACT CHANNEL AND TIMING
Lower
ACQUISITION COST PER BOOKED LOAN

Typical outcomes for this solution pattern, not the results of a named client engagement.

Technologies Used

Azure Machine LearningMicrosoft FabricDynamics 365 Customer InsightsPower AutomatePower BIAzure OpenAI
Full architecture and data flow in the Data & AI Catalog

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