Research on privacy fraud detection of Logistic regression based on homomorphic encryption

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

Purpose: This research aims to investigate the impact of internal controls on fraud detection in manufacturing firms in Garissa County, Kenya. The study assesses the influence of Internal Control, Risk Assessment, Control Activities, Information and Communication, and Monitoring on fraud detection in these companies.
 Methodology: Utilizing a quantitative cross-sectional survey design, data is collected from 75 manufacturing companies affiliated with the Kenya Association of Manufacturers (KAM) through purposive sampling. Internal Audit Managers (IAM) are key respondents, and primary data is collected using structured questionnaires distributed through drop-and-pick method. The collected data is analyzed descriptively using figures and tables, and inferentially using Microsoft Office spreadsheet program (Excel) and SPSS. The research instruments' validity and reliability are established through the test-retest method, and outcomes are presented using charts and tables.
 Findings: The study reveals that Internal Control, risk assessment, control activities, information and communication, and monitoring all positively and significantly influence fraud detection in manufacturing firms in Kenya. The study concludes that top management in manufacturing firms should establish clear lines of authority and allocate duties effectively, making each employee accountable for their actions. Additionally, the management should adopt effective fraud detection and prevention methods, motivate employees to report fraudulent activities by offering rewards, and formulate clear duties and responsibilities for all staff members.
 Unique Contribution to Theory, Practice and Policy: The study recommends proper communication channels within the organization, regular seminars for creating awareness on fraud detection methods, and punitive measures for employees engaged in fraudulent activities.

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

This paper examines some special fraud patterns called evasive fraud patterns (which are types of evasive fraudulent behaviours) caused by a set of marginal fraud risk factors (i.e. individual fraud risk factors) which have inter-dependencies in the form of negative correlations. These inter-dependencies, if not captured properly, may prevent a bank's fraud detection system from detecting such evasive fraud patterns effectively. Using corporate m-banking (m-banking) as an application context, this paper proposes a novel fraud detection method, the CFD method, to address this open issue. The CFD method measures an aggregated fraud risk value for a given corporate m-banking transaction. Different from existing methods, which typically assume that the different marginal fraud risk factors are independent of each other, the CFD method can capture evasive fraud patterns caused by fraud risk factors that are inter-dependent or independent of each other. Evaluation results using Monte Carlo simulations showed that the CFD method is more effective in detecting evasive fraud patterns than the independence-based aggregation method.

Abstract- Transparency, regulatory compliance, and trust in financial ecosystems depend on the integrity of financial reporting pipelines. Financial reporting fraud remains a widespread problem with serious repercussions for markets and stakeholders. Ensuring data quality at every stage is crucial for operational efficiency and successful fraud detection, as businesses depend increasingly on automated data pipelines for real-time financial reporting. With an emphasis on fraud detection capabilities, this paper investigates the development and application of comprehensive data quality frameworks suited to financial reporting pipelines. Post-hoc audits and manual checks are frequently the mainstays of traditional fraud detection methods, which are inadequate for managing large volumes of financial data at high speeds. The credibility of analytics and regulatory reporting can also be weakened by missing, erroneous, and inconsistent data in reporting systems, which can conceal fraudulent activity or produce false positives. Therefore, a strong data quality framework must incorporate domain-specific integrity constraints, anomaly detection logic, lineage tracing mechanisms, and enforce standard data validation rules. In line with anti-fraud goals, this study suggests a structured framework for data quality that combines rule-based, statistical, and metadata-driven validation procedures. Five essential pillars—completeness, accuracy, consistency, timeliness, and integrity—are incorporated into the framework presented in this paper. To identify questionable anomalies and deviations early, each pillar is mapped to particular validation mechanisms, including cross-ledger balancing, reference reconciliation, threshold-based monitoring, schema enforcement, and duplication checks. By incorporating these dimensions into ETL pipelines, organizations can proactively evaluate and score the quality of incoming data and flag records that might point to fraudulent manipulation, such as manipulated ledger entries, underreported liabilities, or revenue misstatements. The framework is integrated into a modular architecture that guarantees practical applicability with contemporary cloud-native data platforms and legacy systems. It uses tools like SQL-based integrity rules to identify transaction irregularities, Apache Atlas to track lineage and transformations, and Apache NiFi to orchestrate validation workflows. Financial controllers and compliance officers are also given access to real-time metrics and dashboards to monitor fraud risk indicators and data quality scores. This study uses a synthetic financial dataset enhanced with known fraud scenarios to assess the efficacy of the suggested framework. The analysis shows that while low-quality data introduces a lot of noise and lowers model reliability, high data quality scores are strongly correlated with fewer false positives in fraud detection models. The study also demonstrates how incorporating data quality validation early in the pipeline lifecycle enhances reporting output trust and speeds up the identification of fraudulent trends. This framework aids in the creation of safe, audit-ready, and compliant financial reporting pipelines by coordinating data quality assurance with fraud detection objectives. Additionally, it fills a significant void in managing financial data, where data quality is frequently viewed as an operational issue rather than a security or compliance requirement. The research's conclusions apply to financial institutions, regulators, auditors, and tech companies who want to improve financial reporting systems' resistance to fraud. This paper argues for a paradigm change in which data quality frameworks are essential to financial reporting fraud detection rather than being merely incidental. Adopting such frameworks will be crucial to creating robust, transparent, and reliable financial ecosystems as the financial sector accelerates digital transformation. Future research could build on this work by incorporating machine learning methods into the data quality scoring process, enabling even more automation and accuracy in fraud detection workflows. Keywords: Data quality, fraud detection, financial reporting pipelines, ETL validation, financial integrity, data lineage, anomaly detection, audit compliance, data governance, metadata-driven validation.

