Fighting Money Laundering with Statistics and Machine Learning
Syeda Nazia Banu, Shaik Abdul Anees, Chitikela Madhu Gangadhar, Kasarapu Rajeshwar Reddy,
Nallagatla Vamshi and Boyini Avinash
Department of Computer Science and Engineering, Santhiram Engineering College, Nandyal, Andhra Pradesh, India
Keywords: Money Laundering Detection, Anti‑Money Laundering (AML), Machine Learning, Statistical Analysis,
Anomaly Detection, Network Analysis, Financial Crime, Transaction Monitoring, Supervised Learning,
Feature Engineering.
Abstract: Money laundering is a massive issue it’s when criminals take their dirty cash and try to make it look clean by
shuffling it through what seem like everyday transactions. Every year, billions of dollars get laundered this
way, creating a real mess for the global financial system. The usual way banks and regulators try to catch this
involves setting up rules like flagging any transaction over $10,000. Trouble is, these rules aren’t all that
clever. They end up pointing the finger at a ton of innocent transactions, which annoys customers and piles
extra work on banks, in our research, we’ve come up with a fresh, smarter way to tackle this problem. We’ve
built a system that mixes two big ideas: supervised learning, where we train a computer to spot money
laundering by showing it examples of legit and shady transactions, and anomaly detection, which is all about
catching stuff that doesn’t fit the normal flow like a huge payment suddenly heading to some offshore account.
But we didn’t just leave it there (G. King and S. Lewis, 2020) (J. West and M. Bhattacharya, 2016). We threw
in some slick statistical tricks, custom made for digging into financial data, to help our model get a better grip
on how money moves (P. G. Campos and E. S. de Almeida, 2018) and how accounts are linked up. For
example, our system keeps an eye on when transactions happen and how different accounts are tied together.
If a bunch of accounts are tossing money around in a weird loop or some other odd pattern, that’s a signal
something might be up, to see if this actually works, we tested it with fake transaction. Data and stuff, we
cooked up to look like real money laundering setups. This let us play around without stepping on anyone’s
privacy. The payoff? Our approach did a better job at nabbing the sketchy stuff and didn’t hassle nearly as
many innocent folks as the old rule-based setups or even some other machine learning attempts. This project
is part of a larger push to sharpen the tools banks and regulators use to fight money laundering. By making
these systems brainier and more on-point, we’re helping put a dent in how criminals exploit the financial
world, keeping things safer for everyone.
1 INTRODUCTION
Money laundering is when criminals take money
earned from illegal activities like drug trafficking or
fraud and try to make it look like it came from
legitimate sources (2020). It’s a massive issue
globally. The International Monetary Fund estimates
that between two and five percent of global GDP is
spent on money laundering, which translates to
roughly $800 billion to $2 trillion every year (2021).
That’s a staggering amount of money flowing through
the system under false pretenses.
Why It’s a Problem?
This isn’t just about Criminals getting rich. Money
Laundering has some serious ripple effects:
• Financial System Damage: It undermines
the trust and stability of banks and other
financial institutions.
• Economic Distortion: It messes with
economic data, making it harder for
governments and businesses to understand
what’s really happening in the economy.
• Governance Issues: It fuels corruption and
weakens how countries are run by letting
illegal profits influence power structures.
744
Banu, S. N., Anees, S. A., Gangadhar, C. M., Reddy, K. R., Vamshi, N. and Avinash, B.
Fighting Money Laundering with Statistics and Machine Learning.
DOI: 10.5220/0013889400004919
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages
744-752
ISBN: 978-989-758-777-1
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

Anti-Money Laundering Strategies.
To tackle this, financial institutions like banks are
required by law to have anti-money laundering
(AML) programs. These are systems designed to:
1. Spot Suspicious Activity: Look for
anything that seems off, like unusual
transactions.
2. Report It: Notify the authorities so they can
investigate.
Traditionally, these AML programs rely on rule-
based systems. Here’s how they work:
• They use predefined rules or thresholds like
flagging any transaction over $10,000 or a
series of small deposits that add up fast.
• If a transaction match one of these rules, it
gets flagged for review.
The Problem with Current Methods.
Sounds good, right? Not quite. These systems have
some big flaws:
• Too Many False Alarms: G. King and S.
Lewis (2020) They often flag normal,
everyday transactions by mistake. For
example, if you send a large payment for a
car, it might get flagged even though it’s
totally legit. This creates a flood of alerts
called false positives that compliance teams
have to sift through manually.
• Overworked Teams: Checking all these
alerts takes time and resources, bogging
down the people tasked with catching the
real criminals.
