Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
Authors: Erik Altman
Jovan Blanuša
Luc Von Niederhäusern
Beni Egressy
Andreea Anghel
Kubilay Atasu
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: Synthetic Data
Anti Money Laundering
Multi-Agent
Virtual World
GNN
TL;DR: We generate realistic financial transactions labelled for money laundering -- a case where real data generally does not know this ground truth.

Abstract: With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0 trillion dollars are laundered globally each year.  Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area.

To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets.  We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.

Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
Erik Altman' 1 Jovan Blanuša² Lue von Niederhäusern Béni Egressy Andreea Anghel² Kubilay Atasu
IBM Watson Research, Yorktown Heights, NY, USA
IBM Research Europe, Zurich, Switzerland 'ETH Zurich, Switzerland ealtman@us.. ibm. {jov, 1vn)@zurich. hemtriich.i.......... iim. com begressydethz.ch ch {aan, kat] iim.
Abstract
With the widespread digitization of finance and the increasing popularity cryp- tocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering - the movement of illicit funds to conceal their origins can bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5% of global GDP or $0.8 $2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-availant benchmark is needed for comparing models and for the advancement of the area.
To this end, this paper contributes synthetic financial transaction dataset generator and synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.
1 Introduction
Money laundering refers the movement of illicit funds conceal their origin and make them appear to come from legitimate sources. Laundering schemes often cross bank and national boundaries, producing complex transaction patterns. Even though the amount of laundering is difficult to estimate precisely, the UN puts the global figure at up to S2 trillion per year 66 as noted in the abstract.
Other, narrower, figures help illustrate the amount of laundering. The CEO of Danske Bank resigned in 2019 after it was revealed that the Estonian branch alone may have laundered $230 billion [I]. Finextra estimates that 2017 there may have been $200 billion laundering solely in U.S. online sales Bank fines can be in the billions of dollars for failing to follow stringent standards on laundering 2 As such, it is valuable have high-quality data to help build models to detect laundering. Alas, for legal and privacy reasons, real data is not generally available. Even if real data were available, its labeling inherently poor, as most laundering often goes undetected 5
Synthetic financial data is an emerging area to help address a multitude challenges with real data and the algorithms used for analysis. These challenges include privacy and differential privacy, competitive ad advattage, small sample sizes, what-if scenarios, poorly labeled real data, bias, consistent
"Thi work was performed when Béni Egressy was with IBM Research Europe.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Trans
Timedhimp
Source bank
Source Account
Torpet bank ID
Target Account
Amount
Currency
Payment
type
MAY
1400
Chocse
NAY
00:34
1:3
18 MAY 2019 16:55
950
USO
Credit card
A 2019 10.05
1
0
1200
CHF
Wire
2/10 11:34
Coedit
14 all 7019 09-37
WGI
20 2015 16.02
2500
USO
Wire
(8)
(b)
Figure 1: Financial transactions in (a) tabular format and in (b) graph format.
baseline data for regulators, and more. Realistic synthetic data can help address many of these issues but has its own challenges. For example, does synthetic data match real data and desired scenarios? Can synthetic data be constructed without access to instances of real data being imitated? We address these questions in Sections
and
Relational tables are the most common way of storing financial data. Figure D illustrates financial transactions stored in tabular format, where each row transaction that transfers financial assets from source account to a destination account, potentially held in different banks. The amount, the timestamp, the payment currency, and the payment type are also indicated for each transaction.
Graphs offer a natural representation for financial transaction data. A graph-based data representation exposes the connectivity of the underlying data objects and makes possible to extract complex patterns of interactions between them. The graph-based representation of transactions shown in Figure Th is illustrated in Figure pp. In a financial transaction graph, the nodes typically represent ac- counts and the directed edges between the accounts represent financial transactions. Several different transactions between the same two accounts can take place at different points in time as illustrated in Figure Dp. Therefore, financial transaction graphs are essentially directed multigraphs [TO] This general graph structure underlies many financial crime analysis techniques
Financial crime refers to illegal acts committed to obtain financial gain. Financial crime is often linked with suspicious account activities. For instance, in financial transaction graphs, cycle represents a series of transactions in which the money initially sent from one bank account returns back the same account; the existence of such cycles is strong indicator of money laundering (58). Figure Tb provides a clear visual depiction of cycles in money laundering, which consists of the highlighted transactions from Figure Ta. Similarly, in cryptocurrency transaction networks. criminals use sophisticated mixing and shuffling schemes to cover the traces of their activities [34]. Such schemes can usually be expressed in terms of subgraph structures, such scatter-gatt and bipartite patterns [58 55
33 46 Efficient discovery of such suspicious patterns of interactions across account holders can enable timely and cost-effective detection of criminal activities and their perpetrators.
Our contributions can be listed as follows:
A synthetic financial transaction generator, which builds multi-agent virtual world in which some of the agents are criminals with illicit income to launder. This synthetic approach provides perfect information about which transactions laundering and the specific patterns, such as cycles, that are used in each instance. This perfect information is not available in real data. Section providde additional details.
A set of realistic, standardized AML datasets that can be used for developing and bench- marking money laundering detection models. The datasets cover range of sizes and difficulties and are all made publicly available.
A set of initial experiments with Graph Neural Networks (GNNs) and with Gradient Boosted Trees (GBT) that demonstrate the use of the datasets and provide baselines for future work. Importantly, our GNN code is open source. In addition, our GBT results can be reproduced using publicly available tools. Our experimental evaluation is presented in Section 4 Observations in Section 7 Jabout how our data release is ethically sound and supports the fight against money laundering and other forms of financial crime.
2
Related Work
Despite the importance of good data for money laundering detection, legal, privacy, and competition concerns mean that real financial transaction data is not generally available [52] 31. Even in the