Credit card fraud has caused a huge loss to both banks and consumers in recent years. Thus, an effective Fraud Detection System (FDS) is important to minimize the loss for banks and cardholders. Based on our analysis, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Furthermore, due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make FDS difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we propose a framework to train a fraud detection model using behavior features with federated learning, we term this detection framework FFD (Federated learning for Fraud Detection). Different from the traditional FDS trained with data centralized in the cloud, FFD enables banks to learn fraud detection model with the training data distributed on their own local database. Then, a shared FDS is constructed by aggregating locally-computed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. Furthermore, an oversampling approach is combined to balance the skewed dataset. We evaluate the performance of our credit card FDS with FFD framework on a large scale dataset of real-world credit card transactions. Experimental results show that the federated learning based FDS achieves an average of test AUC to 95.5%, which is about 10% higher than traditional FDS.

With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.

Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier

Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain

Personal, commercial, and economic impacts of financial fraud are significant. Traditional rule-based fraud detection systems often generate false positives and struggle to adapt to new fraud methods. ML algorithms are examined as an alternative to traditional financial fraud detection methods in this chapter. It explores how supervised, unsupervised, and hybrid models may identify financial data abnormalities and fraud patterns. Money laundering, payments fraud, and other financial crimes are covered in this chapter. Fraud detection is complicated by data imbalance, privacy concerns, and model scalability. It also shows how AI, blockchain, and predictive analytics will fight fraud and secure financial institutions. This chapter reviews how new technology is changing financial crime prevention, using examples from credit card and online retail fraud.

The area of fraud detection has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews various existing learning methods for financial fraud detection of credit card fraud across different areas and find out their strengths and weaknesses. This paper has developed a credit card fraud detection system using XGBoost (eXtreme Gradient Boosting) method. In the proposed system, data imbalance problem has also been resolved by using SMOTE. Results are evaluated and compared with many states of art methods & found that the presented method performs well in achieving more accurate results after resolving data imbalance.

Mobile payment systems are becoming more popular due to the increasing number of smartphones, attracting fraudsters' attention. Therefore, existing researchers have developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available, and their detection performance is negatively affected by severe class imbalance in financial fraud data. This study aims to propose a new model entitled Vector Result Rate (VRR) for fraud detection based on deep learning while considering the economic consequences of fraud detection systems. The proposed framework is experimentally implemented on a large dataset containing more than six million mobile phone transactions. A comparative evaluation of existing machine learning methods designed to model unbalanced data and detect outliers is performed for the comparison. The results show that the VRR achieves the best results by integrating several classification algorithms with supervision and classifiers regarding standard classification criteria.

Fraud in public assistance programs, such as the Supplemental Nutrition Assistance Program (SNAP) and Electronic Benefit Transfer (EBT), poses significant economic and social challenges, diverting vital resources from vulnerable populations. Traditional fraud detection methods, including manual audits and static rule-based systems, have proven insufficient to address the complexity and adaptability of modern fraudulent schemes. This paper proposes a conceptual framework for AI-powered fraud detection, emphasizing the use of machine learning, anomaly detection, and predictive analytics to combat fraud effectively. The framework addresses systemic challenges, including evolving fraud tactics, sector-specific issues, and technological barriers such as data privacy and scalability. It highlights the core components of AI-driven systems, ensuring interoperability across public assistance programs and e-commerce platforms. Ethical considerations, such as transparency, fairness, and accountability, are integrated into the framework to prevent algorithmic bias and protect beneficiaries' rights. The paper also explores AI adoption's economic and social implications, outlining the potential for cost savings, operational efficiency, and improved equity in benefit distribution. Finally, strategic recommendations are provided to support the ethical design, sector-agnostic deployment, and continuous improvement of AI-based fraud detection systems. By addressing these challenges, this paper aims to contribute to a more efficient, fair, and transparent approach to public resource protection.

Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies — from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory — and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity.

Purpose: The purpose of this paper is to analyze the role of machine learning (ML) in fraudulent online payment transaction detection across industries, with a specific focus on integrating Environmental, Social, and Governance (ESG) principles to bring sustainability and ethics in fraud detection. Methodology: This study reviews the effectiveness of machine learning (ML) techniques in real-time fraud detection, presenting the efficiency of different ML techniques across industries and how those techniques can be modified to apply the principles of ESG. It checks on ethical practices concerning data, compliance with data privacy, and sustainable management, further investigating how effectively ML-driven frameworks can support fraud detection without compromising the standards of ESG. Findings: The study finds that integrating ESG principles within machine learning frameworks for fraud detection enhances both the effectiveness and ethical alignment of these systems. ML models not only support real-time fraud detection but also reinforce sustainable data management and governance practices, providing businesses with an advanced approach to mitigating cyber risks while upholding ESG commitments. Unique Contribution to Theory, Practice, and Policy: This paper contributes by advancing ESG-integrated frameworks for fraud detection, offering a sustainable and ethical model for using ML in financial systems. In practice, it provides actionable insights for industries seeking to align fraud prevention with ESG goals. The paper also suggests policy considerations, advocating for stronger ethical standards and data governance in ML applications for fraud detection.