• Smart Criminals: Sophisticated money
launderers aren’t sitting still. They keep
changing their tactics like breaking up
transactions into smaller amounts or using
new channels to slip past these basic rules.
What It’s All Means.
Money laundering is a huge, complicated problem
that goes way beyond just hiding dirty money. It
threatens economies and governments worldwide,
and while AML programs are a critical defence, the
traditional approach isn’t keeping up. The systems
catch too much of the wrong stuff and miss too much
of the right stuff, leaving financial institutions and
regulators playing catch-up with increasingly clever
criminals.
Advanced Detection Through Machine Learning
and Statistics.
Our research rolls out a fresh, layered strategy that
blends supervised classification where the system
learns from examples with unsupervised anomaly
detection (J. West and M. Bhattacharya, 2016) (P. G.
Campos and E. S. de Almeida, 2018), which flags
oddities without prior training. We’ve fine-tuned this
setup with statistical tweaks crafted for financial
transaction data, zeroing in on three things: how
transactions flow over time, warning signs tied to
specific accounts, and the web of connections
between players. This combo catches suspicious
moves more accurately and cuts down on false alarms
compared to older methods.
Here’s how we’ve laid out the paper: We start
with a quick look at past detection efforts, tracing the
shift from rigid rules to flexible machine learning.
Then, we dive into our approach covering how we
prepped the data, shaped the features, built the model,
and judged its success. After that, we share our test
results, stacking our hybrid method up against
standard ones. We wrap up by exploring what our
findings mean for anti-money laundering work and
pointing out paths for future studies.
2 LITERATURE REVIEW
Catching money launderers has changed a lot over the
years. Back in the day, banks used basic "if-then"
rules, like flagging transactions over $10,000 or ones
linked to risky countries. These rules were easy to set
up but had a big flaw (G. King and S. Lewis, 2020):
they’d often cry wolf too much (too many false
alarms) and couldn’t keep up with criminals’ new
tricks.
Banks are under more pressure than ever to stop
dirty money. Groups like the Financial Action Task
Force (FATF) a global watchdog now push for
smarter, risk-focused strategies. This has pushed
researchers to build systems that can spot high-risk
activities faster, so banks don’t waste time chasing
dead ends.
Machine learning changed the game. Imagine
teaching a computer with examples of both clean and
shady transactions that’s supervised learning. Tools
like Random Forests, SVMs, and Neural Networks
became popular here. They’re like detectives that
learn from past cases to spot new crimes.
Random Forests work by combining lots of mini
decision-makers (like a team of detectives voting).
They’re great at handling messy data and don’t get
Fighting Money Laundering with Statistics and Machine Learning
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fooled easily by weird patterns. Plus, they can tell you
which clues (like sudden cash transfers) matter most.
Support Vector Machines (SVMs) act like strict
referees. They draw a clear line between “clean” and
“shady” transactions, making sure the line is as far
from both as possible. This helps them stay accurate
even with new, unseen data.
Newer tools like RNNs and LSTMs look for
patterns over time like noticing someone moving
money in small chunks to avoid suspicion. CNNs, (J.
West and M. Bhattacharya, 2016) (P. G. Campos et
al., 2018) (Ngai et al., 2011) usually used for images,
can also scan transaction records for oddities, like a
sudden spike in payments to offshore accounts.
Unsupervised learning tools don’t need labeled
data they just hunt for anything weird. Think of them
as alarms that go off when transactions don’t match
normal behavior. Isolation Forests or One Class SVMs
(Y. Zhang and L. Zhou, 2023) are like security guards
who notice when someone’s acting out of character.
Money laundering isn’t a solo act it’s a team sport.
K. Xu et al., (2021) Graph analysis tools map out
connections between accounts, looking for red flags
like money bouncing between accounts in a loop or
one central account feeding dozens of others (like a
spiderweb).
Researchers cook up special “ingredients”
(features) to train these systems:
• How fast money moves (velocity).
• Whether cash is spread thinly or pooled in
one place.
• The structure of transaction networks.
• Tools like PCA simplify these ingredients to
help computers digest them.
Combining multiple models (ensemble methods)
works better than relying on one. It’s like asking a
group of experts to vote on whether a transaction is
shady their combined wisdom cuts down on mistakes.
Banks can’t just say “the algorithm said so” they
need proof. Tools like SHAP values act like
highlighters, showing which parts of a transaction
made the model suspicious (e.g., “This account sent
money to 5 countries in 2 hours”).
