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Data-Driven Customer Relationship Management bin Banking with Python

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Data-Driven Customer Relationship Management bin Banking with Python
Subtitle
Meeting a banking client's needs with machine learning across multiple sales channels
Alternative Title
Building Data-Driven Client Relationship Management in Banking with Python
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118
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CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
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Abstract
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! Solution - 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
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