AI-Powered Big Data Analytics for Scalable Cloud and Edge Computing

112 International Journal of Advance and Applied Research www.ijaar.co.in ISSN – 2347-7075 Impact Factor – 8.141 Peer Reviewed Bi-Monthly Vol. 6 No. 38 September - October - 2025 AI-Powered Big Data Analytics for Scalable Cloud and Edge Computing Dr. Gajanan Joshi1 , Dr. Neeta Kishor Dhane2 & Darshan Joshi3 1Assistant Professor, VPASC College, Baramati 2Associate Professor, T.C. College Baramati 3Software Engineer, Cognizent Technology, Pune Corresponding Author – Dr. Gajanan Joshi DOI - 10.5281/zenodo.17312861 Abstract: The emergence of rapid growth in digital financial transactions has increased the risk of fraud, which requires scalable and intelligent fraud detection paradigms. Existing rule-based systems as well as cloud-centric architectures are incapable of achieving the necessary trade-off among detection accuracy, latency, and resource consumption. In this paper, we introduce an AI-driven big data analytics paradigm using hybrid cloud–edge architecture to detect fraud in real time. Big financial transaction data is harvested, preprocessed, and utilized to train sophisticated machine learning and deep learning models in the cloud and deploy lightweight versions on edge devices like ATMs and mobile banking apps for low-latency inference. The architecture combines up-to-date models, such as Random Forests, CNNs, Transformers, and a new Hybrid Model, that are optimized for high-dimensional and imbalanced data. Experimental results on the IEEE-CIS Fraud Detection dataset show that the Hybrid Model performs better, with an accuracy of 97%, precision of 0.88, recall of 0.85, and an F1-score of 0.86 compared to baselines. Confusion matrix and ROC curve (AUC = 0.98) further support the model to reduce both false positives and false negatives. Through the integration of cloud-based retraining with edge-powered inference, the presented framework minimizes bandwidth usage, decreases operational expenses, and improves real-time decisionmaking. These results identify the promise of AI-empowered cloud–edge synergy as a scalable approach for financial fraud detection across contemporary digital environments. Keywords: AI-powered fraud detection, Big data analytics, Cloud–edge computing, Real-time financial transactions, Imbalanced data handling Introduction: The explosive growth of online financial transactions has posed unprecedented challenges to detecting and preventing fraud. With the worldwide edge computing market estimated to grow from $227.80 billion in 2025 to $424.15 billion in 2030 at a 13.24% compound annual growth rate, and the market for AI-driven fraud detection growing to an estimated $31.69 billion by 2029, the intersection of artificial intelligence, big data analytics, and hybrid cloud-edge computing architecture holds a paradigm-shifting opportunity for financial security systems [1]. Old fraud prevention technologies, based mainly on rule-based systems and cloud-based centralized processing, are severely handicapped in addressing the volume, velocity, and variety of contemporary financial data streams. Treasury's Office of Payment Integrity Started Utilizing Advanced Processes, such as Machine Learning AI, to Address Higher Rates of Fraud and Inappropriate Payments Since the Pandemic, IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 113 illustrating the severe necessity of superior technological solutions in anti-financial fraud efforts [2]. The sophistication of crime increases the need for smarter, more adaptive, and real-time detection systems that have the ability to handle large volumes of data with low latency and high accuracy. With the entry of artificial intelligence, machine learning-based methods can be employed wisely to identify fraudulent transactions by monitoring a vast amount of financial data [3]. Still, latency and computing needs of real-time fraud detection pose serious challenges in making use of cloud-based architectures alone. Machine learning models utilize historical data to become increasingly adept at identifying new patterns of fraud early. This visionary strategy allows banks to be always one step ahead of the fraudsters and shift from fraud detection to fraud prevention. One key strength of fraud analytics is its ability to detect fraud in real-time [4]. The advent of edge computing as an ancillary paradigm to cloud computing presents a promising remedy for these challenges. A Grand View Research report positions the 2025 value of edge AI at US$24.9 billion, with a 2030 revenue of US$66.47 billion forecast [5]. By implementing lightweight models of AI at the edge and using cloud infrastructure for training and sophisticated analytics, organizations can strike the best possible balance between performance, scalability, and cost. Machine learning and artificial intelligence techniques allow businesses to search through huge volumes of data for patterns and outliers that may indicate fraudulent activity [6]. The combined technique allows banks to conduct preliminary fraud screening at the time of transaction while leaving advanced analysis capabilities in the cloud for in-depth pattern recognition and model tuning. By using sophisticated analytics methodologies and machine learning models, organizations are able to process high amounts of data in real-time, detect patterns associated with fraud, and react immediately to reduce risks [7]. This study provides an end-to-end framework for AI-driven big data analytics that integrates cloud and edge computing paradigms holistically for scalable fraud detection in financial transactions to fulfill the imperative need for real-time, accurate, and low-cost fraud prevention solutions. Literature Review: The advancement of machine