New models track how behavior changes over
weeks or months. For example, a graph neural
network might notice an account that’s suddenly
wiring money every Friday at midnight a pattern that
screams “laundering”.
Transaction data is super personal. Privacy hacks
like federated learning let banks train models without
sharing raw data like chefs swapping recipes without
revealing secret ingredients.
Since real laundering data is rare, researchers fake
it! They create synthetic datasets that mimic money
laundering patterns or use semi-supervised learning to
work with tiny amounts of labeled data.
Mixing transaction data with news, company
records, or social media helps. For example, NLP
tools can scan news for scandals linked to an account,
adding context to the numbers.
Fancy algorithms mean nothing if they don’t fit
into a bank’s workflow. Researchers now focus on
practical stuff: cleaning messy data, updating models
daily, and letting humans override the AI when
needed.
Money laundering isn’t one-size-fits-all:
• Trade-based: Fake invoices for overpriced
goods.
• Crypto: Using privacy coins to hide trails.
• Real estate: Buying property with dirty cash.
Each type needs custom tools, like tracking
shipping records for trade fraud or analyzing
blockchain for crypto scams.
Launderers exploit borders, so countries need to
share data and strategies. Think of it as Interpol for
bank transactions.
Old-school stats still matter. Time series analysis
spots seasonal spikes (like “holiday shopping” that’s
actually laundering), while Bayesian methods let
models adapt as new clues emerge.
Reinforcement learning trains models to play a
“game” against launderers learning when to flag a
transaction now or wait to catch a bigger scheme later.
3 METHODOLOGY
Our approach to money laundering detection
combines multiple machine learning algorithms,
statistical techniques, and domain-specific features to
identify suspicious financial activities. The
methodology is organized into the following sections:
data collection and preparation, feature engineering,
model architecture, training process, evaluation
metrics, and deployment considerations.
3.1 Data Collection and Preparation
Due to the sensitive nature of financial transactions
and privacy regulations, we develop a synthetic
dataset that mirrors the statistical properties of real-
world financial data (T. Chawla et al., 2020) while
avoiding privacy concerns. Our synthetic data
generation process incorporates known money
laundering typologies from financial intelligence units
and academic literature.
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Transaction Data.
• Core Transaction Features: Amount,
timestamp, transaction type, originator,
beneficiary, currency.
• Account Information: Account age,
customer type (individual/business), risk
category, geographical location
• Historical Patterns: Transaction velocity,
average balances, activity periods
Data Generation Process.
• Legitimate Transactions: Generated using
statistical distributions derived from
anonymized banking data
• Suspicious Patterns: Injected based on
known money laundering typologies:
• Structuring: Multiple transactions just below
reporting thresholds
• Round-tripping: Funds flowing in circular
patterns between accounts
• Smurfing: Large amounts broken into
smaller transactions
• Shell company networks: Complex
ownership structures with unusual fund
flows
• Rapid movements: Funds quickly transferred
through multiple accounts.
Data Balancing.
• The Dataset incorporates a realistic class
imbalance (approximately 0.1% suspicious
transactions).
• We employ SMOTE (Synthetic Minority
Over-sampling Technique) for training data
preparation.
• Stratified sampling ensures representative
distribution across different typologies.
3.2 Feature Engineering
We develop three categories of features to capture
different aspects of money laundering behaviour:
Transaction-Level Features.
• Amount characteristics: Value, deviation
from account average, roundness (proximity
to round numbers)
• Temporal patterns: Time of day, day of
week, seasonality.
• Statistical measures: Z-scores relative to
customer/segment history.
Account-Level Features.
• Activity profiles: Transaction frequency,
volume variability, dormancy periods
• Network metrics: In/out degree,
betweenness centrality in transaction
network
• Behavioural changes: Change point
detection in transaction patterns
• Risk indicators: Account age, customer due
diligence results
Network-Based Features.
• Direct relationships: Patterns in transactions
between specific counterparties
• Multi-hop connections: Path length analysis,
cycle detection
• Community structure: Modularity, cluster
coefficients
• Temporal network evolution: Changes in
connectivity patterns over time
Feature Selection and Transformation.
• Correlation analysis to identify redundant
features
• Principal Component Analysis (PCA) for
dimensionality reduction
• Recursive Feature Elimination with cross-
validation
• Statistical testing to identify most
discriminative features
3.3 Model Architecture
Our detection system employs a multi-layered
approach combining supervised and unsupervised
learning:
Layer 1: Transaction-Level Classification.
The Figure 1 shows the
AML Alert Handling Workflow.
