A project report on
Financial Analytics (MGT3012)
Title
Unveiling Fraudulent Patterns with Deep
Autoencoders
Submitted in partial fulfillment for the award of the degree of
MTECH (INTEGRATED) COMPUTER SCIENCE
AND BUSINESS ANALYTICS
By
Niwin Kumar - 20MIA1011
Aditi Anand - 20MIA1123
SCOPE
April, 2024
Acknowledgement
I extend my sincere gratitude to Dr. JYOTIRMAYEE, for her
invaluable guidance and support throughout the development of
the "Unveiling Fraudulent Patterns with Deep Autoencoders"
project. Dr. JYOTIRMAYEE's expertise and encouragement
were pivotal in shaping the project and navigating its
complexities.
I would also like to express my thanks to my parents and friends
for their unwavering support, providing the foundation for my
dedication and perseverance during the project.
This project represents a significant milestone in my academic
journey, and I appreciate the contributions of all those who,
directly or indirectly, played a role in its successful completion.
Niwin kumar 20MIA1011
Aditi Anand 20MIA1123
Abstract:
Credit card fraud poses a significant financial burden on both cardholders
and issuing institutions. This project investigates the potential of deep
autoencoders for anomaly detection in credit card transaction data to
combat this threat.
The methodology involves preprocessing transaction data, constructing a
deep autoencoder model, training it on normal transactions, and
evaluating its ability to differentiate between normal and fraudulent
transactions. The evaluation focuses on the reconstruction error
distribution and, potentially, other relevant metrics.
The project demonstrates the potential of autoencoders to learn the
patterns of normal transactions and identify deviations that might indicate
fraud. While the specific results are limited by the available information,
the findings suggest promise for this approach.
Future work includes model optimization, handling imbalanced datasets,
real-world system integration, and continuous learning to maintain
effectiveness against evolving fraud tactics. This research contributes to
the development of more secure financial systems by exploring the use of
deep learning for credit card fraud detection.
S.NO
TABLEOF
CONTENTS
PAGE NO
1
Introduction
4
2
6
3
Existing Real World
Problem
Proposed Methodology
4
System Architecture
10
5
Results
13
6
Conclusion
14
7
References
17
7
1. Introduction
Credit card fraud continues to plague the financial sector, incurring
substantial losses annually. Early and accurate detection is crucial in
mitigating these risks. This project investigates the effectiveness of deep
autoencoders, a type of neural network, for identifying fraudulent
transactions in real-time.
Understanding Autoencoders for Anomaly Detection
Autoencoders are a fascinating class of neural networks with the unique
ability to learn compressed representations of data. They consist of two
parts:
 Encoder: This network compresses the input data into a lowerdimensional latent space, capturing the essential features.
 Decoder: This network attempts to reconstruct the original data from
the latent representation, essentially decompressing it.
The beauty lies in the anomaly detection aspect. During training, the
autoencoder focuses on reconstructing "normal" transactions effectively.
When presented with a fraudulent transaction, the reconstruction error
(the difference between the original and reconstructed data) will be
significantly higher. This anomaly in reconstruction serves as a red flag,
potentially indicating fraudulent activity.
2. Existing Real World Problem
Credit Card Fraud
Credit card fraud is a pervasive and concerning issue in the financial
sector, posing significant challenges for both cardholders and issuing
institutions. Here's a breakdown of the problem:
 Financial Losses: Fraudulent transactions result in stolen funds and
charge backs, leading to substantial financial losses for card issuers
and potentially hefty charges for cardholders. These losses can reach
billions of dollars annually.
 Security Concerns: Credit card fraud erodes consumer trust in the
financial system. Incidents of fraud expose vulnerabilities in security
measures and raise concerns about the protection of personal financial
data.
 Growing Threat Landscape: The evolution of technology has brought
about new avenues for fraudsters. The increasing adoption of online
transactions and digital wallets creates opportunities for exploiting
weaknesses in these systems.
This project tackles the challenge of credit card fraud by investigating the
potential of deep autoencoders for anomaly detection in transaction data.
