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Review Open access Sep 2026

Cybersecurity In Fintech Ecosystems: A Systematic Review of Threats and Security Strategies

FinTech's tremendous expansion has revolutionized the financial industry by making services like online lending, mobile banking and digital payments possible. Financial systems are becoming more vulnerable to cybersecurity risks, data breaches and financial fraud as a result of this digital transition. Strong cybersecurity has become a significant concern as financial institutions depend more and more on digital infrastructures. The research paper analyses the key vulnerabilities in digital banking systems and addresses the changing cybersecurity issues in FinTech ecosystems. In order to assess current academic research, industry reports and cybersecurity frameworks, the study uses a systematic literature review (SLR) methodology together with exploratory and descriptive research approaches. Important security algorithms utilized in financial transactions, such as AES, RSA, ECC, SHA-25 and SSL/TLS protocols, are also covered. The research found that existing solutions frequently address specific risks or separate security tasks, leading in inadequate protection across interconnected FinTech environments. Key research gaps include a lack of integration of various threat indicators, difficulties in explaining and detecting fraud in real time, interoperability concerns and insufficient integration of technological, organizational and governance-level security measures. Based on these findings, the study demonstrates the importance of an integrated and versatile cybersecurity architecture that includes multi-vector threat monitoring, behavioural analytics, intelligent fraud detection and risk management. Such hybrid method can improve detection capabilities, minimize false positives, boost data protection and increase the cyber resilience, consistency and trustworthiness of FinTech payment and financial service ecosystems.

Vedankita Mohod, R. Jugele · 0 citations
Review Open access Sep 2026

Feature Based Survey on Fake News Detection: Statistical and Semantic

The digital news portals and social media are rapidly expanding, which has significantly increased the spread of fake news, which affects public opinion, social harmony, and trust in information sources. Detection of fake news at an early stage is a critical research challenge. In recent years, researchers have applied multiple methods for the classification of news articles as fake using Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques. This paper discusses a feature-based analysis of text-oriented fake news detection methods published from 2017 to 2025. The analysis demonstrates how different textual features, such as linguistic, stylistic, psychological, statistical, semantic, and syntactic features, are used for the identification of fake or real news. A comparative analysis of existing studies shows that most research primarily depends on statistical and semantic representations like N-grams, TF-IDF, and word embeddings, whereas linguistic, stylistic, and psychological cues are comparatively less explored. In addition, syntactic features have gained very limited attention despite their potential to enhance detection performance. The review emphasizes integrating multiple feature types to develop more reliable and interpretable detection systems. It also identifies research gaps and suggests future directions for developing comprehensive feature-based frameworks for fake news detection

Itika U. Lakkewar, R. Jugele · 0 citations

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