Data science · Wealth management
Targeting portfolio growth for financial advisers
Machine learning segmentation on sensitive client data, giving advisers targeted suggestions for portfolio expansion
The challenge
Advisers needed to know which clients had the most potential for portfolio expansion, but the data was highly sensitive and spread across systems, and targeting relied on judgement alone.
How we approached it
- Secure data handlingWork on PII-level datasets with strict data security and integrity
- Client segmentationCustom algorithms to categorise the client base for business initiatives
- ML pipelinesDataiku machine learning pipelines to identify and target customers
- Automated testingPyTest and Great Expectations to keep data accuracy high
- Adviser supportDirect work with financial advisers in North America on revenue growth
What was delivered
Segmentation and targeting pipelines advisers use to prioritise outreach, built on tested, compliant data.
Impact
BetterClient targeting from advanced segmentation
Higher data integrity and compliance, more efficient operations through automated testing, and stronger adviser performance.
More work
Related case studies
Product & data · TradeOrigin
From paper letters of credit to a digital deal platform
A trade finance platform for banks and corporates
Read the case study →AI workflow automation · EMEAFrom manual lead handling to automated marketing operations
80% less manual workload across 20+ EMEA countries
Read the case study →Analytics & BI · NestléFrom scattered HR data to workforce planning teams can act on
Workforce analytics for supply and demand planning
Read the case study →