Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering

11/03/2020
by   Daniel Borrajo, et al.
6

Many business applications involve adversarial relationships in which both sides adapt their strategies to optimize their opposing benefits. One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities. In this paper, we present a novel way of approaching these types of applications, in particular in the context of Anti-Money Laundering. We provide a mechanism through which diverse, realistic and new unobserved behavior may be generated to discover potential unobserved adversarial actions to enable organizations to preemptively mitigate these risks. In this regard, we make three main contributions. (a) Propose a novel behavior-based model as opposed to individual transactions-based models currently used by financial institutions. We introduce behavior traces as enriched relational representation to represent observed human behavior. (b) A modelling approach that observes these traces and is able to accurately infer the goals of actors by classifying the behavior into money laundering or standard behavior despite significant unobserved activity. And (c) a synthetic behavior simulator that can generate new previously unseen traces. The simulator incorporates a high level of flexibility in the behavioral parameters so that we can challenge the detection algorithm. Finally, we provide experimental results that show that the learning module (automated investigator) that has only partial observability can still successfully infer the type of behavior, and thus the simulated goals, followed by customers based on traces - a key aspiration for many applications today.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

11/03/2020

Domain-independent generation and classification of behavior traces

Financial institutions mostly deal with people. Therefore, characterizin...
11/01/2020

Heuristic-based Mining of Service Behavioral Models from Interaction Traces

Software behavioral models have proven useful for emulating and testing ...
08/27/2021

Superstring-Based Sequence Obfuscation to Thwart Pattern Matching Attacks

User privacy can be compromised by matching user data traces to records ...
03/16/2021

Generation of Realistic Cloud Access Times for Mobile Application Testing using Transfer Learning

The network Quality of Service (QoS) metrics such as the access time, th...
02/23/2019

Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

Process Mining is a famous technique which is frequently applied to Soft...
03/12/2019

Probabilistic Temporal Logic over Finite Traces (Technical Report)

Temporal logics over finite traces have recently gained attention due to...
03/26/2020

Adversarial System Variant Approximation to Quantify Process Model Generalization

In process mining, process models are extracted from event logs using pr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.