By identifying deviations from normal patterns, the model can contribute
to:
 Fraud Prevention: Early detection of anomalies can enable institutions
to block suspicious transactions before funds are disbursed.
 Reduced Financial Losses: Proactive identification of fraud attempts
minimizes the financial impact on both cardholders and institutions.
 Enhanced Security: Autoencoders can be integrated into existing
fraud detection systems, adding an extra layer of protection and
bolstering overall security.
3. Proposed Methodology
This section details the methodology employed in the project for credit
card fraud detection using a deep autoencoder.
1) Data Preprocessing
 Data Acquisition: The first step involves loading the credit card
transaction dataset containing features associated with individual
transactions, such as amount, time, location, and potentially other
relevant details.
 Missing Value Imputation: The data is examined for missing values.
Techniques like replacing missing values with the mean, median, or
mode of the feature, or using k-Nearest Neighbors (KNN) imputation,
are employed to ensure data integrity and prevent issues during model
training.
 Feature Scaling: Numerical features are scaled using StandardScaler.
This normalizes the data by subtracting the mean and dividing by the
standard deviation, ensuring all features are on a similar scale for
efficient autoencoder training.
 Time Column Removal: While time might be a factor in some fraud
cases, it's likely not a defining characteristic for anomaly detection.
Therefore, the "Time" column is removed from the data, simplifying
the data and focusing the model on features with a more direct impact
on transaction behavior.
 Class Separation: The target variable indicating whether a transaction
is fraudulent (usually labeled 1) or normal (usually labeled 0) is
separated from the remaining features. Separating the class variable
allows the model to focus on learning the patterns of normal
transactions during training.
2) Autoencoder Model Construction
 Keras Library: The Keras library, a popular deep learning framework
in Python, is used to construct the autoencoder model. Keras provides
high-level building blocks for defining and training neural networks.
 Model Architecture: The specific architecture will be determined by
analyzing the code, but a standard deep autoencoder architecture is
typically employed. This architecture consists of:
 Encoder Network: This network compresses the input transaction data
(excluding the target variable) into a lower-dimensional latent space
using fully connected layers with activation functions. The number of
neurons in each layer gradually decreases, capturing the essential
features of the data in a more compact representation.
 Decoder Network: This network attempts to reconstruct the original
input data from the latent space representation. It utilizes fully
connected layers with an increasing number of neurons, eventually
mirroring the structure of the encoder network in reverse. Activation
functions are often used in the decoder as well.
 L1 Regularization: L1 regularization is implemented during training.
This technique adds a penalty term to the loss function based on the
absolute values of the weights in the model. This helps prevent
overfitting by reducing the model's reliance on specific features and
encouraging it to learn more generalizable patterns.
3) Model Training
 Training Data Selection: The autoencoder is trained exclusively on
the "normal" transactions from the preprocessed data. This is crucial,
as the model needs to learn a robust representation of legitimate
transaction behavior to effectively identify anomalies.
 Loss Function and Optimizer: The model is trained using a common
choice for autoencoders, such as the mean squared error (MSE) loss
function. An optimizer like Adam is employed to efficiently update
model weights during training.
 Model Checkpointing (optional): Model checkpointing might be
utilized to save the model's best performing state during training. If
the model's performance on a validation set degrades, the training
process can be stopped, and the best checkpoint can be loaded for
evaluation.
 TensorBoard Integration (optional): TensorBoard, a visualization tool,
might be leveraged to monitor metrics like loss, accuracy, and
gradients during training, providing valuable insights into the training
process.
4) Evaluation
 Reconstruction Error Analysis: The reconstruction error for each
transaction in the unseen test set is calculated. This error represents
the difference between the original transaction data and its
reconstructed version by the autoencoder.
 Distribution Analysis: A crucial aspect of the evaluation involves
analyzing the distribution of reconstruction error for both normal and
fraudulent transactions. Ideally, normal transactions should have a
lower average reconstruction error compared to fraudulent
transactions. A clear distinction between these distributions
strengthens the model's ability to detect anomalies.