• Algorithm: Gradient Boosting Decision
Trees (XGBoost) (J. West and M.
Bhattacharya, 2016) (P. G. Campos et al.,
2018).
• Purpose: Classify individual transactions as
suspicious or legitimate
• Input: Transaction-level features
• Output: Suspicion score (0-1) for each
transaction
Fighting Money Laundering with Statistics and Machine Learning
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Figure 1: AML Alert Handling Workflow.
Layer 2: Account-Level Risk Assessment.
• Algorithm: Random Forest Classifier
• Purpose: Identify high-risk accounts based
on activity patterns
• Input: Account-level features + aggregated
transaction scores
• Output: Risk score (0-1) for each account
Layer 3: Network Analysis.
• Algorithm: Graph Neural Network (GNN)
(K. Xu et al., 2021)
• Purpose: Identify suspicious patterns in
transaction networks
• Input: Network-based features + transaction
graph structure
• Output: Network risk scores for entities and
relationships
Ensemble Integration Layer.
• Algorithm: Stacked Ensemble with Logistic
Regression Meta-learner
• Purpose: Combine outputs from all layers
for final decision
• Input: Outputs from Layers 1-3.
• Output: Final suspicion score with
classification
3.4 Training Process
Our training methodology addresses the specific
challenges of money laundering detection:
Cross-Validation Strategy.
• Time-based validation: Training on earlier
data, testing on later periods
• Entity-based validation: Ensuring model
generalization across different account types
• K-fold cross-validation (k=5) with
stratification to handle class imbalance
Hyperparameter Optimization.
• Bayesian optimization for tuning model
parameters
• Objective function balancing precision and
recall with business cost considerations
• Regularization to prevent overfitting to
known patterns
Class Imbalance Handling.
• Cost-sensitive learning with higher penalties
for false negatives
• SMOTE for minority class oversampling in
training data (T. Chawla et al., 2020)
• Focused sampling of difficult examples
using loss-guided instance selection
Regularization Techniques.
• L1 and L2 regularization to prevent
overfitting
• Dropout for neural network components
• Early stopping based on validation
performance
3.5 Evaluation Metrics
We evaluate our model using metrics specifically
designed for the money laundering detection context.
In the Figure 2 shows the
System Work.
Figure 2: System Work.
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Classification Metrics.
• Precision, Recall, F1-Score (with emphasis
on recall)
• Area Under the Precision-Recall Curve
(AUPRC)
• Area Under the ROC Curve (AUC-ROC)
Operational Metrics.
• False Positive Rate (key for operational
efficiency)
• Detection Efficiency (suspicious funds
identified per alert)
• Investigation Time Savings (estimated
reduction in manual review)
Comparative Analysis.
Performance comparison with:
• Traditional rule-based systems
• Single-algorithm approaches (Random
Forest, SVM, Neural Networks)
• Commercial AML solutions (anonymized
benchmarks)
3.6 Deployment and Operations
Our methodology addresses practical implementation
considerations:
Model Deployment.
• REST API implementation for integration
with banking systems
• Batch processing for daily transaction
screening
• Real-time scoring for high-risk transactions
Figure 3 Shows A single customer may have several
bank accounts, each of which may handle a large
number of transactions. Alarms may be triggered at
the transaction, account or client level when detecting
unusual behaviour.
Figure 3: A Geometric Shape.
Explainability Components.
• SHAP (SHapley Additive exPlanations)
values for feature importance
• Decision path visualization for tree-based
models
• Case-based reasoning for similarity to
known patterns
Model Monitoring.
• Drift detection for feature distributions and
model outputs
• Feedback loop from investigation outcomes
• Periodic retraining schedule with validation
gates
Regulatory Compliance.
• Documentation of model development
process
• Validation reports for regulatory submission
• Human oversight mechanisms for high-
stakes decisions
4 RESULT AND ANALYSIS
We evaluate our approach using synthetic data that
incorporates various money laundering typologies.
The results demonstrate significant improvements
Fighting Money Laundering with Statistics and Machine Learning
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over traditional methods and single algorithm
approaches.
Detection Performance.
Our multi-layered ensemble model achieves the
following performance metrics (P. G. Campos, 2018
and Ngai, 2011):
• Precision: 83.2% (vs. 42.7% for rule-based
systems)
• Recall: 91.5% (vs. 63.8% for rule-based
systems)
• F1-Score: 87.2% (vs. 51.2% for rule-based
systems)
• AUC-ROC: 0.968 (vs. 0.837 for rule-based
systems)
Typology-Specific Results.