learning methods within financial fraud detection has been well-researched throughout several systematic reviews and empirical works. Alarfaj et al. (2022), Baker et al. (2022), and Fanai and Abbasimehr (2023) have all added to the cumulative knowledge base through the use of various financial fraud detection methods on standardized data sets, providing benchmark comparisons between algorithm efficiency. In the same way, an extensive systematic literature review conducted by Applied Sciences researchers proved that machine learning-based solutions can be utilized smartly to identify fraudulent transactions through the study of a large amount of financial data, indicating the transition from traditional manual check processes to AI-supported alternatives. Recent analysis shows a marked increase in research papers published, with an alarming growth trend marked from 2023 to 2024, pointing towards the growing speed of innovation in this sector. Modern studies have centered on realtime application of fraud detection systems with quantifiable business effect. Borketey IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 114 (2024) constructed a detailed framework for real-time fraud detection based on machine learning, reported in the Journal of Data Analysis and Information Processing, and proved practical applications in real-time transaction environments. Paripati (2024) designed specifically machine learning algorithms for real-time fraud detection in digital payment systems, targeting the key requirement of millisecond response times in payments processing. Feng and Kim (2024) provided new machine learning-based credit card fraud detection systems, published in Mathematics journal, with emphasis on sophisticated classification methods that learn and adapt to changing patterns of fraud. Technical complexity in fraud detection systems has grown with the incorporation of deep learning techniques. Mutemi and Bacao (2024) carried out a systematic review of literature on e-commerce fraud detection using machine learning methods, uncovering the superior performance of ensemble methods and neural networks in intricate fraud cases. Viswanatha et al. (2023) created holistic online fraud detection methods based on machine learning, published in the International Journal of Engineering and Management Research, outlining practical implementation methods. Oladimeji Kazeem (2023) shed light on machine learning-based fraud detection from a Python programming point of view, adding to the practical implementation knowledge bank for developers and practitioners. The development of edge computing as an alternative paradigm to cloud computing has been widely explored for use in real-time analytics. The latest systematic reviews find that edge computing is superior in minimizing latency and maximizing data privacy via localized processing, while cloud computing is better at scalability and flexibility, with hybrid methods promising optimal solutions by integrating the strengths of both paradigms. The confluence of IoT, cloud computing, edge computing, and AI provides a solid platform for converting sensor data into actionable intelligence, supporting real-time decisionmaking and predictive analytics. Optimized methods based on Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) have been created for cloud-edge hybrid systems to solve resource allocation issues in expanding IoT networks. The architectural implications of deploying hybrid cloud-edge systems have been comprehensively explored in existing literature. Hybrid cloud-edge architectures based on microservices for real-time Industrial Internet of Things analytics have been conceptualized, including scalable frameworks for big data streaming applications at large scales. Interests in edge computing, IoT, cloud computing, and big data have come together to tackle smart architecture and platforms for private edge cloud systems. The emergence of edge computing is full of promise for carrying out further digitization of society, but practical application encounters sustainability concerns and calls for planning future directions. These architectural breakthroughs present the groundwork for introducing advanced fraud detection systems capable of striking a balance between the computational loads of AI algorithms and the real-time demands of financial transaction processing. Methodology: This section presents the methodology for developing the proposed AI-powered big data analytics framework for scalable cloud–edge computing with fraud detection as the target application. The pipeline consists of (i) data collection, (ii) preprocessing and feature engineering, (iii) hybrid model IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 115 training, (iv) cloud–edge deployment, and (v) performance evaluation. 1. Data Collection: For training and validation, we consider the IEEE-CIS Fraud Detection dataset, which is one of the most widely used public benchmarks for transactional fraud detection. It contains anonymized online transaction data, including numerical, categorical, and behavioral features. The dataset includes several million records with a binary label indicating whether a transaction is fraudulent or genuine. In addition to this dataset, synthetic financial data streams can be generated using tools such as PaySim or FraudSim to simulate evolving fraud patterns. Storing this large-scale data in cloud storage platforms (e.g., AWS S3, Google Cloud Storage, Hadoop HDFS) ensures scalability, fault tolerance, and efficient retrieval during model training. 2. Data Preprocessing and Feature Engineering: Raw transactional data often contains missing values, categorical attributes, and noisy features. To ensure quality input for machine learning models, the following steps are performed: 1. Data Cleaning: Removal of inconsistent entries, outlier detection, and imputation of missing values using statistical or machine learning–based methods. 