Protecting patients from financial scams

Financial scams have been described as the crime of the twenty-first century, representing a global challenge for agencies involved in the protection of older people at risk of financial abuse. Financial abuse is the second most common form of adult abuse, but traditionally research has focused on abuse that occurs within relationships of trust within families. Less is known about financial scams perpetrated by individuals or organisations unknown to the individual, and which specifically target older people. In part, this is because financial scams are often underreported, as victims can be reluctant to disclose their experience, posing challenges to those who have a safeguarding role. This paper discusses factors emerging from the literature that act as triggers for scam involvement. These include loneliness, emotional vulnerability linked to life events, cognitive impairment and mental capacity. The findings of a small exploratory qualitative study with older people and their carers (n = 12) who have experienced financial scams will be discussed in relation to the literature. These provide unique insights into the experience of being scammed and the impact on individual health and well-being. Key themes will be discussed in relation to social work practice and integrated working across health and social care.

A Ponzi Scheme (PS) is a fraudulent investment system that collects money from new investors to pay existing clients. Despite numerous warnings from regulatory agencies, new schemes continue to evolve, and many Nigerians have fallen victim. This paper examined the level of awareness and susceptibility to PS among youths in Kano State, Nigeria. The study was conducted among 400 (270 males and 130 females) youth using a descriptive survey approach. The participants were randomly selected based on their consents. A semi-structured questionnaire was used to collect primary data. The data was sorted and entered into the computer for data analysis using the standard statistical instrument. Statistical analysis revealed that the majority of the surveyed youths were aware of PS, the factors influencing participation, and the consequences on victims. The respondents were aware of PS via their friends, social media platforms, news websites/blogs, television/radio programs, and school. The results showed a significant difference in awareness based on gender and locality (P < 0.05): females and urban youths demonstrated higher awareness levels than males and rural youth respondents. The study recommends mass awareness campaigns and financial literacy education to reduce Nigerian youths’ susceptibility to PS and related financial scams. Keywords: Ponzi scheme awareness, regulatory agencies, financial scam, youths

Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years. However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes. However, these state-of-the-art approaches face several major limitations, including lacking interpretability, high false positive rates and the weak robustness to evasion techniques, These limitations mean that existing real-world methods for detecting Ponzi schemes are ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, we propose a heuristic-guided symbolic execution technique to identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.

India is in the midst of a rapid digital revolution, driven extensively by the Digital India initiative. This transformation has encouraged massive growth in internet usage, online banking, and digital governance, extending even into rural and underserved regions. Yet, parallel to these advancements, the country has witnessed a sharp escalation in cybercrimes. This study explores the surge in online threats in India ranging from everyday issues like financial scams and identity misuse to sophisticated challenges such as phishing, ransomware, deep fake manipulation, and cases of online sexual harassment. Drawing on NCRB statistics, real-world incidents, and expert perspectives, the research highlights how weak enforcement, insufficient digital literacy, and the constantly adapting strategies of cyber offenders intensify the problem. Ultimately, the paper underscores the pressing need for stronger cyber laws, wider awareness programs, and collective initiatives to secure India’s digital ecosystem.

The dark Web is like a secretive nook of the Internet, tucked away from the usual search engines and only reachable through specialized networks like Tor or I2P. In this murky realm, privacy and encryption serve a dual purpose, facilitating both legitimate communications that prioritize privacy and a variety of illegal activities, including drug and weapon trafficking, financial scams, data breaches, and cyber exploitation. The dark Web presents a unique set of challenges due to its complex nature and anonymity, making life tough for digital investigators. They need to navigate through encrypted systems, deal with unpredictable data, and tackle complex international laws. This paper takes a closer look at dark Web forensics, revealing how investigators can identify, extract, and preserve digital evidence in this secretive space. This highlights the importance of open-source intelligence, blockchain analysis, machine learning, and network forensics as essential tools for uncovering criminal activities and tracking illegal digital footprints. Furthermore, the study addresses the ethical and legal challenges of infiltration, surveillance, and evidence gathering in anonymous networks, stressing the importance of maintaining procedural integrity and respecting privacy rights. The paper wraps up by recommending strategic approaches to enhance international collaboration, facilitate better data sharing, and establish uniform forensic protocols to effectively combat cybercrime linked to the dark Web.

