Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph

• URL: http://arxiv.org/abs/2604.23494v1
• Date: Sun, 26 Apr 2026 01:54:11 GMT
• Title: Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph
• Abstract summary: Graph-based anti-money laundering systems can score suspicious activity at two granularity levels -- transactions or actor addresses -- yet compliance action is conducted per actor.<n>This paper contributes an evaluation methodology for measuring how scoring granularity affects investigation queue composition under fixed review budgets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based anti-money laundering (AML) systems on blockchain networks can score suspicious activity at two granularity levels -- transactions or actor addresses -- yet compliance action is conducted per actor. This paper contributes an evaluation methodology for measuring how scoring granularity affects investigation queue composition under fixed review budgets. We formalize the evaluation through a projection framework mapping transaction-level scores to the actor-level action unit via four aggregation operators, and introduce budgeted investigation metrics -- yield@budget, burden decomposition, and case fragmentation. Using the public Elliptic++ Bitcoin dataset (203,769 transactions; 822,942 address occurrences), we train independent random forest classifiers at each level under a causal temporal protocol and compare review queues through Jaccard overlap, burden decomposition, and feature-matching ablations. At one-percent budget, temporal evaluation yields mean Jaccard of 0.374 (SD 0.171); static pooled evaluation yields 0.087 (95% CI [0.079, 0.094]). An enriched address model receiving all 237 features produces even lower overlap (Jaccard=0.051), with 4.3% illicit per 100 reviews versus 30.2% for the transaction-projected queue. Address-level detection value is temporally concentrated: two timesteps exceed 91% illicit per 100 reviews while the static burden is only 3.4%. A fixed hybrid policy underperforms the best single-level queue by 5.05pp (CI [-10.2pp, -0.9pp]). These findings establish that scoring granularity is a consequential design variable for AML investigation systems -- same data, same budget, different queues, different addresses investigated.