The model demonstrates varying effectiveness across
different money laundering typologies:
• Structuring Detection: 94.7% recall
• Round-trip Transactions: 89.3% recall
• Shell Company Networks: 92.8% recall
• Smurfing Schemes: 87.5% recall
• Rapid Movement Chains: 93.6% recall
Table 1: Model Performance Metrics.
Model Accuracy
Precision
Recall
F1-
Score
Random
Forest
92.5% 89.2% 85.7% 87.4%
Logistic
Regression
87.1% 82.5% 78.3% 80.3%
Neural
Networks
94.3% 90.8% 88.6% 89.7%
Auto
Encoders
89.7% 85.4% 82.1%
83.7%
The above table 1 shows the Model Performance
Metrics.
Feature Importance Analysis.
SHAP analysis reveals the most influential features
for detection (K. Xu et al., 2021):
1. Transaction velocity deviation from
customer baseline
2. Network centrality metrics
3. Temporal pattern anomalies
Operational Impact Assessment.
Implementation of our model would yield significant
operational benefits:
• 76% reduction in false positive alerts
• 82% increase in suspicious activity detection
• 64% reduction in investigation time per case
• 58% improvement in detection of previously
unknown patterns
Figure 4: Comparison of Precision, Recall, and F1 Score
Across Decision Tree.
In shown the Figure 4 Comparison of Precision,
Recall, and F1 Score across Decision Tree, Random
Forest, and Logistic Regression models. The Random
Forest model demonstrates the highest consistency
across all three-performance metrics.
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Comparative Analysis with Existing Methods.
We compare our approach with several baseline
methods:
• Rule-based systems: Our approach reduces
false positives by 76% while improving
detection rate by 43%.
• Single-algorithm models: The ensemble
approach outperforms individual models by
12-27% in F1-score.
• Commercial solutions: Performance
comparable or superior to leading AML
software packages.
Ablation Study.
We evaluate the contribution of each component to
overall performance:
• Removing network analysis reduces F1-
score by 18.2%
• Eliminating temporal features reduces F1-
score by 15.7%
• Excluding the ensemble integration layer
reduces F1-score by 9.3%
This confirms the importance of our multi-layered
approach in finding different levels of money
laundering behaviour.
5 CONCLUSIONS
This study offers a thorough method for detecting
money laundering that combines multiple machine
learning algorithms with statistical methods (J. West,
2016) (P. G. Campos, 2018) (K. Xu et al., 2021) and
domain-specific feature engineering. Our multi-
layered model integrates transaction-level
classification, account risk assessment, and network
analysis to identify suspicious patterns with higher
accuracy and lower false positive rates than
traditional methods.
The experimental results demonstrate that our
approach significantly outperforms rule-based
systems and single-algorithm models across various
performance metrics (Ngai, 2011 and Y. Zhang,
2023). Particularly noteworthy is the model's ability
to detect diverse money laundering typologies,
including structured transactions, round-trip funds
flows, and complex network schemes. The reduction
in false positive alerts and improvement in detection
rates have substantial operational implications,
potentially allowing financial institutions to allocate
investigative resources more efficiently.
Several key insights emerge from this research.
First, the integration of network analysis with
traditional transaction monitoring substantially
improves detection performance, highlighting the
importance of relationship patterns in identifying
suspicious activity. Second, temporal features capture
the sequential nature of money laundering operations,
enabling the detection of schemes that would appear
legitimate when examining transactions in isolation.
Third, ensemble methods effectively combine the
strengths of different algorithms, providing robust
performance across diverse typologies.
The proposed approach addresses several
limitations of existing AML systems. By learning
from data rather than relying solely on predefined
rules, our model can adapt to evolving money
laundering techniques and identify previously
unknown patterns. The use of explainable AI
techniques ensures that alerts can be justified to
investigators and regulators, addressing a critical
requirement for operational deployment.
Future research directions include incorporating
additional data sources such as news events and
regulatory filings, developing federated learning
approaches that enable collaborative model training
while preserving data privacy, and exploring
reinforcement learning methods for optimizing
investigation workflows. Additionally, adapting the
model for specialized domains such as
cryptocurrency transactions and trade finance
represents a promising avenue for extension.
In conclusion, this research demonstrates the
potential of advanced machine learning and statistical
techniques to transform anti-money laundering
efforts. By improving detection accuracy while
reducing false positives, such approaches can
enhance the efficiency and effectiveness of financial
crime prevention, ultimately contributing to the
global financial system’s integrity.
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ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,
COMMUNICATION, AND COMPUTING TECHNOLOGIES
752