Building Data-Driven Client Relationship Management in Banking with Python

Meeting a banking client's needs with machine learning across multiple sales channels

Paul Hughes

Business Cases Data Science Machine-Learning Scientific Libraries (Numpy/Pandas/SciKit/...) Windows

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.

The Challenge
- 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 subjective.
- 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 analytics
- Pandas and Jupyter for development and collaboration
- Luigi pipeline for daily processing of millions of transactions and engineering features
- 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

Type: Talk (30 mins); Python level: Intermediate; Domain level: Intermediate

Paul Hughes

Credit Suisse

Paul Hughes leads the Data Science and Analytics team in Credit Suisse's Private Banking and Wealth Management division where he is responsible for delivering Advanced Analytics solutions globally.
Prior to Credit Suisse he worked as a quant portfolio manager in Hedge Fund Asset Management in Zurich before founding his own Data Science consultancy.
At the beginning of his career, he was part of a successful renewable energy start-up.
Paul's background is in Physics and Mathematical Finance.