Client background: A global expert in data capture, categorisation and structure sectors
To generate insights for consumer transaction data like consumer’s credit, earning and spending behaviors
Identifying preferred customer channel and devising personalized marketing strategies
Build predictive analytics around Propensity to spend, Propensity to pay a loan, Propensity to default, Suburb level income and spending levels and patterns. Facilitate better results than the current categorization engine
Categorizing Financial Transaction data of its customers achieve maximum accuracy level (match rate, match accuracy and false positives)
Performed exploratory analysis on data
Utilized ML and AI to come up with Behavioral Insights/ KPIs
Used clustering, Regression / Time Series and Association Mining for model building, based on KPIs ( such as Propensity to Pay). Leveraged classification technique using Recurrent Neural Network (RNN), Naive Bayes Classifier and logistic regression for data categorization
Created visualizations for exploratory data as well as inferences
Generated behavioral Insights based on KPIs such as insights covering Customer’s Propensity to Spend or Customer Segmentation.
Identified main channels of transactions (ATM withdrawal, credit/debit card payments, using Classification algorithms)
Rolled out personalized marketing; product cross-selling based on customer segmentation
Unearthed correlations in data and produced accurate output as to the categorisation group of data; improved accuracy levels
Python / R
JSON/CSV for data.