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Table 1: Comparison to key previous synthetic AML data.
MLDP
AMLsim AMLworld (This Work)
Do placement?
Do layering?
Do integration?
Model key patterns?
Model other patterns?
Model multiple banks?
Model multiple currencies?
Complex entity graph?
Model transfers, payments, credits, etc.? # of Transactions
Transfers only Transfers only
2,340
1.32 M
180 M 1/807 & 1/1,750
Laundering
3/5
1/762
restricted cases when anonymized data available, ground truth labels are not known and data
labeling is inherently poor since laundering often goes undetected 7 5 6
For example, one of the few studies to use "real" data from Starnini al. [66] which reports months of data from major Italian bank with 180M transactions. Although myriad laundering patterns are possible, this work looks for only two patterns (aka smurffs or motifs), and there no guarantee that all instances of these two patterns are detected. In addition, this data was made available only Starnini al. and appears not to be available for use by other investigators.
On a smaller scale, Harris et al. [44] received 2,827 transaction history summaries for 4,469 customers of Scotia Bank in Canada, with the goal of assessing which customers pose high risk for laundering.
To get around these data availability issues, there have been proposals across many domains to generate synthetic data [53] 45 44
40 61 37). However, to the best of our knowledge, existing AML efforts suffer from one or more of the following shortcomings: (i) the synthetic data generators need access to real data, which they laundering labels; or (iii) the data produced lacks important attributes of real data.
32
then mimic; (ii) the AML data lacks authoritative is-
The small set of previous synthetic AML efforts are agent-based, like ours. However, agent-based simulations can vary significantly in their approach, e.g. modeling more or less detail, or using a larger or smaller number of agents. For activities like money laundering, with only tiny fraction of illicit transactions (i.e. minority class transactions), modeling fidelity and scale matters. Table [ outlines key aspects of money laundering data. Table also notes the capabilities and differences between our approach A AMLworld, detailed in Section 3 and two of the most prominent previous efforts to generate synthetic laundering data: Money Laundering Data Production (MLDP) [8 39)
and AMLsim 63 57 9.. As Table suggests. the capabilities of AMLworld are significantly more advanced than those of its predecessors, making for more robust data sets. (Terms like placement, layering, and integration will be defined in Section [3 as will the "8 key patterns.") Higher numbers of transactions and lower laundering rates generally more realistic, and therefore more useful. The 2,340 transactions and 60% laundering rate for MLDP suggest tiny, unrealistic scenario. Section 3.3 provides additional detail about AMLworll and AMLSim and about the techniques used in AMLworld to enhance realism.
15 35 62 are powerful machine learning models specifically designed for relational data (graphs). In particular, they can also be used for financial crime detection in transaction networks. Cardoso al. [1] and Weber et al. [64] use GNNs for money laundering detection, Kanezashi et al. (28) use GNNs for phishing detection on the Ethereum blockchain, and Rao et al. [47] uses GNN to detect fraudulent transactions. Graph Substructure Network, proposed by Bouritsas et al. [4], takes ad advantage of pre-calculated subgraph pattern counts to improve the expressivity of GNNs. GNNs could also be used to count subgraph patterns, such in Chen et al. 17,. which can enable detecting patterns associated with financial crime.
GNNs 65 59
30
Synthetic Datasets
Abundant labeled data available for key domains such as images, speech, natural language process- ing, and recommendation engines. However, due concerns regarding privacy, law, and competitive
3

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intermadiste vertices
(8) Fan-out (b) Fan-in (c) Gather-scatter (d) Scatter-gather (e) Simple cycle
(f) Random
(g) Bipartite
(h) Stack
Figure 2: Laundering Patterns Modelled
advantage, little no financial transaction data is broadly available. To this end, we: (1) introduce our detailed AMLworld generator, and (2) make multiple datasets produced by AMLworld public.
3.1 Motivation for Synthetic Data
Aside from the difficulty of accessing real financial data with laundering transactions, real data has shortcomings that synthetic data can help overcome:
Individual banks see only their own transactions- not the transactions at the multitude of other institutions with which the bank's customers transact.
Ground truth labels are usually incomplete, with many instances of laundering being missed.
As a result, many AML models mislabel laundering transactions (false negatives).
Identifying complex money laundering patterns in real data requires additional work - if it is even possible for individual banks (please see the first bullet point).
The conclusive ground truth labels in synthetic data enable the precise comparison of different models. Additionally, the multiple banks in synthetic data enable measurement of how much performance would improve if banks could share their data in federated learning setup. The scenario of an individual bank can easily be simulated by filtering the synthetic data.
3.2
AML patterns
In their AMLSim generator, Suzumura and Kanezashi 57
introduced set of patterns often used in money laundering. These 8 patterns illustrated in Figure 2 Among its many capabilities outlined in Section 3.3 our AMLworld also generates these 8 patterns.
To be more precise, fan-oul pattern of vertex shown in Figure is defined by the outgoing edges of v connecting v to at least 2 different vertices [8 Analogously, a fan-in pattern, shown in Figure is connected to k > different vertices via incoming edges. A gather-scatter pattern combines a fan-in pattern with fan-out pattern of the same vertex, as illustrated in Figure 2 (55). A fan-out pattern of vertex and fan-in pattern of vertex form ceatter-gather pattern if the fan-out and the fan-in patterns connect vertices v and u, respectively, to the same set of intermediate vertices [55]. Figure 2 shows example of scatter-gather pattern with four intermediate vertices.
simple cycle pattern, shown in Figure P. is sequence of edges that starts and ends with the same vertex and visits other vertices at most once (27 [2] Figure shows cycle in the financial transaction graph, which node launders money through nodes and D. Random pattern, depicted in Figure 2 is similar to the cycle pattern in Figure Z except with random funds are not returned to their original account. As such, random can be viewed as a random walk among accounts owned controlled (e.g. through shell companies). A bipartite pattern in Figure 2 moves funds from a set of input accounts to a set of output accounts. Finally, a stack pattern in Figure 2h extends the bipartite pattern by adding additional bipartite layer.
In these cases, as with all the laundering patterns, the laundering entity owns or controls all accounts (nodes) used. For example, an entity may "control" shell companies, that appear to "own" an account. Over the years, rules have become stricter about who the Ultimate Beneficial Owner (UBO) of an account. However, accurate UBO designations can be difficult to enforce or detect. Note also that some entities own or control more nodes than others, and thus can have larger patterns or more variation in their node selection.