4. System Architecture
While the core of this project focuses on the deep autoencoder model, a
real-world credit card fraud detection system would encompass a broader
architectural design. Here's a breakdown of the potential components
involved:
1. Data Ingestion Module:
This module is responsible for continuously acquiring real-time
transaction data from various sources. Data sources could include:
Banks and financial institutions
Payment gateways
Merchant platforms
The data might be streamed or collected periodically, depending on the
system's design.
2. Preprocessing Pipeline:
The incoming data is fed into a preprocessing pipeline that performs
similar operations as those applied in the project's methodology:
Handling missing values
Scaling numerical features
Potentially performing additional data cleaning or transformation steps
This ensures the data is compatible with the autoencoder model for
anomaly detection.
3. Autoencoder Model:
The trained autoencoder model, developed in the project, serves as the
core anomaly detection engine.
Preprocessed transaction data is fed into the model.
4. Fraud Scoring and Alerting:
The autoencoder calculates the reconstruction error for each transaction.
This reconstruction error serves as an anomaly score, indicating how well
the model can reconstruct the transaction data. A pre-defined threshold is
set on the reconstruction error.
Transactions exceeding this threshold are considered potential fraud
attempts. Additionally, other factors beyond reconstruction error might be
incorporated into the fraud scoring process. These could include:
Transaction location compared to cardholder's usual location
 Time of day or night of the transaction
 Merchant category (if available)
 Past transaction history associated with the card
Based on the combined score, transactions exceeding a certain threshold
trigger alerts for further investigation.
5. Security Personnel and Action:
 Alerts are directed to security personnel or a dedicated fraud
investigation team.
 They can then analyze the flagged transactions, investigate suspicious
activity, and take appropriate actions, such as:
 Contacting the cardholder to verify the transaction
 Blocking the card if fraudulent activity is confirmed
 Initiating a chargeback process
6. Feedback Loop (Optional):
In a more advanced system, a feedback loop might be implemented.
Confirmed fraudulent transactions can be used to retrain the autoencoder
model, potentially improving its ability to detect similar fraud attempts in
the future.
This system architecture provides a comprehensive framework for
leveraging autoencoders in a real-world credit card fraud detection
scenario. By combining anomaly detection with additional risk factors
and human expertise, the system can strive to be more robust and
effective in combating financial crime.
Fig. 1 System architecture representations
5. Results:
The evaluation process aims to assess the effectiveness of the trained
autoencoder model in identifying credit card fraud. However, the
provided code snippet limits the scope of the results presented here.
1. Reconstruction Error Analysis:
The model calculates the reconstruction error for each transaction in the
unseen test set. This error signifies the discrepancy between the original
transaction data and its reconstructed version by the autoencoder.
A crucial aspect of the analysis involves examining the distribution of
reconstruction error for both normal and fraudulent transactions. Here's
what we expect to observe:
 Normal Transactions: Ideally, these transactions should have a lower
average reconstruction error. Since the model was trained on normal
transactions, it should be able to reconstruct them effectively,
resulting in a smaller difference between the original and
reconstructed data.
 Fraudulent Transactions: As fraudulent transactions deviate from the
patterns the model learned during training, the reconstruction error is
expected to be significantly higher. This indicates a substantial
difference between the original transaction and the autoencoder's
attempt to reconstruct it.
A clear distinction between the reconstruction error distributions for
normal and fraudulent transactions would be a promising outcome. This
suggests the model can effectively differentiate between normal and
anomalous spending patterns.
2. Evaluation Metrics: Going Beyond Reconstruction Error
The code might include calculations of specific metrics to assess the
model's overall performance in fraud detection. These metrics could
include:
 Precision: This metric measures the proportion of identified
fraudulent transactions that are actually true positives (not false
alarms).
 Recall: This metric measures the proportion of actual fraudulent
transactions that are correctly identified by the model.
 ROC AUC (Area Under the Curve): This metric summarizes the
model's ability to discriminate between normal and fraudulent
transactions. A higher AUC indicates better performance.
