This is a case study that documents how a small data science team in a
big bank took on the challenge to transform a fragmented sales process
into a data-driven one using Python and machine learning.
This talk outlines the various ways Python has been instrumental in
delivering a production solution that serves advisers and relationship
manager on a continuous basis.
- A bank has many clients with diverse needs and cost pressures mean
fewer advisers resulting in reduced client coverage.
- Multiple sales channels and mixed service levels meant sales
processes were uncoordinated and driven by heuristics and often very
- And... Excel sheets everywhere!
- Go data-driven!
- Learn from clients and understand product usage
- Empower and inform advisers and call centre agents
- Build a front-to-back sales process (no more Excels!)
- How? With Python!
The Python Bits
- Scikit learn machine learning pipelines that implement two distinct
approaches to product affinity in banking and wealth management
- SQL Alchemy based API for data engineering and rapid prototyping of
- Pandas and Jupyter for development and collaboration
- Luigi pipeline for daily processing of millions of transactions and
- Extracting features from text with NLP (Spacy)
- Delivering machine learning interpretability in production, e.g.
with Random Forests and treeinterpreter
- A Python module that we built with all the reusable bits: building
training and prediction datasets, developing pipelines, generating
monitoring data and enabling explainability