Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.30684

PixSim: a calibrated open-source simulator of instant-payment fraud, recovery and interdiction under analyst capacity constraints

Bashir Zeimarani·Alireza Khatib·Somayeh Mousavinasr·Carlos Maurício Serodio Figueiredo
View original source ↗Read source document ↗Export metadata ↓

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: https://doi.org/10.5281/zenodo.22948895

Explore related subjects

cs.LGcs.CEq-fin.RM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bashir Zeimarani, Alireza Khatib, Somayeh Mousavinasr, Carlos Maurício Serodio Figueiredo. 2026-09-25. PixSim: a calibrated open-source simulator of instant-payment fraud, recovery and interdiction under analyst capacity constraints. https://arxiv.org/abs/2609.30684

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers

We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method searches for an interpretable conjunction of feature--threshold rules that corrects residual false-positive or false-negative errors while explicitly constraining newly introduced errors. The approach combines graph-based search over candidate rule paths, depth-dependent dynamic constraints, and a reduced-histogram procedure for efficient threshold evaluation. Unlike retraining or modifying the base classifier, the learned correction path operates on its predictions and can therefore be applied to arbitrary binary classifiers with suitable input features. Experiments on a large binary-classification problem demonstrate that the method can identify compact correction rules efficiently; for example, one configuration removes 90\% of false positives while sacrificing 5\% of true positives.

cs.LG↗
arXiv2026

Stochastic Bilevel Optimization with Heavy-Tailed Noise

This paper considers the smooth bilevel optimization in which the lower-level problem is strongly convex and the upper-level problem is possibly nonconvex. We focus on the stochastic setting where the algorithm can access the unbiased stochastic gradient evaluation with heavy-tailed noise, which is prevalent in many machine learning applications, such as training large language models and reinforcement learning. We propose a nested-loop normalized stochastic bilevel approximation (N$^2$SBA) for finding an $ε$-stationary point with the stochastic first-order oracle (SFO) complexity of $\tilde{\mathcal{O}}\big(κ^{\frac{7p-3}{p-1}} σ^{\frac{p}{p-1}} ε^{-\frac{4 p - 2}{p-1}}\big)$, where $κ$ is the condition number, $p\in(1,2]$ is the order of central moment for the noise, and $σ$ is the noise level. Furthermore, we specialize our idea to solve the nonconvex-strongly-concave minimax optimization problem, achieving an $ε$-stationary point with the SFO complexity of~$\tilde{\mathcal O}\big(κ^{\frac{2p-1}{p-1}} σ^{\frac{p}{p-1}} ε^{-\frac{3p-2}{p-1}}\big)$. All the above upper bounds match the best-known results under the special case of the bounded variance setting, i.e., $p=2$. We also conduct the numerical experiments to show the empirical superiority of the proposed methods.

cs.LG↗
arXiv2026

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. By adapting variants of Legendre Memory, i.e., translated Legendre (LegT) and scaled Legendre (LegS), in the Encoding and Decoding phases, FLAME can effectively capture the inherent inductive bias within data and make efficient long-range inferences. To enhance the accuracy of probabilistic forecasting while remaining efficient, FLAME adopts a normalizing-flow-based forecasting head, which can model complex distributions over the forecasting horizon in a generative manner. Comprehensive experiments on three well-recognized benchmarks, including TSFM-Bench, ProbTS, and TFB, demonstrate that FLAME is a strong out-of-the-box tool for decision intelligence.

cs.LG↗