Executive Summary
A crucial need arises to assess the risks to consumer financial health and develop risk mitigation techniques due to the fast expansion of credit card-based wealth management solutions offered by banks and fintech businesses. The proposal details a research plan to identify major threats related to overspending, security vulnerabilities, and lack of transparency through a thorough literature review. It aims to analyze credit card transaction data and user survey data to quantify risk factors empirically. The ultimate goal is to develop policy and product reforms to address these identified hazards.
The literature consolidates research demonstrating that credit cards tend to promote overspending via monetary incentives and decreased payment resistance. Advanced financial technology systems may bring about hidden artificial intelligence prejudices, unfair treatment, and privacy weaknesses that jeopardize secure investment practices. Elaborate incentive systems and ambiguous financial product phrases make comprehension difficult. Research identifies risk categories, but gaps exist in understanding the psychological and regulatory factors that cause damage at both individual and systemic levels.
Anonymized card transaction data will be used methodically to do descriptive analytics on spending and debt trends. User surveys will gather experiences and impressions. By combining quantitative transaction mining with qualitative analysis of attitudes, problems of inequality may be brought to the forefront. Creating risk matrices will methodically outline dangers and their corresponding mitigations.
The suggested mixed methods investigation focuses on elucidating the choices between potential innovation and unforeseen repercussions as financial technology progresses rapidly. Enhancing empirical evidence on risks and solutions may bolster consumer safeguards during times of change. Vigilant governance can ensure that innovative card-based wealth management products do not compromise financial security. However, maintaining ethical development requires proactive and multidisciplinary examination. This proposal provides a model for handling the challenging balancing act of fintech via thorough multiperspective research.
Background
The rapid pace of advancement in the fintech industry has created challenges in conducting thorough risk evaluations, prompting warnings about credit card-based wealth management offerings (Ashta & Herrmann, 2021). New technologies such as AI, big data analytics, and blockchain provide advantages but also present threats due to their intricacy. Companies expedite product launches without thoroughly assessing consequences, and unexpected malfunctions or breaches might greatly hinder users’ financial objectives. Regulation and monitoring are insufficient yet necessary in the perceived unregulated fintech industry (Ashta & Herrmann, 2021, p. 212).
New fintech-enabled credit card products have a significant danger of exacerbating economic inequality (Broby, 2021). Integrating wealth management tools into the current credit card system may benefit high-net-worth clients but also exacerbate the financial challenges faced by those in debt. Approximately 80% of Americans rely on their paychecks for their living expenses, and more than 40% do not have enough savings to compensate for a $400 unexpected cost (Broby, 2021). Complex financial technology products might exacerbate existing issues caused by self-reinforcing cycles of late fees and excessive interest rates, thereby worsening financial situations for those already struggling.
Artificial intelligence and machine learning are versatile technologies that provide both opportunities and risks. AI techniques may improve credit card fraud detection but may also automate bias. Prejudiced data and algorithms may lead to biased lending choices that result in the exclusion of protected groups. Fintech companies promote the impartiality of AI but often neglect to thoroughly assess biases and mistake rates in training data across different client populations. AI systems with good intentions might unintentionally maintain unfairness in financial services (Khalid et al., 2022, p. 2).
Climate change hazards increasingly endanger global financial systems, while credit card-based wealth management solutions often overlook environmental sustainability (Morgan, 2022). Physical climate disasters and transition risks affect profitability, yet many companies engage in “greenwashing” by using superficial ESG branding. Wealth management services that promote travel incentives contribute to carbon emissions without any offsets. Improving climate risk disclosures and incorporating green finance standards into credit card wealth products might assist in mitigating shared environmental vulnerabilities (Morgan, 2022).
AI and machine learning techniques may aid in identifying credit card fraud, but over-dependence on them might reduce alertness and stifle ethical inquiry (Carrasco & Sicilia-Urbán, 2020). Fraud analysts rely heavily on algorithms, diminishing their ability to thoroughly examine atypical instances. Warning indicators are overlooked, and innocent people are unjustly burdened with identifying errors that impact their accounts. Blindly trusting automated choices reduces the likelihood of seeking contradictory evidence. Human monitoring and judgment are crucial for providing necessary checks and balances (Carrasco & Sicilia-Urbán, 2020).
Credit card-based wealth management typically makes use of behavioural finance biases to intentionally raise client risk tolerances over acceptable levels (Patel, 2023). Presenting attractive card incentive programs to promote wealth may lead to overconfidence, extrapolation, hyperbolic discounting, and other cognitive biases. Consumers irrationally prioritize immediate advantages above long-term expenses. Simple fintech products might encourage those with insufficient financial knowledge to have unrealistic beliefs about the dangers involved in stock investing. Enhanced ethical principles and clear linguistic standards might reduce negative impacts (Patel, 2023).
