Millennium bcp targets personal loan customers with BigQuery machine learning
Millennium bcp, Portugal's largest private bank, used BigQuery's machine learning tools, Google Analytics 4 and Firebase to analyze behavioral patterns of existing customers and build predictive models segmenting clients by loan propensity, achieving a 2.6x higher conversion rate and 2.4x greater conversion volume in owned media, a doubling of paid media conversion volume with 1.9x higher conversion rates, and a 36% drop in cost per acquisition.
Overview
Millennium bcp, Portugal's largest private bank, used BigQuery's machine learning tools, Google Analytics 4 and Firebase to analyze behavioral patterns of existing customers and build predictive models segmenting clients by loan propensity, achieving a 2.6x higher conversion rate and 2.4x greater conversion volume in owned media, a doubling of paid media conversion volume with 1.9x higher conversion rates, and a 36% drop in cost per acquisition.
This entry has 14 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Millennium bcp's personal loan applications relied on physical branch visits with limited hours and lengthy manual processes, which restricted scalability and fell short of customers' expectations for seamless, personalized digital service.
The solution
Millennium bcp's in-house digital marketing team used BigQuery's machine learning tools to analyze behavioral patterns of existing loan customers and build predictive propensity models segmenting users into low, medium and high interest tiers, then activated those segments as tailored audiences through Google Analytics 4, Firebase (personalized in-app messaging) and Display & Video 360 (targeted paid campaigns).
Reported business value
BigQuery-powered audiences delivered 2.6x higher conversion rates and 2.4x greater conversion volume than other first-party audiences in owned media; in paid media, conversion volume doubled with 1.9x higher conversion rates, and cost per acquisition dropped 36%.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other financial services entries in the register.
Navy Federal Transforms Service With AI
Navy Federal Credit Union is reshaping banking for military members by unifying data and leveraging generative and agentic AI on the Databricks Data + AI Platform. By embracing AI-augmented workflows and upskilling teams, Navy Federal delivers customized services while streamlining productivity through responsible change management and data readiness.
Lloyds Banking Group cuts mortgage income verification from days to seconds with ML
Lloyds Banking Group, the UK's largest digital bank, migrated 15 modelling systems from on-premise infrastructure to Google Cloud's Vertex AI (now Agent Platform), giving over 300 data scientists and AI developers scalable machine learning capabilities. In six months the bank ran 80 new ML experiments and launched 18+ GenAI systems into production, including an algorithm that reduces the income verification step in mortgage applications from days to seconds. The migration also cut unplanned ML platform downtime to zero and saved 27 CO2 tonnes of operational emissions.
Banking Innovator bunq Supports Growth, Strengthens Security Using AWS
bunq, a Dutch neobank with over 11 million users across Europe, uses Amazon Bedrock for several generative AI use cases including summarizing new user data with large language models, removing the need for agents to process onboarding documents manually. Using Amazon Bedrock, bunq tripled user support process efficiency while maintaining over 90 percent accuracy. Sensitive data stays within bunq's AWS virtual private cloud, supporting GDPR and PCI DSS compliance alongside tools such as AWS CloudHSM, AWS Security Hub and AWS KMS.
TBC Bank Operationalizes Trusted Data with Lakebase
TBC Bank, the largest banking group in the Caucasus region, built a Lakehouse on Databricks and adopted Lakebase and Databricks Apps to move from on-premises SQL Server instances and month-long reporting cycles to self-service analytics and AI-driven applications, including a web-based AI chatbot and AutoML-based credit risk scoring. Credit risk model deployment fell from 14 weeks to two days, and more than 600 users regularly query governed data through Genie.
Was this helpful?
Your feedback helps us improve our use case database

