No abstract

Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in the NLP community, posing a challenge due to their tremendous number of parameters and limited robustness. In particular, deep-learning models are vulnerable to adversarial attacks: a little change in the input harms the model's output.In this work, we examine adversarial attacks on transaction records data and defences from these attacks. The transaction records data have a different structure than the canonical NLP or time series data, as neighbouring records are less connected than words in sentences, and each record consists of both discrete merchant code and continuous transaction amount. We consider a black-box attack scenario, where the attack doesn't know the true decision model, and pay special attention to adding transaction tokens to the end of a sequence. These limitations provide more realistic scenario, previously unexplored in NLP world.The proposed adversarial attacks and the respective defences demonstrate remarkable performance using relevant datasets from the financial industry. Our results show that a couple of generated transactions are sufficient to fool a deep-learning model. Further, we improve model robustness via adversarial training or separate adversarial examples detection. This work shows that embedding protection from adversarial attacks improves model robustness, allowing a wider adoption of deep models for transaction records in banking and finance.

Roughly 10 percent of the insurance industry's incurred losses are estimated to stem from fraudulent claims. One solution is to use tabular data to construct models that can distinguish between claims that are legitimate and those that are fraudulent. However, while canonical tabular data models enable robust fraud detection, complex sequential data have been out of the insurance industry's scope. For health insurance, we propose deep learning architectures that process insurance data consisting of sequential records of patient visits and characteristics. Both the sequential and tabular components improve the quality of the model, generating new insights into the detection of health insurance fraud. Empirical results derived using relevant data from a health insurance company show that our approach outperforms state-of-the-art models and can substantially improve the claims management process. We obtain a ROC AUC metric of 0.873, while the best competitor based on state-of-the-art models achieves 0.815. Moreover, we demonstrate that our architectures are more robust to data corruption. As more and more semi-structured event sequence data become available to insurers, our methods will be valuable for many similar applications, particularly when variables have a large number of categories, such as those from the International Classification of Disease (ICD) codes or other classification codes. INDEX TERMS deep learning, embeddings, fraud detection, health insurance, social media and text, structured data VOLUME 4, 2016