Related papers

• KONTOGRAPH: Verified Point-in-Time Feature Consistency and Amortised Explanation for Real-Time Anti-Money Laundering under a 200 ms Decision Budget [0.0]
  Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock.<n>This removes both the overnight batch window in which anti-money-laundering (AML) analytics traditionally ran and the settlement delay that made recovery possible.<n>We present KONTOGRAPH, an end-to-end AML pipeline for the SEPA Instant rail built under a self-imposed 200 ms 99th-percentile budget.
  arXiv  Detail & Related papers  (2026-08-23T12:28:26Z)
• Beyond Aggregate Calibration: Decomposing Income-Conditional Recall Disparities in Automated Credit Default Prediction [0.0]
  High-income defaulters are disproportionately classified as label noise relative to low-income defaulters.<n>A 16.86 percentage point gap in true positive rate (recall) between high- and low-income borrowers who ultimately defaulted.
  arXiv  Detail & Related papers  (2026-08-08T15:57:15Z)
• Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction [55.11480729304395]
  We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and ranks second on the benchmark.<n>The ap proach targets three failure modes. First, agent-driven syn thesis builds large semantic and anchor-relative candidate pools.<n>Second, a determinis tic steerable-data pipeline creates a verified 10,000-sample main set, plus reserve samples, using masks, templates, and path verification.
  arXiv  Detail & Related papers  (2026-06-29T06:39:03Z)
• Metric Aggregation Divergence: A Hidden Validity Threat in Agent-Based Policy Optimization and a Contractual Remedy [7.1976264551575895]
  Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model.<n>We introduce the metric contract - a single callable runtime enforced at dispatch time - as the remedy.
  arXiv  Detail & Related papers  (2026-06-27T18:17:33Z)
• Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness [45.148328075418156]
  A deployed ordering agent is decomposed into a fixed taxonomy of layers.<n>The pure suite runs in CI on every change against a locked per-slice baseline.<n>We validate by controlled regression injection, degrading one layer at a time across seven non-safety layers.
  arXiv  Detail & Related papers  (2026-06-10T05:55:13Z)
• Compliance-Scored Best-of-N Guardrail Orchestration for Multimodal Document Generation in Payments Dispute Defense [0.0]
  High-stakes enterprise document generation includes financial dispute narratives, compliance notices, and audit summaries.<n>Prior to a unified guardrail layer, production systems often stitched together separate PII redaction, content moderation, and format validation steps.<n>We present a guardrail orchestration layer for text and image inputs that couples multi-candidate generation with an explicit compliance score used for early exit.
  arXiv  Detail & Related papers  (2026-06-01T00:24:30Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback [1.2362187555287152]
  Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors.<n>We propose a behavioral forecast-evaluation methodology that complements accuracy tests by assessing the intermediate decision process itself.
  arXiv  Detail & Related papers  (2026-05-07T06:31:34Z)
• AgentEval: DAG-Structured Step-Level Evaluation for Agentic Workflows with Error Propagation Tracking [13.891522069967507]
  We present AgentEval, a framework that formalizes agent executions as evaluation directed acyclic graphs (DAGs)<n>AgentEval achieves 2.17x higher failure detection recall than end-to-end evaluation and 72% root cause accuracy against an 81% human ceiling.
  arXiv  Detail & Related papers  (2026-04-26T07:38:47Z)
• Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems [6.14481021961242]
  We present a statistical framework for multi-agent pipelines structured as directed acyclic graphs (DAGs)<n>We model each agent as a categorical decision and introduce tighter agent-level performance confidence bounds.<n>We evaluate our system on two labeled datasets in behavioral health.
  arXiv  Detail & Related papers  (2026-04-24T01:52:54Z)
• Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode [9.116800340266066]
  Claude Code's auto mode is the first deployed permission system for AI coding agents.<n>Anthropic reports a 0.4% false positive rate and 17% false negative rate on production traffic.<n>We evaluate 253 state-changing actions at the individual action level against oracle ground truth.
  arXiv  Detail & Related papers  (2026-04-04T17:56:30Z)
• On Randomness in Agentic Evals [6.177270420667714]
  Agentic systems are evaluated on benchmarks where agents interact with environments to solve tasks.<n>Most papers report a pass@1 score computed from a single run per task, assuming this gives a reliable performance estimate.<n>We find substantial variance: single-run pass@1 estimates vary by 2.2 to 6.0 percentage points depending on which run is selected.
  arXiv  Detail & Related papers  (2026-02-06T19:49:13Z)
• Almost Asymptotically Optimal Active Clustering Through Pairwise Observations [59.20614082241528]
  We propose a new analysis framework for clustering $M$ items into an unknown number of $K$ distinct groups using noisy and actively collected responses.<n>We establish a fundamental lower bound on the expected number of queries needed to achieve a desired confidence in the accuracy of the clustering.<n>We develop a computationally feasible variant of the Generalized Likelihood Ratio statistic and show that its performance gap to the lower bound can be accurately empirically estimated.
  arXiv  Detail & Related papers  (2026-02-05T14:16:47Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
  Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
  arXiv  Detail & Related papers  (2025-09-25T13:04:29Z)
• AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions [3.1459290341742676]
  AMLNet is a knowledge-based multi-agent framework with two coordinated units: a regulation-aware transaction generator and an ensemble detection pipeline.<n>The generator produces 1,090,173 synthetic transactions spanning core laundering phases and advanced typologies.<n>The detection ensemble F1 0.90 adapts to the external SynthAML dataset, indicating architectural generalizability.
  arXiv  Detail & Related papers  (2025-09-15T05:25:46Z)
• Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference [44.644520116360106]
  We estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control.<n>We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks.<n>We show that for graphs that admit good clustering, a truncated Horvitz-Thompson estimator achieves a $tilde O(1/NT)$ mean squared error (MSE)<n>Our results simultaneously generalize the results from citethu2022switchback,ugander2013graph and citetleung2022rate
  arXiv  Detail & Related papers  (2023-12-25T01:00:58Z)
• Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation [45.74830585715129]
  Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas. But when those methods are applied to non-controllable environments, many different factors can degrade the model's expected performance. We propose a novel way to improve OSDA approaches by extracting a high-confidence set of unknown instances.
  arXiv  Detail & Related papers  (2023-09-16T11:33:40Z)
• Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning [43.51182049644767]
  Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. Regularization-based methods which force the perturbed samples to have similar predictions with the original ones have attracted much attention. We propose a novel contrastive loss to guide the embedding of the learned network to change linearly between samples.
  arXiv  Detail & Related papers  (2022-02-24T06:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.