While innovative fintech payment systems provide governments with appealing advantages like transparency, speed, and data insights, they also bring up worries about financial integrity (Uña et al., 2023). Anonymous prepaid cards and mobile money platforms, facilitated by e-money licensing, might facilitate corruption, deception, or the funding of terrorists. Complex technology complicates inquiries and obscures documentation. Regulations in most nations have not evolved quickly enough, posing a challenge to public financial management. It is crucial to expand financial technology governance standards with financial advancements (Uña et al., 2023).
Research Question
- What are the key risks associated with credit card-based wealth management products and how can they be mitigated?
Hypothesis
Wealth management via credit cards is likely to include more risks of overspending and incurring large interest charges when compared to conventional investment solutions. Enforcing spending restrictions, providing balance transfer opportunities, and offering financial education initiatives may assist reduce these risks.
Prospective Dataset.
An extensive dataset is necessary to thoroughly assess risks and mitigation measures related to credit card-based wealth management solutions. Potential sources of pertinent data consist of credit card transaction records from prominent banks and financial technology companies providing these services, together with survey data that captures user viewpoints.
Anonymous transaction data illustrating credit card transactions, payments, reward redemptions, and balances chronologically might provide important insights (Carrasco & Sicilia-Urbán, 2020). Specific data may reveal how money is spent, how interest grows, and how users interact with card features that encourage financial management. Analyzing data to discover unusual situations that suggest potential fraud concerns may be developed using descriptive analytics (Patel, 2023). Collaborating with top card issuers such as American Express, Chase, and Citi would provide access to comprehensive data on a large scale required for machine learning methods.
Supplementing transactional data with surveys that collect direct input from consumers of credit card-based wealth management products would be beneficial (Ashta & Herrmann, 2021). Both closed-ended risk perception tests and open-ended qualitative reports may reveal strengths, faults, and unexpected effects from consumer perspectives. Online panels from vendors like Qualtrics enable a variety of cardholder viewpoints (Broby, 2021). Highlighting demographic disparities is crucial for assessing the effects of products on economic parity. By combining use statistics with user surveys, it is possible to triangulate risk detection and prioritize risk reduction.
The transactional and survey data serve as two essential components—behavioural insights linked to attitudinal viewpoints (Morgan, 2022). The dataset includes both quantitative and qualitative data to facilitate mixed methods analysis for enhancing credit card-based wealth management solutions in the ever-changing fintech industry.
Methodology
The quantitative study of the credit card transaction dataset will concentrate on revealing spending and debt trends linked to wealth management solutions. Methods such as cluster analysis, sequence mining, and principal component analysis may uncover customer archetypes and typical patterns based on real use habits (Noviandy et al., 2023). Statistical models will also depict the connections between card attributes and interest costs. Notable impacts may identify the causes of consumer debt, a significant risk factor that hinders wealth objectives (Khalid et al., 2022).
Analyzing open-ended survey answers using qualitative coding and theme analysis will effectively convey the depth of users’ viewpoints and experiences. Consumers’ intricate interpretations of credit card benefits and the resulting psychological connections will become apparent. Inductive grounded analysis allows for unexpected discoveries that go beyond predetermined frameworks. Analyzing qualitative themes across different demographic groups may highlight disparities in society and the importance of diversity and inclusivity. Client interviews or focus groups may provide additional insights to complement the survey data for a more comprehensive understanding (Uña et al., 2023).
A risk matrix will be created by combining insights from transaction data analytics and thematic survey research to outline potential challenges to achieving good wealth management results (Ashta & Herrmann, 2021). The framework will identify risks such as overspending, interest, fraud, inequity, and climate implications in comparison to potential mitigation strategies. Afterwards, mitigations may be ranked based on their practicality and potential to reduce risk. This study combines quantitative and qualitative methodologies to analyze complex factual data and provide relevant policies and suggestions.
Literature Review and Research Gaps
Banks and fintech businesses have quickly introduced credit card-based wealth management solutions, but there has been a delay in analyzing the associated risks and implementing necessary protections (Ashta & Herrmann, 2021). These services enable individuals to invest and accumulate money by using credit card rewards, cash-back incentives, and airline mile redemption schemes (Patel, 2023). The simplicity and attractiveness of accumulating money via daily credit card use come with significant concerns such as overspending, security vulnerabilities, and lack of transparency, which might jeopardize solid financial planning if not addressed (Noviandy et al., 2023). This study consolidates research on risks associated with credit card-centered wealth management and possible ways to reduce these risks.
