Realistic Synthetic Financial Transactions for Anti-Money Laundering Models

With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different Graph Neural Networks in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected

Automated Instruction-Set Extension

NOVELTY - The method involves traversing a tree of potential complex computer operations; and pruning the tree for optimality under constraints. The optimality comprises maximization of a function of merit. The constraints comprise a convexity constraint, a maximum-input-multiplicity constraint, or a maximum-output-multiplicity constraint. USE - For determining complex computer operation for computer application. ADVANTAGE - Enhances processing performance by automatically forming extensions from high-level application code. DETAILED DESCRIPTION - An INDEPENDENT CLAIM is also included for a system for determining a complex computer operation for a computer application

Automatic Application-Specific Instruction-Set Extensions under Microarchitectural Constraints

Many commercial processors now o#er the possibility of extending their instruction set for a specific application---that is, to introduce customised functional units. There is a need to develop algorithms that decide automatically, from highlevel application code, which operations are to be carried out in the customised extensions. A few algorithms exist but are severely limited in the type of operation clusters they can choose and hence reduce significantly the e#ectiveness of specialisation. In this paper we introduce a more general algorithm which selects maximal-speedup convex subgraphs of the application dataflow graph under fundamental microarchitectural constraints, and which improves significantly on the state of the art

InternationalJournalofParallelProgramming,Vol.31,No.6,December2003(©2003) Automatic Application-Specific Instruction-Set Extensions under Microarchitectural Constraints

ThispaperpresentsamethodologyforautomaticallydesigningInstruction-Se