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AMLSim Randomly decide transoctions at each time step in accordence with histogram config files
Account Balla
Histogrem
Histogram Use Normal Transaction Flows
Single
Fan-Out
Forward
Mutual
Mil Period
Fan-In
Periodic
Account
Connectivity Histogram
(Accounts] of Out-Nodes
Histogram Use Laundering Patterns
Fan-Out
Fan-In
Cycle
Random
Max Revind
Perlod
Bipartite
Scatter-Gather
Random Graph
Stack
Gether-Scatter
Figure 3: Overview of AMLSim.
Biweekly salery
B
Go to next time step
Criminol Entity?
Daily meal
Monthly interest
tronsaction
Go to next
Yearly
time step
Ctc
Includes time of-day, day-of-week, day-of-monthh
Theft, smuggling, extortion, etc
Yes
Yes
illicit Funds?
Layer illicit
Which store to buy items?
funds
Select
Which company supplier?
accounts
Counter-Party
Which employees pay?
AMLworld Simulation Framework
No
Etc
Have Ililcit Funds?
Decide
Salary
Monthly Rent
Need to obfuscate funds from criminal activity?
transaction
Small Purchase
amount
Etc
For each entity at each time step, follow paths
Laundering Pattern
Criminal Entity?
No
and B
Transaction
Entity be company
Yes
Choose Accounts for Pattern
Choose legit illicit funds
Criminals legit funds too.
Figure 4: Overview of AMLworld Simulation.
3.3 What is new in our AMLworld generator?
Our AMLworld generator attempts to improve on the pioneering efforts of AMLSim 57 Table I in Section 2 gives breakdown of the differences. For more detail, Figure 3 depicts AMLSim and Figure depicts AMLworld. As the figures suggest, AMLworld models much richer set of characteristics.
For example, AMLworld models the entire money laundering cycle 19] (1) Placement: sources like smuggling of illicit funds; (2) Layering: mixing the illicit funds into the financial system; and (3) Integration: spending the illicit funds.
AMLSim focuses only on (2) Layering hence the patterns described in Section Placement in AMLworld can come from sources criminal activity: extortion, loan sharking, gambling, prostitution, kidnapping, robbery, embezzlement, drugs, and smuggling. Money received and the frequency of these criminal enterprises vary by activity and by the acting entity. Illicit funds are then layered into the financial system (under A in Figure 4). Integration happens under B the bottom right of Figure 4 when criminal entities decide how to deploy their funds. AMLSim by contrast handles only layering. To support this detailed laundering model AMLworld tags illicit funds from their origin (e.g. smuggling proceeds) through all transactions used to launder those funds.
AMLworld also has more detailed modeling of population and transaction characteristics, and launder- ing activity is not limited to specific patterns. Section 3.4 outtines other key attributes AMLworld including the A.Lworld mechanisms to support "Select Counter Party" at the upper right of Figure 4
Some AML papers refer patterns smurf's motifs.
5

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Stock
Bank Account
Supplier 2
Relail Company
Employer
Person
BankkAccount
Supplier
BankkAcccunt
Bank Account
Bank Account
Figure 5: Illustration of the entity circular flow graph representing our Virtual World Model.
3.4 The Virtual World Model
At its foundation AMLworld builds a multi-agent virtual world of banks, individuals, and companies with individuals and companies buying items, and making bank transfers to get supplics, pay salaries, pay pensions, etc. The underlying model has good and bad actors, with bad actors doing things like smuggling as noted in Section 3.3
The bad actors' attempts to launder their illicit funds result in money laundelau transactions. In addition to the laundering patterns discussed above, AMLworld also makes some laundering transfers happen "naturally", e.g. criminal boss launders money by paying employees, buying supplies, etc.
The data produced is of transactions illustrated in Figure T with each transaction labeled as laundering (illicit) or not. AMLworld has been used to generate billions of transactions in a variety of currencies including dollars, euros, yuan, yen, bitcoin, and more.
The actions of all agents in our virtual world are governed by statistical distributions. Thus the model and data are not based obfuscating anonymizing real individuals, but are based on real statistics. Likewise, our model uses no seed of real transactions from which to expand. Everything is synthetic. More specifically, depicted in Figure the underlying virtual world model of A.Lworld uses a complex entity graph where different entities are connected in many ways. For example, a company serves as an employer, but may also offer retail sales or supply products or services to other companies and individuals. An individual or company may also own other companies or parts (shares) of companies. Each entity (person company) owns and via subsidiaries.
more bank accounts directly
The entity graph in Figure is essentially variant of the Circular Flow graphs used to measure GDP (Gross Domestic Product) 3] As such any financial actor (person or company) can be a (blue) major node, and each such major node financial flows between these nodes. (Flows are often bidirectional, but for simplicity. Figure 3) only subset of important flow directions.)
more bank accounts. Edges in the graph reflect
Transactions are generated based on these relationships and at appropriate frequencies. For example, salaries are typically paid weekly, biweekly, or monthly (as noted in Figure 4. Interest is almost always paid monthly. addition, a small number of laundering transactions are conducted by entities with funds from illicit activities. Since the generator knows the source of all funds, it knows which transfers are laundering.
Returning to Figure 5, the circular flow graph representing our virtual world illustrates several other important aspects of our approach:
The circular flow graph enables the generation of a fiancial transaction graph (see Figure with list of all transactions between entities during the time period of simulation.
There are multiple types of entities in the model, including individuals, corporations, part- nerships, and sole proprietors. Banks are a special type of entity in the model they are the locus of most financial transactions among the other types.
Entities such people and companies have bank accounts through which their transactions are conducted. Each person and each company may have multiple bank accounts across multiple banks. This spreading can further legitimate pursuits such as saving and spending, but can also facilitate illegal activity such as obfuscating fund sources during laundering. Ownership of companies is cross-linked, e.g. company X may own company Y in whole part (e.g. through stock). Individuals may also own companies in whole or part. This ownership pattern serves two functions for AMLworld: (1) it represents the legitimate complexity of the world, and (2) it allows layers of shell companies to be created through

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which bad actors can launder their ill- ill-gotten funds. These shell companies may have accounts arbitrary banks. c.g. their accounts may be at different banks than the primary bank accounts of their controlling entity.
The graph structure in Figure is arbitrary, e.g. the graph is not bipartite between merchants and consumers as can be the case in modeling credit card transactions [.
The AMLworld simulator parameterizes population and transaction information to facilitate: (a) tuning: (b) modeling future changes in behavior; and (c) updating to better match real behavior. For (c), authoritative real data not always available. Feedback loops AMLworld also enforce degree of tuning to make statistics consistent with observed behavior. For example, if results show too many transactions per person per month, then parameters may be adjusted. Some events are more important than others, e.g. moving money to avoid a negative bank account balance takes precedence in most cases over moving money to get a higher interest rate. Since parameters are not all independent, a certain amount of iteration is needed to arrive at consistent set of parameters.
As suggested by point (b) of the previous paragraph, parameterization is also useful for checking what-if scenarios, e.g. what if there were more laundering, or what if there were changes to banking laws? Synthetic data enables these explorations, which are not possible with real data.
Finally, we note the laundering tag on financial transfers is transitive in AMLworld. If A pays B $100 in illicit funds, and B then pays $50 of those funds to C, and C pays $25 of the $50 to D. then not only is the initial $100 considered laundering, but the $50 and the $25 are also. This transitivity is supported by detailed tracking; AMIworld propagates this tag through all transactions, including co-mingling of funds from legitimate and non-legitimate sources. AMLworid's complete labeling of laundering is nearly impossible with real-world data.
3.5 Public Availability of Synthetic Data
We have used AMLworld to create several synthetic AML datasets that have been made available on Kaggle [50]. Data is divided into two high-level groups: HI and LI, with higher and lower illicit ratios (laundering) respectively. Both HI and LI are further divided into small, medium, and large datasets, with large having 175M 180M transactions. Due to space constraints, Table 4 in the Appendix provides details about the datasets.
However, to demonstrate the realism of AMIworld, we discuss several statistics about the LI-Large dataset. LI-Large has 176M transactions spanning a little over 3 months. We focus on LI-Large, but apart from their size and laundering rates, the other five datasets have similar characteristics. Several figures in the Appendix provide grounding about the realism of our data. Figure histograms annualized transactions per account, and its distribution roughly aligns with U.S. Federal Reserve data [5I) Table shows the distribution of transaction formats like ACH, wire, and cheque. Again numbers correspond roughly with Federal Reserve statistics [41]
Overall in 1,750 transactions is laundering in LI-Large. For comparison, Starnini al. [56] report on real data with 180M transactions over 6 months from a major Italian bank. They find 855 laundering motifs (equivalent to our Scatte-Scatter and Gather patterns), each with "less than 20 [nodes] total". This amounts to around laundering transaction per 21,000. By comparison Appendix Table 7 indicates LI-Large has 8.212 combined Gather-Scatter and Scatter- Gather transactions yielding very similar laundering transaction per 21,440 transactions from these patterns.
4 Performance Evaluation of Machine Learning Models
In this section, we provide a preliminary evaluation of the suitability of our synthetic datasets for creating effective machine learning models. A deeper treatment and evaluation is beyond the scope of this "datasets and benchmarks' submission, but can be found in the work of Egressy et. al. [20]. In key way, these measurements are even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.
We train Gradient Boosted Tree (GBT) and popular message-passing Graph Neural Network (GNN) models on the datasets introduced in Section 3.5] using the tabular- and graph-based data representa- tions, respectively. Parameter tuning for GBTs and GNNs is described in Section C of the appendix.
7