Studies indicate three main risk factors connected to credit card-based wealth management: overspending and debt, security and technology, and lack of transparency hazards (Uña et al., 2023). Credit card convenience might lead customers to overestimate their future debt repayment capabilities, exceed budget limits, and accumulate substantial interest charges that hinder wealth accumulation objectives (Ashta & Herrmann, 2021; Patel, 2023). Approximately 80% of Americans do not pay their monthly credit card payments in full, suggesting inadequate financial planning and fiscal discipline (Uña et al., 2023). Initial minimum payments may seem benign, but they may lead to compound interest that diminishes overall wealth in the long run. The typical household has a credit card debt of $6,200 with annual percentage rates (APRs) of 16% or more (Noviandy et al, 2023). Consumers may also make additional purchases they don’t need to maximize reward points, which may reinforce negative financial behaviours (Patel, 2023). Psychological studies indicate that credit card users have a decreased sense of the pain of payment, leading to lower self-awareness of their spending habits (Ashta & Herrmann, 2021, p. 215). The emotional gratification from using a card for purchases might make it challenging to show self-control.
AI fraud detection systems may help reduce illicit use, but relying too much on them might create new risks (Carrasco & Sicilia-Urbán, 2020). Flawed algorithms mirror skewed data and marginalize protected groups, thereby automating prejudice. Data privacy and hazards of system hacking increase as fintech ecosystems get more complex and include various third parties (Khalid et al., 2022). Consumers have little visibility into how widespread AI influences results. Simultaneously, individuals tend to show excessive deference towards algorithmic conclusions they perceive as unbiased, without confirming their correctness or fairness across various client groups (Carrasco & Sicilia-Urbán, 2020). Unclear loyalty program practices and ambiguous details in credit card agreements increase risks for users of credit card services (Morgan, 2022; Noviandy et al., 2023). Consumers find it challenging to evaluate credit cards and rewards programs due to the extensive paperwork and purposefully vague wording used to describe interest rates, expiry regulations, and blackout dates (Patel, 2023). Youthful individuals increasingly depend on mobile applications and less human guidance, making them vulnerable to exploitation by too intricate systems that they hardly understand (Ashta & Herrmann, 2021).
Scholarship is starting to address industrial innovation, while studies have identified hazards in each area (Uña et al., 2023). Most study focuses on credit card use in general rather than on particular emerging wealth management services. Research mostly focuses on US and European markets rather than exploring the consequences in emerging countries, as noted by Khalid et al. (2022) and Morgan (2022). With the increasing presence of card-based investing and savings platforms worldwide, it is crucial to evaluate how these technologies impact global socioeconomic inequities, as highlighted by Broby (2021). How can financial security be improved in the face of increased uncertainty and continuous developments in credit card and fintech? Defensive precautions and proactive policy improvements both have the potential to improve consumer welfare. Individually, customers may protect themselves by closely reviewing their accounts, setting spending limits based on budgets, and diversifying investments instead of only depending on loyalty benefits (Ashta & Herrmann, 2021; Morgan, 2022). Opting for cash over cards minimizes the temptation to overspend. Choosing impartial, fee-based financial advising services may counteract persuasive marketing tactics and assist in making difficult decisions.
When choosing credit card wealth products, choose providers who prioritize robust data security, ethical AI methods, and product simplicity to enhance protection (Broby, 2021; Carrasco & Sicilia-Urbán, 2020). Enhanced financial rules, improved technical standards, and financial literacy initiatives are interconnected policy measures to address various issues (Khalid et al., 2022; Uña et al., 2023). Enforcing technology governance standards that prioritize the capacity to provide explanations for automated judgments and solutions for failures enhances responsibility. Enhanced platform compatibility facilitates transparent product comparisons. Enforcing ethical principles for AI involves doing bias testing and validation across different client categories to ensure fairness (Carrasco & Sicilia-Urbán, 2020). Clear and simple risk labelling similar to food or medicine warnings helps clients be more cautious (Uña et al., 2023). Supporting financial education to promote responsible credit habits will help against the financial industry’s manipulation of consumer behaviour (Ashta & Herrmann, 2021). Enhanced legislation that increases responsibility for data breaches encourages companies to spend more on security measures (Broby, 2021). Although there is no one solution, implementing a series of legislative changes that target specific risk variables may enhance consumer protections in conjunction with continuous financial advancements.
Although early studies outline dangers broadly, there are still substantial study gaps concerning the exact processes via which credit card features and wealth management product structures spread particular damages. Further investigation is required to understand how the interaction of payment technology features, loyalty program regulations, and customer behaviour leads to excessive spending and debt, hindering saving efforts. Patel (2023) highlights that transaction mining research has uncovered dangerous purchase sequences and consumer archetypes by detecting patterns in thousands of card statements. However, Patel says that the whole extent of this study has not been fully investigated yet (p. 76). Noviandy et al. (2023) suggest conducting controlled studies that manipulate incentive programs, product complexity, and user financial literacy to reveal causal correlations with spending effects. Conducting randomized field research with customers in collaboration with banks might provide more valuable insights than just analyzing observational data.