This paper describes the empirical universe of financial fraud as it has been documented in the academic literature. Specifically, it describes the different forms of fraudulent behaviour in the context of financial market activities, the prevalence and consequences of such behaviour as identified by previous research, and the economic and market structures that scholars believe facilitate it. To structure the discussion, a conceptual distinction is made between three types of financial fraud: false financial disclosures, financial scams, and financial mis‐selling. The findings of the literature review highlight a number of recent developments that scholars think have facilitated the occurrence of financial fraud, including (1) the development of new fundamental conflicts of interest and perverse incentive structures in the financial industry; (2) an influx of unsophisticated, gullible participants in the financial marketplace; (3) the increasing complexity involved in financial market transactions as a result of rapid technological, legal, and financial innovation and an ever‐widening menu of financial products; (4) an increase in the use of justified secrecy in the form of a mystification of trading models adopted by fund managers.

Recruitment fraud has emerged as a serious global concern with the rapid growth of online job portals, social media hiring, and remote recruitment practices. Fraudulent recruiters exploit job seekers through fake job offers, phishing emails, counterfeit interviews, and financial scams, causing financial loss and psychological distress. Traditional rule-based and manual verification mechanisms are insufficient to detect evolving fraud patterns in real time. This paper presents HireForensics, an intelligent recruitment fraud detection system that leverages machine learning and natural language processing (NLP) techniques to identify deceptive patterns in recruitment ecosystems. The proposed system analyzes job descriptions, recruiter messages, communication behavior, and metadata to classify recruitment activities as legitimate or fraudulent. Multiple machine learning models including Logistic Regression, Random Forest, Support Vector Machines, and ensemble classifiers are implemented and evaluated. Experimental results demonstrate that the proposed model achieves high accuracy and robustness, making HireSafe AI a scalable, cost-effective, and reliable solution for safeguarding job seekers and recruitment platforms. Keywords Natural Language Processing (NLP), Random Forest, Ensemble Learning, Heuristic Analysis, TF-IDF, Logistic Regression

The Money Smart for Older Adults is a program that is tailored to older adults to raise awareness for the risk of financial exploitation and teach them how to plan and make informed financial decisions. The purpose of this study was to examine financial circumstances of older adults in the program and to explore how the program could better support their financial well-being. Individual, in-depth interviews were conducted with 29 older adults who attended the program provided by a local agency in northern Texas. Three themes emerged when exploring financial circumstances of the participants: (1) victims of financial fraud scams, (2) struggles with money management, and (3) inability to make ends meet. The program has been serving older adults, particularly ethnically diverse older adults and low-income older adults who may not have access to financial education workshops or seminars provided by private financial institutions. The Money Smart for Older Adults Program was perceived as helpful among the participants because it raised awareness of the importance of their financial well-being and it also supported their financial decision making.

As financial institutions increasingly digitized, they are up against a growing suite of cybersecurity threats such as ransomware, cryptojacking, AI-enabled phishing, and quantum computing attacks. Other research work and government reports reinforce general cybersecurity concerns but are not technical or applied. They do not cater specifically to financial institutions. To fill these gaps, this study introduces an AI-based cybersecurity framework focusing on effective protection of financial data, compliance with regulations, and minimizing time for detecting threats.Building upon the existing research and drawing on best practices from both the financial and technology sectors, this paper presents a promising new framework that combines machine learning-driven methods for real-time fraud detection, the use of blockchain technology to ensure transaction integrity, and quantum-resistant and decentralized encryption methods to protect sensitive financial information from cyber threats. Whereas other research focuses on broad, high-level strategies, this research offers a step-by-step technical roadmap to zero-trust security, anomaly detection and automated cybersecurity responses. It also analyzes actual cyberattacks on financial institutions and develops predictive models to proactively reduce risks.To tackle on email-based financial scams, the research presents a deep learning-embedded BERT paradigm integrated with NLP to enhance phishing identification. It also introduces a biometric security mechanism that ensures that sensitive user data is unalterable, and accessible only to authorized parties. Cybersecurity measures for integrated financial IoT are proposed contribution to the NIST Cybersecurity Framework, including the prevention of cyber threats for automatic teller machines, mobile banking, and electronic payment systems. Challenges of compliance with GDPR, PCI-DSS and ISO 27001 are also addressed.Based on empirically-testing real-time financial datasets, the framework shows improved robustness to cyberattacks. These findings lay the groundwork for the future of cybersecurity, helping financial institutions stay secure and adaptive in the face of evolving cyber threats.