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Source and destination account IDs of the transactions are not used as features in our experiments, which prevents our models from recognizing laundering transactions simply by learning account IDs.
GBT Baselines We use LightGBM [29] and XGBoost 16] as our GBT baselines, which are widely used machine learning models for tabular data. GBT baseline experiments were performed using version 3.1.1 of LightGBM and version 1.7.6 of XGBoost, To improve the performance of these GBT models, we use Graph Feature Preprocessor (GFP) 48 49,, which creates additional features for the datasets, To achieve this, GFP interprets the input data as a graph and extracts various graph- baspd features from this graph, such as vertex statistics and number of simple cycles [ 12 As result,
GBT models can exploit the underlying graph structure of the datasets. Section D provides details on how GFP was configured for the experimental evaluation.
of the appendix
GNN Baselines GIN (Graph Isomorphism Network) with edge features [65 25 and PNA (Principal Neighbourhood Aggregation) [60 [8] are used as GNN baselines. Since AML is transaction classification problem, we also include a baseline using edge updates (GIN+EU) [.. This approach is similar to the architecture used by Cardoso et al. [15], which has recently been used in self- supervised money laundering detection. Note that the GNN results for the Large datasets are not available because training models for these datasets requires significantly more compute resources.
Because the vertices are accounts and the edges are transactions in our financial transaction graph representation, detection of illicit transactions is an edge classification problem. All our GNNs use a final edge readout layer that performs classification using the edge embedding and the respective endpoint node embeddings input. To reduce the complexity, we use neighborhood sampling (23 when training and testing our GNN models. We sample 100 one-hop and 100 two-hop neighhors.
Data Split We use a 60-20-20 temporal train-validattin-tess split, i.e., we split the transaction indices after ordering them by their timestamps. The data split is defined by two timestamps, t and f2. Train indices correspond to transactions before time L,, validation indices to transactions between times & and (2. and test indices to transactions after 22. However, in the GNN case, the validation and test set transactions need access to the previous transactions to identify patterns. So construet train, validation, and test graphs. This corresponds to considering the financial transaction graph as a dynamic graph and taking three snapshots times t1, t2, and t3- tmaz. The train graph contains only the training transactions (and corresponding nodes). The validation graph contains the training and validation transactions, but only the validation indices are used for evaluation. The test graph contains all the transactions, but only the test indices are used for evaluation. This is the most likely scenario faced by banks and financial authorities when classifying unseen batches of transactions.
Evaluation of GBT and GNN Baselines As our proposed datasets are naturally very imbalanced, traditional metrics like accuracy do not provide reliable measure of model performance. In this context, we have chosen to emphasize the Fl score for the minority class. Detailed precision and recall scores for the GNN baselines can be found in appendix Section E Table@sshoweee performance for the range of models evaluated. These results show that message- sesssageppssingg GNNs that exploit the connectivity between accounts are effective in capturing laundering transactions. Advanced GNN architectures such as PNA and GIN+EU, which respectively implement extended message aggregation and edge update mechanisms, significantly enhance the GNN performance. Furthermore, the combination of GFP with gradient boosting techniques, specifically LightGBM and XGBoost, also demonstrates strong performance across different datasets. An interesting observation is the near-par performance of PNA with GBT baselines, even without relying on handcrafted features.
the inference
The LI datasets present more significant predictive challenges compared to the HI datasets. This is primarily attributed to their lower illicit ratios as well as the longer time span of their laundering patterns. The primary difference between LI and HI datasets is that criminals launder less frequently in LI, which makes it harder to discover the laundering patterns. To address this, our models incorporate the measure of weighting the predictions of the minority class higher in the loss function. However, considering the intricacies of dealing with the LI datasets, exploring other strategies, such as the Pick and Chose method (36]. which employs label-balanced sampler, or GraphSMOTE (66), which introduces synthetic minority class samples in the embedding space, may be imperative.
The prediction accuracy on the more challenging LI datasets can be improved by reusing or fine- tuning models pretrained on the HI datasets. Table 3 shows that reusing the pretrained LightGBM and PNA models on the HI- and -Large datasets enables higher Fl scores than those of XGBoost models trained on the respective LI datasets (see Table Furthermore, the classification
8

