  Machine learning plays an essential role in preventing financial losses in
the banking industry. Perhaps the most pertinent prediction task that can
result in billions of dollars in losses each year is the assessment of credit
risk (i.e., the risk of default on debt). Today, much of the gains from machine
learning to predict credit risk are driven by gradient boosted decision tree
models. However, these gains begin to plateau without the addition of expensive
new data sources or highly engineered features. In this paper, we present our
attempts to create a novel approach to assessing credit risk using deep
learning that does not rely on new model inputs. We propose a new credit card
transaction sampling technique to use with deep recurrent and causal
convolution-based neural networks that exploits long historical sequences of
financial data without costly resource requirements. We show that our
sequential deep learning approach using a temporal convolutional network
outperformed the benchmark non-sequential tree-based model, achieving
significant financial savings and earlier detection of credit risk. We also
demonstrate the potential for our approach to be used in a production
environment, where our sampling technique allows for sequences to be stored
efficiently in memory and used for fast online learning and inference.

Risk Management;Machine Learning