Empowering retail banks to rapidly generate value from their customer data
Automated Machine Learning for Retail Bank
Enabling data science teams in retail banks to rapidly deliver value by developing, deploying and managing hundreds of predictive models with proven ML architecture
Data Lake: Enabling rapid integration of data from multiple data warehouses and product factories
Algorithms and Automated Machine Learning: Partially pre-trained algorithms and advanced ML/AI technology developed and validated on over 20 million of retail banking clients
Integration: Modular tools enabling streamlined integration of ML/AI models with the existing legacy systems with minimal disruption and investment
Feature generator
Auto ML (model training)
Model interpretation
Data Lake & Reporting
Model comparison and performance analysis
Model deployment
Hyper-personalization of digital customer journeys
Hyper-personalization of digital customer journeys
Deploying ML/AI models to optimise digital actions, channels and psychological triggers for retail bank’s customers by hyper-personalization
Supported use cases
The next best action
Optimal psychological trigger
The next best channel
The optimal contact time
Message personalization
Client example
AVO Africa – a „super App” by the leading South African bank Nedbank, powered by Cantab PI autoML
Use cases
Lead generation and targeting for cash loans
Increase in banking products sold with the same marketing spend, enabled by a propensity to buy ML algorithm
Result:
Increase of the banking products sold by
18%
Preventing customer churn
Reduction of customer churn, and measurable improvement of customer satisfaction, enabled by four ML algorithms flagging likely churn by churn driver
Result:
Reduced customer churn by
20%
Optimisation of omni-channel targeting
Increase in revenue due to ML-driven optimisation of sales channels and messages
Result:
Increase in revenue
23%
Accurate credit scoring on current account (PSD2) data only
Deployed behavioral credit scoring models using only data from current account transactions of the client, with performance superior to the legacy models relying also on credit history.
Result:
Gini coefficient of the developed credit scoring model