Fair Isaac Corporation

Heming Xu

Scott Michael Zoldi

Yuting Jia

Kiyoung Yang

A method for detecting fraud and non-fraud pattern changes can be based on transaction pathway transversal analysis. A decision tree can be built based on a training dataset from a reference dataset. Pathway transversal information can be recorded along each pathway for the reference dataset. A first mean and a first variance of a class probability can be calculated of all samples over each pathway. A pathway distribution for a new transaction dataset under investigation and a second mean and a second variance of all samples of the new transaction dataset can be obtained. The second mean and the second variance can represent a fraud probability. The deviation metrics between one or more feature statistics of a feature along each pathway for the reference dataset and the new dataset can be determined on a local level. Feature contributors to pattern changes can be determined by analyzing the deviation metrics.

Tree pathway analysis for signature inference

Computer implementation methods of processing transactions to determine the fraud risk of transactions incorporating card issuer bin and cardholder location associated with a multitude of customers. The artificial intelligence models developed with such information provide an output of likelihood of fraud for payment card transactions. Disclosed are the methods of utilizing aggregated payment card transaction data at the card issuer bin and card holder location level to improve fraud detection. The implementation of the method is demonstrated to have boosted the performance of the developed models in detection of fraudulent payment cards.

Fraud detection by profiling aggregate customer anonymous behavior

The current subject matter describes a method and system of detecting frauds or anomalous behavior. The procedures include extracting characteristics from a dataset to generate words and documents, executing a topic model to obtain the respective probabilities of appearance of a document in each latent archetype, dividing the dataset into a plurality of subsets based upon the archetypes. The formed subsets are further utilized to estimate the quantiles and calculate scores using a self-calibrating outlier model. The score of each new transaction is determined based on a single archetype or based on the sum of weighted scores determined from all the archetypes and associated statistics. Such methods are superior to a simple self-calibration outlier model without an LDA archetype. The detection system with the LDA archetypes and self-calibrating outlier model is implemented with the sliding window technique incorporating new transactions into the topic model and it is capable of operating in real-time for the purpose of identifying frauds and outliers.

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Heming Xu