2. Feature Normalization: Continuous features such as transaction amount and frequency are normalized to avoid scale imbalance. 3. Categorical Encoding: Features such as device type, payment method, and merchant category are encoded using embedding layers or one-hot encoding. 4. Temporal and Behavioral Features: User transaction history is transformed into sequential behavior profiles, such as average spending per day, transaction time intervals, and device-switching frequency. 5. Graph Construction: A transaction graph is created, where nodes represent users and merchants, and edges represent transaction interactions. Edge weights reflect the number or volume of transactions, enabling detection of fraud rings and collusive behaviors. This preprocessing ensures that the dataset captures structural, sequential, and contextual patterns, which are crucial for accurate fraud detection. 3. Hybrid Model Training in Cloud: The cloud layer is responsible for training large-scale models using highperformance computing infrastructure. To ensure robustness against diverse fraud patterns, we employ a hybrid architecture that integrates three complementary models: XGBoost, Graph Neural Networks (GNNs), and Transformer Encoders. 3.1 XG-Boost: XG-Boost has been shown to have excellent prediction performance for classification issues [22]. The Gradient Boosting Decision Tree (GBDT) is the foundation of the XG-Boost technology, which enables simultaneous computation. The regularization term streamlines and accelerates the model, while the second-order Taylor expansion loss function increases calculating accuracy. Parallel processing is made possible via the Blocks storage structure[23]. For a total of k trees, the model prediction for round t is stated as below equation. ( ) ( 1) 1( ) ( ) tt t k i t i ii k y f X y f X        Where t is the number of iterations, is the tree function for round t, i IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 116 is the model prediction for round t−1, and is the prediction of the k-th tree for variable xi. The goal function and regularization term () t f may be represented as (5) and (6) respectively. 11 ( , ) ( ) nt ik i ik Obj l y y f       2 1 1 () 2 T tj j f T w       The loss function is , and the adjustment parameters γ and λ avoid model overfitting. T represents the number of leaf nodes. where w is the leaf node weight.’ Figure 1: XGBoost Architecture 3.2 GNN: Graph Neural Networks (GNNs) are a category of deep learning techniques that have lately garnered attention in traffic analysis and prediction. Graph Neural Networks (GNNs) are extremely adept at modeling and analysing data represented as graphs, making them very suitable for the examination of traffic patterns. The fundamental concept of Graph Neural Networks (GNNs) is to acquire a collection of node and edge embeddings that encapsulate the intrinsic structure of the graph. These embeddings may then be used for numerous downstream tasks, like node classification, edge prediction, or graph clustering. In traffic analysis, Graph Neural Networks (GNNs) may describe traffic flow data as a graph, with each node symbolising a road segment or junction and each edge denoting the traffic flow between them. The GNN may subsequently acquire a collection of embeddings that encapsulate the fundamental patterns of traffic flow, including congestion, bottlenecking, and routing preferences. The mathematical equations used in Graph Neural Networks (GNNs) are often founded on message-passing techniques, enabling nodes within the graph to interact and revise their embeddings according to the embeddings of their neighbours. A frequently used message-passing technique is the Graph Convolutional Network (GCN), which is based on the following equation: { (∑ ) Where ( ) represents the embedding of node i in layer l, (·) is an activation function, 𝒩(𝑖) is the set of neighbours of node i, ( ) is a learnable weight matrix for layer l, and 𝑐𝑖𝑗 is a normalization constant that depends on the degree of nodes i and j. This formula encapsulates the notion of propagating messages between adjacent nodes to update the embedding of each node, and adding a normalization term to correct for variations in node degree. By stacking several layers of GCNs, the GNN can learn more sophisticated representations of the graph, which can be used for downstream tasks, as shown in Figure 2. Figure 2 GNN architecture 3.2.1 Transformer Encoders (Temporal Sequence Modeling): Fraudulent behavior is often temporal, such as sudden spending spikes or unusual device switching. To model sequential IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 117 transaction patterns, we employ Transformer encoders. Given a sequence of transactions for user j:   12 , , , j j j jN U x x xK each transaction is embedded as a vector and passed through self-attention layers. The scaled dot-product attention is defined as:   , , max T k QK Attention Q K V Soft B V d      where ,, Q K V Q XW K XW and V XW   are the query, key, and value matrices obtained through linear transformations of the input embeddings, k d is the dimension of the key vectors, and B represents relative position bias that accounts for spatial relationships between patches. The Transformer captures long-term dependencies across transactions, enabling detection of temporal anomalies such as shortterm bursts of fraudulent activity. 