Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Deep learning applied to consumer transactions with Think Big Analytics

Eric Greene (Think Big Analytics)
11:05am11:45am Wednesday, June 28, 2017
Implementing AI
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Deep Learning, Financial services
Average rating: **...
(2.67, 3 ratings)

Prerequisite Knowledge

  • An intermediate understanding of neural networks and machine learning terminology and techniques

What you'll learn

  • Compare different approaches to creating deep learning models
  • Explore common issues and problems in training and deploying LSTM networks


Deep learning networks have been shown to be useful predictive models of sequential data, such as audio, speech, and text. In particular, stacked layers of long short-term memory (LSTM) networks have markedly improved natural language processing capabilities. Stacked layers of 1D convolutions have also been shown to work well. Unfortunately, research into applying deep learning to consumer transaction data has been limited in scope.

Drawing on recent research into applying deep learning to a wider range of sequential data types, Eric Greene compares different approaches to creating models that predict payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services, outlining the data requirements, feature modeling, and neural network architectures that work best, as well as common issues in training and deploying deep learning networks. You’ll learn how to develop predictive models leveraging deep learning and terabytes of transaction data using LSTM and 1D convolutions.

Photo of Eric Greene

Eric Greene

Think Big Analytics

Eric Greene is a principal architect for Think Big Analytics, where he brings technical innovation to fruition by working with business and technical leaders across industries. Eric has recently focused on cognitive computing applied to different domains within financial services and has designed and developed systems for real-time fraud detection, collections customer segmentation and automated prescription, customer account balance forecasting, and internal operations anomaly detectors.