Banks can now tap IBM Watson to fight financial crime


Who will be the first to implement the new suite of Watson services? From the newly formed Watson Financial Services division, IBM has released the first suite of services covering regulatory requirements, financial crime insights, and financial risk modelling. These cognitive tools have been made possible following IBM's 2016 acquisition of global consulting operation, Promontory Financial Group. Promontory was originally working to provide support to banks dealing with the growing and tightening regulation and risk management within the financial services. It was the knowledge and expertise accessed in this acquisition that brought life to the new financial services-focussed Watson services, with regulation and risk accounting for two thirds of the suite, and a financial crime tool completing the set.

Towards Applying Interactive POMDPs to Real-World Adversary Modeling

AAAI Conferences

We examine the suitability of using decision processes to model real-world systems of intelligent adversaries. Decision processes have long been used to study cooperative multiagent interactions, but their practical applicability to adversarial problems has received minimal study. We address the pros and cons of applying sequential decision-making in this area, using the crime of money laundering as a specific example. Motivated by case studies, we abstract out a model of the money laundering process, using the framework of interactive partially observable Markov decision processes (I-POMDPs). We address why this framework is well suited for modeling adversarial interactions. Particle filtering and value iteration are used to solve the model, with the application of different pruning and look-ahead strategies to assess the tradeoffs between solution quality and algorithmic run time. Our results show that there is a large gap in the level of realism that can currently be achieved by such decision models, largely due to computational demands that limit the size of problems that can be solved. While these results represent solutions to a simplified model of money laundering, they illustrate nonetheless the kinds of agent interactions that cannot be captured by standard approaches such as anomaly detection. This implies that I-POMDP methods may be valuable in the future, when algorithmic capabilities have further evolved.

Analytics, Security, Deep Learning, IoT, Data Science Online Courses


Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

Multiple perspectives HMM-based feature engineering for credit card fraud detection Artificial Intelligence

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.

Deep learning: the next frontier for money laundering detection


Monitoring transactions for suspicious ones can be more efficient. Up to $2 trillion dollars representing 5% of global GDP – that's the estimated amount of money laundered worldwide each year according to the United Nations Office on Drugs and Crime. The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand. And even then, the good guys have a hard time winning.