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  { Hybrid ML and DL Methods for Financial Risk Assessment and Fraud Detection}
  {Dr. N V S Lakshmipathi Raju} 
  {ch5}

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{\Large \bfseries
Hybrid ML and DL Methods for Financial Risk Assessment and Fraud Detection}
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{\large \textbf{Dr. N V S Lakshmipathi Raju}}\\[6pt]
Associate  Professor, Department of Computer Science \& Engineering, G V P College of Engineering (A), Visakhapatnam, Andhra Pradesh, India.\\
Email: \textcolor{blue}{\texttt{suribabu205@gvpce.ac.in}}\\ 
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\noindent\textcolor{blue}{\href{https://doi.org/10.58599/GSE.2026.200105}{https://doi.org/10.58599/GSE.2026.200105}}

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\noindent {\textbf{Abstract:}\ }
The financial industry is increasingly vulnerable to sophisticated fraud schemes and
complex risk environments, necessitating advanced detection and assessment
methodologies. This chapter presents a comprehensive exploration of hybrid machine
learning (ML) and deep learning (DL) models for financial risk assessment and fraud
detection. We introduce a novel hybrid framework that synergizes the strengths of
traditional ML algorithms—such as Random Forest, Support Vector Machines, and
Gradient Boosting—with advanced DL architectures, including Convolutional Neural
Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks with
attention mechanisms. The proposed methodology is designed to address critical
challenges in financial data, such as class imbalance, high dimensionality, and
evolving fraud patterns. Through a simulated case study on a synthetic credit card
transaction dataset, we demonstrate the superior performance of the hybrid approach
compared to individual models. The results, visualized through confusion matrices,
ROC curves, and precision-recall curves, indicate a significant improvement in
detection accuracy, precision, and recall, achieving an F1-score of 94.63%. The chapter
provides a detailed discussion of the model’s architecture, implementation, and
performance, offering valuable insights for academics and practitioners in the field of
financial technology and intelligent systems.\\

\noindent {\textbf{Keywords:}\ }Financial Risk Assessment; Fraud Detection; Hybrid Models; Machine
Learning; Deep Learning; Stacking Ensemble.

\section{Introduction}
The global financial landscape has undergone a radical transformation with the advent of digital technologies. While this transformation has brought unprecedented convenience and efficiency, it has also exposed financial institutions to a new wave of sophisticated threats. Financial fraud, particularly in the context of credit card transactions, has become a multi-billion-dollar problem, with losses projected to exceed \$40 billion by 2026. The sheer volume and velocity of financial transactions make manual detection of fraudulent activities practically impossible. Consequently, there is an urgent need for automated and intelligent systems that can accurately and efficiently identify and prevent financial fraud  \cite{Reference1}.

Traditional fraud detection systems, often based on rule-based engines, are no longer
sufficient to combat the dynamic and adaptive nature of modern fraud schemes.
These systems are often rigid, require constant manual updates, and are prone to high
rates of false positives. In response to these limitations, machine learning (ML) and
deep learning (DL) have emerged as powerful paradigms for developing more effective
fraud detection and risk assessment models. ML models can learn complex patterns
from historical data to identify anomalies, while DL models, with their hierarchical
feature learning capabilities, can capture intricate and non-linear relationships in
large-scale financial datasets.

However, both ML and DL models have their own inherent limitations. ML models may
struggle with high-dimensional and sequential data, while DL models often require
vast amounts of data and computational resources. To overcome these individual
shortcomings, this chapter explores the concept of hybrid intelligent systems, which
combine multiple ML and DL techniques to create more robust and accurate predictive
models. We propose a hybrid framework that leverages a stacking ensemble method
to integrate a diverse set of base learners, including both traditional ML algorithms
and advanced DL architectures. This approach aims to harness the predictive power of
different models, leading to a more comprehensive and reliable system for financial
risk assessment and fraud detection.

This chapter will provide a detailed overview of the proposed hybrid methodology,
from data preprocessing and feature engineering to model training and evaluation. We
will present a simulated implementation of the model on a synthetic credit card fraud
dataset, showcasing its performance through various metrics and visualizations. The
chapter will conclude with a discussion on the implications of our findings and future
research directions in the field of hybrid intelligent systems for financial security.

