PixSim: a calibrated open-source simulator of instant-payment fraud, recovery and interdiction under analyst capacity constraints
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Computer Science > Machine Learning
arXiv:2609.30684 (cs)
[Submitted on 25 Sep 2026]
Title:PixSim: a calibrated open-source simulator of instant-payment fraud, recovery and interdiction under analyst capacity constraints
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Abstract:Brazil's Pix settles about 5.9 billion instant, irreversible transfers a month. A fraudulent transfer can be recovered only while the funds remain in a traceable account, and in 2025 the Central Bank's recovery mechanism (MED) returned 9% of accepted contested value. Interdiction therefore has to happen before settlement, by routing each transaction to pass, human review or block, under a finite analyst team and a regulatory hold window. To our knowledge no public simulator jointly models irreversible settlement, a regulated recovery mechanism, downstream fund dispersal and capacity-constrained review. We present PixSim, an open-source simulator of the Pix rail with these elements, calibrated to Banco Central do Brasil open data, with every parameter sourced, calibrated to one published observable, or registered as an assumption. With the model frozen, full-scale runs reproduce the 2025 recovery rate within 0.006 and its decomposition within 0.02; the February-April 2026 window is reported as a misfit and the May 2026 tracing regime as a projection. On a benchmark with a payer-side scorer, four reference policies and ten scenarios, within the simulated mule model: recovery after settlement is constrained by dispersal speed; staffing by the arrival profile cuts a fixed rule's alert expiry from 52% to 2% at constant hours; halving the team removes a fixed threshold-and-block rule's advantage over a queue-aware rule, on loss and on loss plus false-block harm (+0.106 of victim value, positive on all twenty paired seeds), while a reversal at two thirds of the team was not confirmed on independent seeds; and a synthetic scorer of held-out AUC 0.82 cuts lost value by about a quarter. Code and data: this https URL
| Comments: | 25 pages, 12 tables, 1 figure. Code: this https URL |
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Risk Management (q-fin.RM) |
| Cite as: | arXiv:2609.30684 [cs.LG] |
| (or arXiv:2609.30684v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30684
arXiv-issued DOI via DataCite (pending registration)
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