  This study addresses the actual behavior of the credit-card fraud detection
environment where financial transactions containing sensitive data must not be
amassed in an enormous amount to conduct learning. We introduce a new adaptive
learning approach that adjusts frequently and efficiently to new transaction
chunks; each chunk is discarded after each incremental training step. Our
approach combines transfer learning and incremental feature learning. The
former improves the feature relevancy for subsequent chunks, and the latter, a
new paradigm, increases accuracy during training by determining the optimal
network architecture dynamically for each new chunk. The architectures of past
incremental approaches are fixed; thus, the accuracy may not improve with new
chunks. We show the effectiveness and superiority of our approach
experimentally on an actual fraud dataset.