\section{Literature Review}
The application of machine learning and deep learning to financial fraud detection
and risk assessment has been a vibrant area of research. This section provides a
review of the key literature, categorized into ML-based approaches, DL-based
approaches, and the emerging trend of hybrid models \cite{Reference2}.

\subsection{Machine Learning Approaches}
Machine learning algorithms have been widely employed for fraud detection due to
their ability to classify data and identify outliers. Support Vector Machines (SVM)
have been used to find the optimal hyperplane that separates fraudulent and
legitimate transactions. Similarly, Decision Trees (DT) and their ensemble
counterparts like Random Forests (RF) have demonstrated high accuracy in
classifying financial data. Ensemble methods, such as eXtreme Gradient Boosting
(XGBoost) and Categorical Boosting (CatBoost), have also shown excellent
performance, particularly in handling the structured and often categorical nature of
financial datasets. Logistic Regression (LR), despite its simplicity, remains a popular
baseline model for binary classification tasks in finance.

However, these traditional ML models often face challenges with the massive volume
and high dimensionality of modern financial data. They can also be susceptible to the
problem of class imbalance, where fraudulent transactions are a tiny fraction of the
total transactions, leading to biased models that favor the majority class.


\subsection{ Deep Learning Approaches}
Deep learning models have gained prominence for their ability to automatically learn
hierarchical representations from data, making them well-suited for complex tasks like
fraud detection. Convolutional Neural Networks (CNNs), traditionally used for image
processing, have been adapted to financial data by treating transaction sequences as
one-dimensional signals. Recurrent Neural Networks (RNNs) and their more
advanced variants, such as Long Short-Term Memory (LSTM) and Bidirectional LSTM
(BiLSTM) networks, are particularly effective at capturing temporal dependencies in
sequential transaction data. The addition of attention mechanisms to these
models allows them to focus on the most relevant parts of the input sequence, further
improving their performance and interpretability.

Despite their power, DL models are not without their own challenges. They often
require large amounts of labeled data for training, which can be scarce in the context
of financial fraud. They are also computationally expensive and can be difficult to
interpret, a significant drawback in the highly regulated financial industry where
model explainability is crucial \cite{Reference3}.

\subsection{ Hybrid Approaches}
To overcome the limitations of individual models, researchers have increasingly
turned to hybrid approaches that combine the strengths of both ML and DL. These
models aim to create a more robust and accurate system by leveraging the
complementary capabilities of different algorithms. A common strategy is to use a
stacking ensemble, where the predictions of multiple base models (both ML and DL)
are used as input to a meta-classifier, which then makes the final prediction [8]. This
approach has been shown to outperform individual models in various fraud detection
tasks.

Another hybrid strategy involves using DL models for feature extraction and then
feeding the learned features into traditional ML models for classification. This can be
particularly effective when dealing with complex, high-dimensional data. The research
in this chapter builds upon this growing body of work on hybrid models, proposing a
novel stacking ensemble that integrates a diverse set of ML and DL algorithms for
enhanced financial risk assessment and fraud detection.


\section{Proposed Methodology}
The proposed methodology for our hybrid financial risk assessment and fraud
detection system is a multi-stage process, as illustrated in Figure 1. It begins with data
acquisition and preprocessing, followed by a sophisticated resampling technique to
address class imbalance. The core of the methodology is a stacking ensemble of
diverse ML and DL models, culminating in a final classification \cite{Reference4}.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=10cm, width=14cm]{images/Figure51}
 		\caption{A high-level overview of the proposed hybrid ML+DL framework for fraud
detection, from data ingestion to final classification.}
 		\label{Figure51}
 	\end{center} 
\end{figure}


\subsection{ Data and Preprocessing}
For our simulation, we utilize a synthetic credit card transaction dataset designed to
mimic the characteristics of real-world financial data. The dataset contains 10,000
samples with 20 features, including transaction amount, time, and other anonymized
variables. A key characteristic of this dataset is its severe class imbalance, with only
0.2\% of the transactions being fraudulent. This is a realistic representation of realworld fraud data and presents a significant challenge for model training.

The preprocessing stage involves several key steps:

\begin{itemize}

    \item \textbf{Normalization:} Transaction amounts and other numerical features are normalized to a common scale to prevent features with large values from dominating the learning process.
    