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Table 2: Minority class F1 scores (%). HI indicates higher illicit ratio. LI indicates lower ratio.
Model
Smalm
LI-Smat
HI-Medium
LI-Median
HI-Large
Li-Lorge
GIN
[5525
28.20
1.13
7.90
2.78
42.39
0.43
3.86 1: 3.62
NA
NA
GIN
EU
47.73 + 7.56
20.62
2.41
49.26
4.02
6.19 8.32
NA
NA
PNA
50
56.7T 1 2.41
16.45
1 1.46
30.71
1.91
27.73 1.65
NA
NA
GFP
48
49
LightGBM 29
62.86 1: 0.25
20.83
4: 1.50
39.48
0.15
20..85
0.38
48.67
0:24
17.09 I 0.46
GFP
49
XGBoosl 16
63.23
0.17
27.30
0.33
65.70
0.26
28. 28.16
0.14
42.68 1 12.93
24.23
0.12
Table 3: Minority class Fl scores (%) of models trained on HI datasets and evaluated on LI datasets.
Model
LI-Small
LI-Median
Li-Large NA
PNA
60
0.00 + 0.00
36.60 = 0.74
PNA
60
fine-tuning (5 epochs)
27.38 1,03
36.84 + 1.64
NA
GFP
LightGiBM 29
0.02 = 0.03
32.51
0.24
21.77 0.15
XGBoost 16
0.00 - 0.00
30.31
0.28
17.48 6.90
Private graph, private model
Private graph, shared model
Shared graph, shared model
Minortyyclas Flass s2ore
100%
100%
60%
60%
40%
40% 23
20%5
20%
22
22
72
79
(a)
Bank ID
(b)
Bank ID
Figure 6: Effect of data and model sharing across banks for (a) HI-Medium and (b) HI-Large datasets.
performance on LI data can be improved further for the LI-Small dataset, where directly using the models trained on HI datasets is ineffective, by fine-tuning the HI PNA models using the LI dataset.
In Figure 6 we report results on HI-Medium and HI-Large datasets for individual banks using GFP to create features and LightGBM to build models. Related experiments performed on the LI-Medium and Large datasets can be found Section of the appendix. For brevity, we focus on top-30 banks that participate in the largest number of transactions in each dataset. We evaluate three setups:
In the shared graph, shared model setup. banks share all their transactions with each other to create a shared financial transaction graph. The GFP is applied to this shared graph to extract graph features, which are also shared. shared LightGBM model built using this shared data, which the banks use to score their own transactions. Note that the results given in [Ssho the aggregate F1 score for all the banks produced using the same setup.
The private graph, private model setup computes the F1 scores using the LightGBM models trained separately by each bank on its private data. In this setup, bank sees only the transactions performed by its own account holders. A private graph is constructed using these transactions, and the GFP is executed on this private graph to extract features. In addition, each bank trains its own model its data, so there is no model sharing either. In the private graph, shared model setup, each bank creates a private transaction graph and extracts graph features from it using GFP in the previous case. The banks do not share the source and destination account ids with each other, but share the remaining transaction features, including the graph features. A shared LightGBM model is trained using this data.
Figure shows that, in general, financial institutions can achieve higher Fl scores by building shared machine learning models. However, building shared models top of shared financial transaction graphs leads to even more significant improvements. To achieve such accuracy improvements in a sovacy-preserving manner, financial institutions use differentiaall-priinn sharing techniques. Even though evaluation of such techniques is outside the scope of our work, we believe that our contributed datasets offer valuable testing ground for building such approaches.
model and topology
Future Work and Research Avenues
The focus of this paper is the generation of high-quality datasets and their use to build effective anti-money laundering models. Here some additional research opportunities that follow naturally:
Historically, AMI models have focused on transactions at one bank because that the only data available to individual banks that review transactions for laundering. Our synthetic

---

data enables analysis across multiple banks as shown in Figure 6 With that comes need to build
machine learning models and graph topology-sharing techniques. 2. Whether considering transactions at one bank or across banks, there is need for machine learning and deep learning models that can detect complex AMI, patterns in graph datasets.
3. Building on points (1) and (2), using deep neural networks, it may be possible to use our synthetic cross-banks data for pre-training, before fine-tuning with real-world data. Our initial experiments given in Table demonstrate that transfer learning is a viable approach.
4. As new models and algorithms are developed, it is important that they run efficiently in terms of time, memory consumption, CPU and GPU use, etc. Techniques to achieve such efficiency when analyzing financial transactions differ from those needed in other domains. 5. Others may find ways to improve our data generation method. For example, with our agent-based approach, there can be challenges around the stability of the data generation: minor parameter tweaks can sometimes yield significant changes in aggregate results, such as the laundering rate. Generative models for graphs could be promising research avenue.
6 Conclusions
We have outlined AMLworld, detailed, multi-agent, virtual world approach to generating synthetic financial data labeled for money laundering. We have established that the generated data matches real- world data in key regards. AMLworld data also has perfect tagging for laundering activity something essentially impossible with real data, where tags typically miss a great deal of laundering activity. By providing realistic tagged data, AMLworld facilitates the development of money laundering detection models, and our experiments provide baseline scores, demonstrating the utility and efficacy of machine learning models. Lastly, AMLworld enables the generation of models detect what-if laundering patterns not yet observed in real data. Six AMLworld datasets are now publicly available on Kaggle [50]. Our initial evaluation on these datasets shows that GNNs and GBTs can be effective solutions for identifying laundering transactions. While GBTs require some feature engincering to capture complex laundering patterns. GNNs often produce competitive results without any feature engineering. Further research is needed to enable collaboration and cooperation between financial institutions through the use of differentially private graph topology and model sharing techniques.
7 Ethics
We believe that our release of this synthetic laundering data helps in the AML fight.
As noted in the Introduction, most laundering activity currently goes undetected. This suggests the malefactors have the upper hand - as opposed to domains like credit card fraud, where the large majority of fraud is detected and quickly. One key goal in releasing this data is to facilitate improvements in laundering detection to enable results closer to credit card fraud detection.
A key advantage for credit card fraud detection is that close ground truth is well understood via customer feedback about unpurchased items. Unfortunately, no good ground truth exists for laundering activities in real financial data - Icading high error rates and much missed fraud. By contrast, our synthetic data has this ground truth that has been so helpful against credit card fraud.
Beyond helping the good side, we believe our data has limited potential to help the bad side. An obvious way we might do that by publishing the algorithms used by bank X to detect laundering. Launderers could then change their behavior so as to avoid being flagged by the algorithms. With only a static set of data, is much harder for launderers to change their behavior to avoid detection.
To this point, we emphasize that what we have open-sourced is a set of data - not the code to generate the data. If launderers had our source code, it would be easier for them tweak the data produced and check the results versus whatever detection algorithms they could get their hands on.
We offer a few additional observations ethics in Appendix B
Acknowledgments and Disclosure of Funding
The support of the Swiss National Science Foundation (project numbers: 172610 and 212158) for this work is gratefully acknowledged.
10

