Disrupting the scammer lifecycle: A dynamically-consistent numerical analysis of a compartment model for scam-victim dynamics

TL;DR

This paper presents a novel epidemiological model for scam-victim dynamics, revealing that disrupting scammers' lifecycle is most effective in controlling scam proliferation, supported by data-driven calibration and stability analysis.

Contribution

Introduces a new compartmental model for scam dynamics, including a finite difference scheme, and identifies scammers' lifecycle as the key target for control strategies.

Findings

Scammers' lifecycle drives scam proliferation more than victim susceptibility.

Model stability is governed by the basic reproduction number $\,\mathcal{R}_0$.

Disrupting scammers' recruitment and arrest significantly reduces scam spread.

Abstract

Online deception and financial scams represent a pervasive threat in the digital age, yet a quantitative analysis and understanding of their propagation is lacking. This study introduces a novel model based on the framework of epidemiological models to describe the interaction between scammers and their victims. We propose a five-compartment deterministic model ($S-V-R-A_s-R_s$) calibrated using longitudinal data in fraud reports from the Canadian Anti-Fraud Centre. The model's theoretical properties are established, including the non-negativity of the state variables and the stability threshold defined by the basic reproduction number ($\mathcal{R}_0$). A non-standard finite difference scheme is developed for the numerical simulations to ensure dynamical consistency between the continuous deterministic model and its discrete equivalent. A key finding of the model sensitivity analysis indicates that the proliferation of scams is overwhelmingly driven by the lifecycle of scammers, their recruitment, attrition, and arrest, rather than the susceptibility of the victim population. The results of this study provide strong quantitative evidence that the most effective control strategies are those that directly disrupt the scammers' population. Overall, this study provides a crucial model for designing and evaluating evidence-based policies to combat the scourge of cybercrime.