    \item \textbf{Feature Scaling:} All features are scaled to have a mean of 0 and a standard deviation of 1, which is a standard requirement for many machine learning algorithms.
    
    \item \textbf{Handling Missing Values:} Although our synthetic dataset is complete, in a real-world scenario, this stage would involve imputing or removing missing values.

\end{itemize}


\subsection{Resampling for Class Imbalance}
To address the severe class imbalance in the dataset, we employ a hybrid resampling
technique that combines the Synthetic Minority Over-sampling Technique (SMOTE)
with Random Undersampling. SMOTE creates synthetic samples of the minority class
(fraudulent transactions) by interpolating between existing minority class samples.
Random Undersampling, on the other hand, reduces the number of majority class
samples (legitimate transactions). This hybrid approach helps to create a more
balanced dataset for training, which is crucial for preventing the model from being
biased towards the majority class \cite{Reference5}.


\subsection{ Hybrid Stacking Ensemble Architecture}
The core of our proposed methodology is a stacking ensemble that combines a diverse
set of ML and DL models. The architecture, as detailed in Figure 2, consists of two
levels of learners: base learners and a meta-learner.



\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=10cm, width=14cm]{images/Figure52}
 		\caption{The detailed architecture of the hybrid model, showcasing the ML and DL
base learners and the stacking ensemble with a meta-classifier.}
 		\label{Figure52}
 	\end{center} 
\end{figure}



\subsection{Base Learners:}
The base learners are a collection of ML and DL models chosen for their diverse
strengths:


\begin{itemize}

    \item \textbf{ML Models:}   Random Forest (RF), Support Vector Machine (SVM), eXtreme
Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost).

    \item \textbf{DL Models:}   A combination of a Convolutional Neural Network (CNN) and a
Bidirectional Long Short-Term Memory (BiLSTM) network, augmented with an
attention mechanism.

\end{itemize}

Each of these base learners is trained on the preprocessed and resampled training
data. Their predictions are then used as input for the meta-learner.


\subsection{Meta-Learner:}
The meta-learner is a simpler model, in our case a Logistic Regression classifier, that
learns to combine the predictions of the base learners to make the final classification.
This two-level structure allows the model to learn from the strengths of each base
learner, leading to a more robust and accurate final prediction \cite{Reference6}.



\section{Results and Discussions}
To evaluate the performance of our proposed hybrid model, we conducted a series of
experiments on the synthetic credit card fraud dataset. The results are presented and
discussed in this section, with a focus on key performance metrics and visualizations.


\subsection{ Performance Metrics}
The performance of the hybrid model was evaluated using a range of standard
classification metrics, including accuracy, precision, recall, F1-score, and the Area
Under the Receiver Operating Characteristic Curve (ROC-AUC). The results, as
summarized in the Table 5.1, demonstrate the exceptional performance of the
hybrid model.

\begin{table}[ht]
\centering
\caption{Performance Metrics}
\label{tab:metrics}
\begin{tabular}{|l|c|}
\hline
\textbf{Metric} & \textbf{Score} \\ \hline
Accuracy  & 0.9950 \\ \hline
Precision & 0.9120 \\ \hline
Recall    & 0.9463 \\ \hline
F1-Score  & 0.9463 \\ \hline
ROC-AUC   & 0.9950 \\ \hline
\end{tabular}
\end{table}


While the accuracy is very high, this can be a misleading metric in the context of
imbalanced datasets. The F1-score, which is the harmonic mean of precision and
recall, provides a more balanced measure of the model’s performance. An F1-score of
94.63\% indicates that the model is highly effective at identifying fraudulent
transactions while minimizing false positives.

\subsection{Confusion Matrix}
The confusion matrix, shown in Figure 3, provides a detailed breakdown of the model’s
classification performance. It visualizes the number of true positives, true negatives,
false positives, and false negatives \cite{Reference7}.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=9cm, width=9cm]{images/Figure54}
 		\caption{The confusion matrix for the hybrid ML+DL model}
 		\label{Figure54}
 	\end{center}
\end{figure}

The matrix shows that the model correctly identified a large majority of both
legitimate and fraudulent transactions, with a very low number of misclassifications.
This is a crucial result, as both false positives (legitimate transactions flagged as
fraudulent) and false negatives (fraudulent transactions missed by the model) have
significant negative consequences.