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Supplementary Material
Realism of Data Generation
16
Ethical Use of the Data
18
Hyperparameter Tuning of Machine Learning Models
18
Graph Feature Preprocessor Configuration
19
Additional GNN Experiments
20
Additional GBT Experiments
20
Datasheet
23
Motivation
23
G.2
Composition
23
G.3
Collection Process, Uses, Distribution and Maintenance
24
A Realism of Data Generation
we used AMLworld create several synthetic AML datasets. The datasets are published on Kaggle [50] under a Community Data License Agreement. As noted previously, data divided into two high-level groups: HI and LI. The HI datasets have slightly higher illicit ratios (more laundering) than LI. Both HI and LI are further divided into small, medium, and large datasets, with large having 175M 180M transactions. Table 44 provides a number of basic statistics about all six Kaggle datasets. We include more detailed analyses below.
As described in Section 3.5
Table 4: Public Synthetic Data Statistics. HI = Higher Illicit (more laundering) LI = Lower Illicit.
Small
Medium
Large
Statistic
H1
I.1
HT
1.1
HT
LI
Days Spanned
10
97
of Bank Accounts
515K
705K
2077K
2028K
2116K
2064K
of "off"Trassse Transactions
5M
7M
32M
31M
180M
176M
of Laundering Transactions
3.6K
4.0K
35K
16K
223K
100K
Laundering Rate (1 per Trans)
981
1942
905
1948
807
1750
Figure 7 histograms annualized transactions per account. It gives micro view of how different entities drive the overall transaction counts found in Table Figure also provides a touch point to real data: numbers roughly align with U.S. Federal Reserve data [51]. [able[5]shows the distribution of transaction formats used in the dataset, such as ACH, wire, and cheque. Again numbers correspond roughly with Federal Reserve statisties 41
Table g breaks down laundering transactions between (a) transactions following one of standard patterns discussed in Section 3.2 in particular Figure 2 and (b) the integration transactions disguised
as other activities (e.g. employee payroll or company supplies).
As with other data, we base salary and pension amounts on real data, in this case from the US Internal Revenue Service [54). Figure [ shows the number of returns filed for the 2018 tax year for each amount of salary income and pension income. Having proper distribution of salaries and pensions in turn helps drive accurate modeling and statistics for transaction sizes and frequency, as just discussed. The $0 top bin in Figure 8 reflects the fact that some people have no salary income or no pension income. Beyond salary and pension people can have income from other sources such as interest or dividends.
16

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Table 5: Distribution of transaction counts by format in LI-Large.
Format
# Transactions
Format
Transactions
Cheque
69,720,485
Reinvestment
7,258,251
Credit Card
49,923,366
Wire
6,346,402
ACH
21,650,558
Bitcoin
3,601,817
Cash
18,069,965
Table 6: Histogram of # of nodes (accounts) in LI-Large laundering patterns. Gather Scatter has
counts: (a) # of nodes from which initial funds come; (b) of nodes to which funds ultimately go.
of Nodes
Fan-out
Fan-in
Cycle
Random
Bipartite
Stack
Scatter-Gather
Gather-Scatter
Min
Max
(1)
(2)
2
50
47
72
70
72
87
54
67
67
2
4
46
54
50
44
30
31
47
43
38
4
53
54
84
79
62
56
64
61
55
76
48
42
12
18
71
62
16
23
37
63
71
72
18
00
0
0
0
0
0
0
0
Table 7: Occurrence count for each laundering pattern in LI-Large.
Pattern
Pattern Count
# Trans in Pattern
Pattern
Pattern Count
# Trans in Pattern
Fan-out
277
2,014
Stack
259
3,239
Fan-in
279
2,003
Random
278
1,831
Cycle
298
2,326
Scatter-guther
276
4,202
Bipartite
277
1,858
Guther-scateer
284
4,010
Table Laundering rates in LI-Large. Ratio is the total transaction count divided by the laundering transaction count.
Description
# of Trans
Ratio
Laundering Trans. Patterns
19.461
9,047
Laundering Trans. Other
81,143
2,170
Laundering Trans. Total
100.604
1,750
Figure S indicates that there are about 3.4x many returns showing salary income pension income, although returns can show both We assume about 62.5% of people in our models have salaries matching the value from the U.S. Department of Labor for labor force participation of the adult workforce [42]. Following the IRS ratios, about 18.3% of the population in our datasets has pension income, with around half of those pensioners also receiving salary.
3 For income tax purposes in the U.S., salary income also includes total wages for hourly workers.
# of Accounts
600 k
525
500 k
414 k 433
400
295
300
200 k
100 k
9 k
(1,4) 4]
(4, 10]
(10, 50]
(50, 100]
[100, 250]
(250) 1k]
(1 k
(5 25
(25 k, 100
(100 k, oe)
# of Transactions
Figure 7: Annualized transaction rate across accounts in LI-Large.
17