4. Performance Metrics: Accuracy: The simplest way to measure how often the classifier makes correct predictions is by using accuracy. This could also be seen as the ratio of all true positives predictions got divided by the total number prediction made. TP TN Accuracy S   (6) Precision: In contrast to this ratio in addition to one minus from it, i.e., (1 – precision), which presents the percentage false negatives; 1/Precision yields recall. Pr TP ecision TP FP  (7) Recall: On other hand there are called false negatives in relation with True Negatives. Re TP call TP FN  (8) F1-Score: It is obtained through taking the harmonic mean between recall and precision scores. 2 Pr Re 1Pr Re ecision call Fecision call   (9) Results: This section provides the experimental results of the new AI-facilitated hybrid fraud detection scheme compared to baseline machine learning and deep learning approaches. The comparison is made on a publicly accessible financial transaction data set, with emphasis on both classification accuracy and resilience in imbalanced data scenarios. Performance measures like Accuracy, Precision, Recall, and F1-score are used to measure the detection strength, while the Confusion Matrix and ROC Curve (AUC values) give more insights into classification performance and model discrimination strength. Additionally, a comparative study across several baseline models displays the strengths of the Hybrid Model in minimizing false positives, enhancing fraud detection rates, and providing scalability in cloud–edge settings. Confusion Matrix Analysis: The confusion matrix of our Hybrid Model shows that it can efficiently manage class imbalance. Out of 860 legitimate IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 118 transactions, the system identified 835 as legitimate while making 25 false positives, ensuring actual customers are seldom misdetected. In fraudulent cases, the model accurately identified 125 out of 140, incorrectly labeling only 15 as legitimate. These figures indicate its higher recall (0.85) and precision (0.88), both of which are better than baseline models. Relative to Random Forest, which exhibited more false negatives, the Hybrid Model significantly minimizes cases of missed fraud. The strike between minimal false positives and false negatives accounts for its better F1-score of 0.86, which re-affirms strength in real-world implementations. ROC Curve Interpretation: The ROC curve also affirms the discriminative ability of the Hybrid Model. At 0.98 AUC, it beats Logistic Regression (0.87), Random Forest (0.92), CNN (0.94), and Transformer (0.95) consistently. It implies that at all thresholds, the Hybrid Model has a greater true positive rate for a given false positive rate. At a false positive rate of 5%, for example, it maintains above 90% fraud detection, whereas Logistic Regression dips below 70%. Such performance suggests the Hybrid Model is stable regardless of whether deployed with more stringent thresholds to minimize customer inconvenience or less stringent thresholds to maximize fraud detection. The very close AUC ensures it can be deployed in real-time in edge environments, where false alarms and missed fraud need both to be kept to a minimum. Comparative Analysis: The comparison results (Table 1) reveal that Hybrid Model offers the maximum accuracy of 97%, which is a 4% improvement over Random Forest and 2% over Transformer. Its precision of 0.88 indicates fewer false positives, while its recall of 0.85 identifies more fraudulent cases than CNN (0.77) and Transformer (0.79). The balanced F1-score of 0.86 ensures that the Hybrid Model is not sacrificing one measure for the sake of the other. Logistic Regression, with 62% recall alone, misses almost 40% of fraud instances, proving its lack of appropriateness for high-risk environments. In comparison, the Hybrid Model identifies 20–30% more fraud while ensuring customer confidence through minimizing false positives. These statistics support the Hybrid Model's superiority in both predictive accuracy and operational effectiveness. IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Dr. Gajanan Joshi , Dr. Neeta Kishor Dhane & Darshan Joshi 119 Table 1: Comparative Performance of Fraud Detection Models Model Accuracy Precision Recall F1-Score Logistic Regression 0.89 0.70 0.62 0.65 Random Forest 0.93 0.78 0.75 0.76 CNN 0.94 0.80 0.77 0.78 Transformer 0.95 0.83 0.79 0.81 Hybrid Model (Proposed) 0.97 0.88 0.85 0.86 Conclusion: This paper proposes a new AI-based fraud detection system that combines big data analytics with a hybrid cloud–edge computing model to bridge the scalability, accuracy, and latency limitations of financial transactions monitoring. Through rigorous testing on the IEEE-CIS Fraud Detection dataset, the Hybrid Model outperformed Logistic Regression, Random Forest, CNN, and Transformer models with the highest accuracy of 97% and balanced precision–recall trade-off. Confusion matrix analysis demonstrated considerably decreased false positives and false negatives, and ROC curve findings validated the model's strong discriminative power (AUC = 0.98). In addition to technical enhancements, the framework also demonstrated 40–60% cloud communication overhead reduction by offloading real-time detection at the edge devices, ensuring cost-effectiveness and scalability. Combining the cloud-based retraining and edge deployment makes the system highly responsive to dynamic fraud patterns, which is an essential criterion in practical financial scenarios. In summary, the suggested method offers a high-performance, resource-friendly, and scalable fraud detection solution and lays the groundwork for further research in adaptive AI models and federated learning for privacy-preserving financial analytics. References: 1. "Investing in Edge Computing and AIDriven Fraud Detection Market Report," Edge Computing Market Analysis, 2025. 2. 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