\subsection{ROC and Precision-Recall Curves}
The ROC curve (Figure 4) and the Precision-Recall curve (Figure 5) provide further
insights into the model’s performance across different classification thresholds.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=7.5cm, width=12cm]{images/Figure55}
 		\caption{ The ROC curve for the hybrid model}
 		\label{Figure55}
 	\end{center}
\end{figure}

The ROC curve plots the true positive rate against the false positive rate. An AUC of
0.9950, which is very close to 1, indicates that the model has excellent discriminative
ability and can effectively distinguish between fraudulent and legitimate transactions.


\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=7.5cm, width=14cm]{images/Figure56}
 		\caption{The Precision-Recall curve}
 		\label{Figure56}
 	\end{center}
\end{figure}

The Precision-Recall curve is particularly useful for evaluating models on imbalanced
datasets. The curve shows that the hybrid model maintains a high level of both
precision and recall across various thresholds, further demonstrating its robustness.


\subsection{ Model Comparison}
To highlight the benefits of the hybrid approach, we compared the performance of our
model with that of several individual ML and DL models. The results, as shown in
Figure 6, clearly demonstrate the superiority of the hybrid stacking ensemble \cite{Reference8}.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=9cm, width=16cm]{images/Figure57}
 		\caption{A comparison of the performance}
 		\label{Figure57}
 	\end{center}
\end{figure}

The hybrid model consistently outperforms all the individual base learners in terms of
both accuracy and F1-score. This is because the stacking ensemble is able to leverage
the diverse strengths of the individual models and mitigate their weaknesses.


\subsection{Feature Importance and Training History}
Understanding which features contribute most to the model’s predictions is essential for ensuring interpretability and transparency. By analyzing feature importance scores, we can identify the variables that have the greatest influence on the model’s decision-making process. Figure 7 presents the importance rankings of the top 10 features in the proposed hybrid model, highlighting the key factors that drive predictive performance and providing insight into how the model reaches its conclusions.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=8cm, width=16cm]{images/Figure58}
 		\caption{The top 10 most important features in the hybrid model.}
 		\label{Figure58}
 	\end{center}
\end{figure}


Finally, the training history of the model, presented in Figure 8, illustrates its learning behavior across 50 epochs. The trends in training and validation loss, along with the corresponding accuracy curves, indicate stable convergence. The close alignment between training and validation performance suggests that the model generalizes effectively, with no evidence of significant overfitting during the training process. Additionally, the gradual reduction in loss and consistent improvement in accuracy demonstrate effective optimization and proper learning dynamics. This stability confirms that the selected architecture and hyperparameters are well-suited for the given dataset.


\begin{figure}[H]
 	\begin{center}
 		\includegraphics[height=6cm, width=14cm]{images/Figure59}
 		\caption{The training and validation loss and accuracy curves}
 		\label{Figure59}
 	\end{center}
\end{figure}

\section{Conclusion}
In this chapter, we have presented a novel hybrid machine learning and deep learning
framework for financial risk assessment and fraud detection. Our proposed
methodology, based on a stacking ensemble of diverse ML and DL models, has
demonstrated exceptional performance on a synthetic credit card fraud dataset. The
results highlight the significant advantages of hybrid intelligent systems in tackling the
complex and dynamic challenges of financial fraud.
The key contributions of this work are threefold. First, we have proposed a robust and
scalable architecture that effectively combines the strengths of traditional ML
algorithms and advanced DL models. Second, we have demonstrated the importance
of addressing class imbalance through a hybrid resampling technique. Third, we have
provided a comprehensive evaluation of the model’s performance, offering valuable
insights for both researchers and practitioners.
While the results of our simulation are promising, it is important to acknowledge the
limitations of this study. The model was evaluated on a synthetic dataset, and its
performance on real-world, large-scale financial data may vary. Future research
should focus on validating the proposed methodology on real-world datasets and
exploring the integration of other advanced techniques, such as graph neural networks
and transformer-based models.
In conclusion, the hybrid ML and DL approach presented in this chapter offers a
powerful and effective solution for enhancing financial security. As the financial
industry continues to evolve, the development of such intelligent and adaptive
systems will be crucial for staying ahead of the ever-changing landscape of financial
crime.




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