---

# of returns in range
20 M
Salary
15 M
Pension
10 M
5 M
5
10 k
15 K
40 k
75 k
200 k
500 k
1 MM
2 M
M
5 5MI
10 M
1000 M
20
25
30
50
100
Income amount in USD
Figure Distribution of U.S. tax returns for 2018 by salary income and pension income.
Table 9:
Successive halving parameter configurations used for hyperparameter tuning of shared models trained using all banks of dataset (Multi-bank models) and private models trained using the data of a single bank (Single-bank models).
Multi- bank models
Single-bank models
Datasets
Small
Medium
Large
Medium
Large
Zo
1000
100
16
1000
100
2
1.5
2
To
0.1
0.1
0.2
0.3
0.1
Additional statistics about our virtual world were provided in Section 3.5
B Ethical Use of the Data
We addressed number ethics issues in Section We offer few additional observations here.
Ethical use of the data includes using for benchmarking and improving models for the detection of money laundering activity. We foresee significant positive societal impacts from such uses of our data. Money laundering has huge cost for society in itself, but more importantly, money laundering enables a whole range of criminal activities to continue, ranging from phishing attacks to human trafficking. Detecting money laundering transactions can help authorities uncover such activities and identify the criminals behind them. Additionally, the data could be used for pretraining detection models before fine-tuning on real data.
Given that the dataset is synthetic, there is no risk of it containing personally identifiable information or offensive content. Moreover, researchers do not need take special care with its use, but they should bear in mind that performance might not translate one-to-one to real data.
C Hyperparameter Tuning of Machine Learning Models
LightGBM and GBT Baselines. We use successive halving 26 the hyperparameter tuning
XGBoost models. Successive halving starts by randomly sampling æo model parameter configurations. Each configuration model parameters is evaluated using a fraction ro S 1 of the initial training set. The algorithm then finds the best zo/n configurations, with 1. These best configurations the next round of successive halving using fraction X To of the train set. This process continues until the fraction of the training set used for the evaluation reaches 1. We use different successive halving configurations for different datasets, as shown in Table 9 Furthermore, the parameter ranges from which To initial parameter configurations
used
sampled shown in Table 10)
GNN Baselines. We used random sampling to identify a good range GNN hyperparameters. second round of random sampling was conducted with this narrower range to pick our final set of hyperparameters. We varied the following hyperparameters: the number of GNN layers, hidden embedding size, learning rate, dropout, and minority class weight (for the weighted loss function).
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Table 10: Model parameter ranges used at for the hyperparameter tuning of GBT models. The small parameter range, indicated with Range-small, is only used for hyperparameter tuning of LightGBM models related to Hrre and Li-Large datasets. The large parameter range shown in column Range-large is used for other datasets.
LightGBM
XGBoost
Parameter num_round num leaves
Range-small
Range-large
Parameter
Range
(32, 512)
(10, T000)
num_roume round
(10, TO00)
(32, 256)
(6384)
max_ max__eeph
(1,
learning_rad _ate
(0.001, 0.01)
10(-2.5,-1
learning_rate
10(-2.5,-1)
lambda_ 12
(0.01, 0.5)
10(-2,2)
lambda
10(-2,2)
scale _os__oss_____ jos_h
(1, 10)
(1, 10)
scale scale_pos___ weight
(1, 10)
lambda_ _l
10(0.01,0.5)
10(0.01.0.5)
colsample beytre bytree
(0.5, 1.0)
subsample
(0.5, 1.0)
The exact ranges we used are listed in I1 The number of random samples was set to between 10 to 50, depending on the training time of the model on a particular dataset. To get our final results, use the peerparrametess with the best validation score to train four models initialized with different random seeds.
Table 11:
Model parameter ranges used for hyperparameter tuning of GNN models. The hyperparam- eters were optimized on the small datasets and used for all GNN models.
Parameter Ranges
GIN (+EU)
PNA
hidden embedding size
(16,72)
(16,64)
learning rate number of GNN layers
(0.005,0.05)
(0.0001, 0.002)
(2,4)
(2,4)
dropout
(0,0.5)
(0, 0.2)
minority class weight
(6,8)
(6,8)
D Graph Feature Preprocessor Configuration
The Graph Feature Preprocessor (GFP) processes transactions represented as temporal edges in streaming fashion. The input of this preprocessor is batch of temporal edges that the preprocessor inserts into its in-memory dynamic graph and extracts various graph features from this graph, such as scatter-gather patterns, simple cycles, and vertex statisties. The output of the preprocessor is the same batch of edges with these additional graph-base based features. This library is available as part of the Snap ML library (4), and the experiments in this paper performed using version 1.14 of Snap ML. More details on GFP are available in the documentation (88
The graph-based features for our experiments are extracted using the batch size of 128. The GFP is configured to generate features based on scatter- scatter-gather patterns, temporal cycles, simple cycles of length up to 10, and vertex statistics. We set the GFP gather patterns and a time window of one day for the rest of the graph- based features. The vertex statistic features are computed using the "Amount" and "Timestamp" fields of the basic transaction features (see Figure Th). This configuration is used for all datasets and experiments that use GFP in this paper.
time window of six hours for scatter-
For each dataset, GFP processes transactions the increasing order of their timestamps. This ordering ensures that the graph-based features for each transaction are extracted using the past data. As result, transactions from the training set will not contain graph-based features computed using the validation or test sets. Processing transactions in such way prevents data Ieakage.
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E Additional GNN Experiments
Our GNN code included with the supplementary material and available publicly on GitHub under an Apache License. The GNNs are implemented using PyTorch Geometric version 2.3.1 [2] and PyTorch version 2.0.1.
All the baseline GNN experiments were run on an internal cluster on Nvidia Tesla V100 GPUs. Table 12 shows the runtimes when training the different GNN baselines on the AML Small and Medium datasets. The size of the GNN models was kept the same: 2 GNN layers and hidden embedding size of 64, The total GPU time, including initial experiments and hyperparameter optimization, estimated to have been around 1000 GPU hours.
Table 12: Total training times (TTT) and inference performance in Transaction per Second (TPS) for all GNN baselines on the AML Small and Medium datasets using an Nvidia Tesla V100 GPU.
HI-Small
LI-Small
HI-Medium
LI-Medium
Model
TTT (s)
TPS
TTT (s)
TPS
TTT (s)
TPS
TTT (s)
TPS
GIN
22703
17210
29655
15784
85652
7101
85994
7316
GIN+EU
26046
11844
36625
11640
85753
4981
85887
5097
PNA
27745
16557
39648
14994
85654
6725
85827
6819
In addition to the minority class F1 scores in Table include more fine-grained results detailing the precision and recall rates of the GNN-based models. Precision evaluates the accuracy of laundering predictions, recall measures the model's ability identify all laundering instances, while the F1 score is the harmonic mean of precision and recall. Table 13 shows the laundering recall rates and Table 14 shows the corresponding precision scores. In Figure we give example precision-recall curves, which visually capture the trade-off between precision and recall across different decision thresholds. The examples are taken from the best- nest-performing seed from the best-performing model (PNA) on all small and medium AML datasets.
Table 13: Minority class recall rate (%) for the GNN-based models. HI indicates higher illicit ratio. LI indicates a lower illicit ratio.
Model
HI-Small
LI-Small
HI-Medium
L.-Medium
GIN 65.25
38.16 + 3.92
14.59 +: 2.37
39.86 1 3.61
8.07 = 9.32 5.51 + 6.82
GIN+BU 1
55.41 + 5.96
23.26 + 2.87
48.06 1 6.45
PNA 60
53.15 - 2.26
16.43 + 2.62
47. 47.42 1 4.30
20.44 + 0.66
Table 14: Minority class precision (%) for the GNN-based models. HI indicates higher illicit ratio. LI indicates a lower illicit ratio.
Model
HI-Small
LI-Small
HI-Medium
LI-Medium
GIN 65 25
27.40 7.98
5.14 3.42
47.78 5.20
5.23 + 3.60 12.13 = 19.35 36.50 10.51
GIN+EU 15
42,29 + 10.69
21.60 10.83
52.56 8.27
PNA 60
58.48 1 10.67
17.37 5.80
69. 12 4 4.26
F Additional GBT Experiments
Figure shows the precision-recall curves for XGBoost models trained on all AML datasets using graph- based features created with Graph Feature Preprocessor. These curves correspond to the results from the row "GFP+XGBoost" shown in Table 2] The steep slope of each curve after the red dot, which represent data point obtained using the prediction threshold of 0.5, indicates that it is challenging to obtain higher recall without significantly degrading the precision. Note that is possible to achieve higher precision of these XGBoost models by simply reducing the recall by few percent.
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1.0
1.0
0.8
0.8
Precision
0.6
P1: 58.79%
Precision
0.4
0.4
0.0
0.0
0.2
0.4
0.8
0.8
1.0
0.6
0.8
1.0
Recall
Recall
(a) HI- II-Small
(b) LI-Small
0.8
0.8
F1: 83.00%
Precision
0.6
Precision
0.6
F1: 28.29.000
0.2
0.2
0.0
0.4
0.6
0.8
1.0
0.8
1.0
Recall
Recall
HI- Meddimm
(d) LI-Medium
Figure 9: Precision-Reche Curves for the best-performing PNA model for all AML datasets. The red dot indicates the Fl score obtained using the prediction threshold 0.5.
Private graph, model
Private graph, shared model
Shared graph, shared model
class f1 score
100%
100%
Minariby cina
40%
40%
20%6
20%
0%
22
201
(8)
Figure 10: Effect sharing the data and LightGBM models across banks for the (a) LI-Medium and (b) LI-Large datasets.
Figure 10 shows performance per-bank basis using the LI datasets. The experiment is explained in Section and the plot is analogous to Figure Ø but here we are using the L.-Meeium and LI-Large datasets. These dataset contain fewer illicit transactions compared the HI-Medium and HI-Large datasets (see Table Therefore, it is more challenging to build a machine learning model for a bank using only local data of these LI datasets compared to the banks of HI datasets. In this case, the average minority-class Fl score across 30 banks shown in Figure 10 is only 4.9% for LI-Medium and 8.7% for LI-Large. Nevertheless, the improvement in minority-clan Fl score of the shared graph, shared model case compared to the private graph, private model case still significant. Sharing the transaction graph and the global model across the banks increases the average minority-class F! score to 20.8% for LI-Medium and to 22. 1% for LI-Large.
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1.0
1.0
F1: 52%
Precision
0.6
Precision
0.8
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Recall
Recall
(a) HI-Small
(b) LI-Small
1.0
0.8
0.8
62.68%
Prectsion
Precision
0.4
0.4
0.2
0.2
Recall
Recall
HI-Medium
(d) LI-Medium
1.0
1.0
F1:44:25%
0.8
Precision
0.6
Precision
0.6
0.2
0.2
0.0
0.0
0.2
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Recall
Recall
(c) Hargerge
(i) LI-Large
Figure 11: Frecision-Recall Curves for the XGBoost models for all AML datasets trained using
graph-based features of GFP. The red dot indicates the Fl score obtained using the prediction threshold of 0.5.
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G
Datasheet
We include datasheet based on the framework set forward by Gebru et al. [22]. Some parts of the proposed datasheet are omitted since our datasets are synthetic.
G.1
Motivation
For what purpose was the dataset created? The dataset was created test, develop, and improve machine-leaming models for financial crime detection. In particular, the datasets focus on identifying money laundering transactions.
Who created the dataset and on behalf which entity? Erik Altman on behalf IBM.
Who funded the creation of the dataset?
IBM
G.2 Composition
What do the instances that comprise the dataset represent? The datasets are synthetic financial transaction networks. Each node in the network represents an account/entity and each directed edge represents a transaction from account to another. Edge features detail the amount, currency, and type of transaction amongst other properties. The datasets also contain transactions that are labeled money laundering. All the information is simulated. No real account transaction details were used to create the datasets.
How many instances are there in total? There are 6 datasets. Each dataset consists of one graph. The number of transactions (samples) ranges from 5M 180M.
Does the dataset contain possible instances or is a sample (not necessarily random) of instances from larger set? The datasets are synthetic. Any number of transactions could be generated.
What data does each instance consist of? When used for transaction classification, the edges can be considered instances. Each instance consists of set of transaction features (incl., amount, currency, date, time, and type). In addition, each transaction is part of a whole network of transactions, and since the network topology plays important role, the position of the instance in the whole network could be considered a "part" of the instance.
Is there label or target associated with each instance? Yes.
Is any information missing from individual instances?
No.
Are there recommended data splits (e.g., training, development/validation,, testing)? Yes.
Are there any errors, sources of noise, or redundancies in the dataset? Not to the knowledge of the authors.
Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? The dataset is self-contained.
Does the dataset contain data that might be considered confidential (e.g., data that protected by legal privilege or by doctor-patient confidentiality, data that includes the content of indi- viduals' non-public communications)? No.
Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? No.
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G.3 Collection Process, Uses, Distribution and Maintenance
Please refer Section and Appendix A for details about the generation process. The current usage is detailed in the paper and potential uses are described in Appendix [B] The Kaggle page acts as the single source of distribution [50). The dataset will be maintained there.
24