Financial fraud is a pervasive concern that impacts all. It can also entail the loss of an asset for the scam victim, but it also has the ability to disrupt the cultural and/or economic underpinnings of “respect” and “trust.” As a result, governments and other policymakers continue to be concerned about such threats. The law-making authorities pass legislation with the sincere goal of defending the general public interests and the country’s overall reputation in the international community. However, in recent years, numerous financial scams have been reported in countries all over the world, resulting in the loss of credibility, confidence, and capital. Forensic auditing is one of the techniques used to combat fraud and malpractice. While forensic auditing is a relatively new field, the rise in the number of frauds in recent years, as well as the establishments’ vulnerability to coping with them, has thrust the Forensic Audit into the mainstream. Unfortunately, in India, forensic audit is mostly used as an exploratory and investigative tool than a preventive tool. In this research paper an attempt has been made to evaluate various international and Indian standards, laws, and precedents that form the foundation for forensic auditing.

The COVID-19 crisis has upended the global consumer landscape. Confinement measures have moved consumers further online, changed their demands and exacerbated behavioural biases. Many mainstream consumers are now financially and psychologically vulnerable and the existing vulnerability of, for instance, the elderly or low-income consumers, has intensified. There are increasing reports of unfair, misleading and fraudulent commercial practices online. These include financial scams, false claims of coronavirus treatment or prevention, price gouging of essential goods and the promotion of unsafe or counterfeit products. Many governments must strike a balance between protecting consumers’ health and safety, strengthening consumer trust, and addressing challenges to business and workers. This has led some governments to fast-track procedures and grant exemptions from consumer laws. Increased international and cross-sector co-operation among government agencies, businesses, and civil society are key to protecting consumers. Information sharing has become more valuable than ever, particularly for agencies with limited resources.

After the emergence of financial scams like Enron, WorldCom and Toshiba, the researchers have emphasized the significance of the audit committee (AC) in curtailing the earnings management (EM). The...

The rise of AI-powered cyberattacks-especially deepfake-driven financial scams and sophisticated phishing schemes-demands an equally advanced defense paradigm.This research proposes a multi-layer AI defense model that integrates real-time threat detection, behavioral analysis, adversarial learning, and biometric authentication to counter phishing and deepfake financial fraud.Drawing from both classical and modern machine learning models, the study explores hybrid architectures combining Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs).The system is tested against synthetic fraud scenarios, showing promising precision, adaptability, and low false positives.This paper also presents a comprehensive literature review, architecture design, sequence diagrams, and comparative analysis to reinforce the effectiveness of multi-layered defense in real-world financial systems.

The exponential rise in digital era not only paves way to its many advantages of networking and communication, but also many fraudulent practices to exploit the users. WhatsApp Messenger, being one of the most frequently used instant messaging service, has in recent times being used for several untold and unsubscribed advertising. User‟s personal details have been extracted and data is being sold to many marketing agencies. These agencies then procure data, mainly the contact details of the users, to spam chat boxes with “advertisements”. In pretext of advertising and giving opportunity to the needy, WhatsApp is used to maliciously trap its users with flashy offers. These offers come with diverse applications and malignant motives, including financial scams. There has been significant growth in cyber laws and measures to protect the public from such fraud in India. The objective of this paper is to throw light on the Cyber Security and the amount of awareness provided by India to its public. This paper analyses several such Phishing practises used into duping users of various age groups, followed by its consequences. It elucidates on legal provisions relating to penalising such offenders and legislative role in providing protection to the users. The paper delves upon various prevention measures and redressal forums available in India. Conceptualizing possible ways to successfully bring down the injustice and constant invasion of privacy amongst the citizens