3. Interpretation of Results: A Story from the Data
High values for precision and recall would indicate the model can
accurately identify fraudulent transactions while minimizing false
positives. A high ROC AUC score signifies the model's strong ability to
distinguish between normal and fraudulent transactions.
However, it's important to acknowledge the limitations. The test set
results might not perfectly reflect real-world performance, as real-world
data can be more complex and contain unseen patterns. Additionally, the
effectiveness of the model in a real-world system would depend on
factors like the chosen threshold for reconstruction error and the
incorporation of additional fraud risk factors.
4. Next Steps: Refining the Fraud Detection Approach
Based on the results, we can explore further optimization. The model
architecture or hyperparameters could be fine-tuned to improve
performance. Techniques to handle imbalanced datasets (where
fraudulent transactions are a small fraction of the total data) might also be
explored if applicable.
Ultimately, the goal is to integrate the model into a broader fraud
detection system, as outlined in the System Architecture section. This
would allow for a more comprehensive approach to combating credit card
fraud.
6. Conclusion
1. Unveiling Fraudulent Patterns with Deep Autoencoders
This project investigated the potential of deep autoencoders for credit
card fraud detection. The methodology employed a well-structured
approach: data preprocessing, autoencoder model construction, training,
and evaluation. While the specific results are limited by the provided
code snippet, the analysis focused on reconstruction error and, potentially,
other evaluation metrics.
The key takeaway lies in the ability of autoencoders to learn the
underlying patterns of normal transactions. This allows them to identify
deviations from these patterns, potentially signaling fraudulent activity. A
clear distinction between the reconstruction error distributions for normal
and fraudulent transactions would be a strong indicator of the model's
effectiveness.
2. Looking Forward:
Despite the limitations, this project lays a foundation for further
exploration. Here are some key areas for consideration:
Model Optimization: The model architecture and hyperparameters could
be further optimized based on the obtained results. Techniques like grid
search or random search can be employed to identify the best
configuration for the model.
Imbalanced Dataset Handling: If the dataset is imbalanced, with a
significant skew towards normal transactions, techniques like
oversampling or undersampling the minority class (fraudulent
transactions) could be explored to improve model performance.
Real-World Integration: The model can be integrated into a
comprehensive fraud detection system, as outlined in the System
Architecture section. This system would combine the autoencoder's
anomaly detection capabilities with other fraud risk factors and human
expertise for a more robust approach.
Continuous Learning: In a real-world setting, the model would benefit
from continuous learning. New data containing evolving fraud patterns
can be used to retrain the model, ensuring it remains effective in the face
of an ever-changing threat landscape.
The Broader Impact: A Step Towards a Secure Financial Future
By leveraging the power of deep learning, this project contributes to the
ongoing fight against credit card fraud. By effectively identifying
anomalies, autoencoders can play a role in protecting consumers and
financial institutions alike. As research and development in this area
continue, we can expect even more sophisticated and effective solutions
to emerge, paving the way for a more secure financial future.
7. References
1. Valkov, V. (2017, August 10). Credit Card Fraud Detection using
Autoencoders in Keras — TensorFlow for Hackers (Part VII).
https://venelinvalkov.medium.com/credit-card-fraud-detection-usingautoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd
(This reference provides a good introduction to using autoencoders
for credit card fraud detection and includes Keras code examples)
2. **Hamel, P., Issaoui, R., & Mbarki, M. (2019, April). Credit Card
Fraud Detection Using Machine Learning. Procedia Computer
Science, 159, 297-303.
https://www.sciencedirect.com/science/article/abs/pii/S00457906220
03822 (This paper offers a broader overview of machine learning
techniques for credit card fraud detection, including autoencoders)
3. Ngai, E., Yeung, D., & Kwok, J. (2008, August). Learning patterns in
social networks for credit card fraud detection. In International
Conference on Data Mining (ICDM) (pp. 877-886). IEEE.
https://ieeexplore.ieee.org/document/9755930 (This reference
explores the use of social network data in conjunction with
transaction data for fraud detection)