Reviews:
[{"review_id": "W8wdn55bDl", "reviewer": "Reviewer_iXyM", "summary": "", "questions": "", "limitations": "Refer to Ethics and other sections.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. Novel Contribution: The paper presents a valuable contribution in the form of a realistic synthetic financial transaction dataset generator and AML datasets. This fills the gap of limited real-world data for training AML models and provides a standardized benchmark for comparisons.\n2. Realism: The agent-based generator is calibrated to closely match real transactions, making the synthetic data more realistic and representative of actual money laundering scenarios.\n3. Complete Ground Truth Labels: Unlike real data, the synthetic datasets have perfect tagging for laundering activity, enabling better training and evaluation of AML models.\n4. Public Availability: The publicly available datasets on Kaggle facilitate research and encourage collaboration among researchers and practitioners working on AML detection.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "z9s47DU4sl", "reviewer": "Reviewer_jAyB", "summary": "", "questions": "", "limitations": "See my comments in \"Opportunities For Improvement\"", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "The contribution of the proposed IT-AML generator is significant as it utilizes a more advanced algorithmic design compared to existing methods like AMLSim. This improvement has the potential to establish a new standard for similar tools in the field of Anti-Money Laundering.\n\nThe quality of the research appears sound, considering the introduction of these novel tools and their potential for effective application.\n\nIn terms of relevance, the paper aligns well with the scope of the Datasets and Benchmarks Track, focusing on the development of synthetic datasets for Anti-Money Laundering purposes.\n\nThe paper demonstrates clarity in its writing style and structure, contributing to its overall cohesiveness and understandability.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "c8y2SbUBob", "reviewer": "Reviewer_w9iG", "summary": "", "questions": "", "limitations": "See answers above.\n", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The addressed problem is a serious real-world problem.\n2. The paper is well-written and the work well presented.\n3. The transaction generation process is clearly described and is quite realistic.\n4. The evaluation is well conducted. ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: In this paper, the authors have introduced  a synthetic financial transaction dataset generator and Anti-Money Laundering (AML) datasets. These datasets offer a realistic benchmark for training machine learning models to detect money laundering. Based on the discussion and current reviews, I recommend accept.
Decision: Accept (Poster)