Another neglected area of study is the intersection between credit card wealth management features and overall financial portfolios and asset allocation techniques. Does offering rewards for everyday spending and investment hinder customers from maintaining more balanced and diverse financial plans? A first benchmark may be established by calculating the share of family income obtained via card rewards compared to steady revenue sources (Ashta & Herrmann, 2021). Comprehensive surveys that include household balance sheets, such as card obligations, revolving debt levels, investment holdings, and relative returns, have the potential to uncover substitution impacts and systemic problems.
It is essential to do further comparative studies on fintech innovation patterns and their effects on customers in different geographic areas (Morgan, 2022). Regulatory environments and cultural distinctions among areas such as North America, Asia-Pacific, Middle East, Africa, and Latin America influence the sorts of goods available and customer acceptance. Risk assessment frameworks such as Khalid et al.’s (2020) machine learning models demonstrate potential in individual countries like Pakistan, but transferring these models to other markets presents difficulties and exposes prejudices. Systematically mapping hazards internationally offers a comprehensive global perspective, while also preventing narrow-minded assumptions. To close knowledge gaps, one must enhance methodological accuracy and broaden issue perspectives.
In a nutshell, Credit card-linked wealth management solutions provide convenience and rewards incentives, but they also come with dangers such as overspending, security concerns, and lack of transparency that need to be addressed (Noviandy et al., 2023; Patel, 2023). Combining personal vigilance with increased regulatory supervision and industry changes enhances safeguards in a rapidly advancing innovation sector that surpasses risk analysis (Ashta & Herrmann, 2021; Morgan, 2022). There are more uncertainties than certainties when it comes to the strategic development of technologies that combine credit, investment, data, and AI fields. Outlining risks and mitigation techniques is crucial for harnessing fintech’s social benefits and preventing avoidable damage. Continual multidisciplinary evaluation combining ethical examination and scientific evidence is essential as financial systems become more linked and complicated.
Conclusion
Credit card-based wealth management services are new fintech innovations that are quickly developing. However, they come with dangers such as overspending, security vulnerabilities, and lack of transparency. These risks need to be addressed to protect consumers’ financial well-being. Research has identified risk categories, but there are still substantial gaps in understanding how certain product characteristics and structures affect hazardous debt and consumption behaviours on both individual and societal levels. Further detailed and experimental investigation is required to understand the causative pathways, especially where financial technology, program rule designs, and cognitive restrictions converge in human psychology. Comparative studies in several geographic places may show how differences in regulations and cultures affect the risks faced by global consumer groups in different ways. It is crucial to use findings from policy analysis, user surveys, transaction mining, data trials, and cross-country research to advance responsible innovation in the merging of credit cards and wealth management on new platforms. Advanced analytics should prioritize democratization and equality instead of exacerbating the concentration of money via obscure means. By implementing careful regulation and ongoing research, we can use the conveniences and efficiencies of fintech disruption to successfully increase financial access and literacy on a broader scale. Unaddressed potential dangers also pose a threat to further division. A continuous comprehensive evaluation that combines ethical concerns with empirical evidence is crucial for promoting healthy sociotechnical advancement.
References
Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211-222. https://doi.org/10.1002/jsc.2404
Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211-222.. https://doi.org/10.1186/s40854-021-00264-y
Carrasco, Rafael San Miguel, and Miguel-Ángel Sicilia-Urbán. “Evaluation of deep neural networks for reduction of credit card fraud alerts.” IEEE Access 8 (2020): 186421-186432. https://ieeexplore.ieee.org/abstract/document/9204699
Khalid, S., Khan, M. A., Mazliham, M. S., Alam, M. M., Aman, N., Taj, M. T., … & Jehangir, M. (2022). Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms. Complexity, 2022.https://doi.org/10.1155/2022/6858916
Morgan, Peter J. “Assessing the risks associated with green digital finance and policies for coping with them.” In Green Digital Finance and Sustainable Development Goals, pp. 51-68. Singapore: Springer Nature Singapore, 2022. https://doi.org/10.1155/2022/6858916
Noviandy, T. R., Idroes, G. M., Maulana, A., Hardi, I., Ringga, E. S., & Idroes, R. (2023). Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques. Indatu Journal of Management and Accounting, 1(1), 29-35.https://doi.org/10.60084/ijma.v1i1.78
Patel, K. (2023). Credit Card Analytics: A Review of Fraud Detection and Risk Assessment Techniques. International Journal of Computer Trends and Technology, 71(10), 69-79. https://doi.org/10.14445/22312803/IJCTT-V71I10P109
Uña, G., Verma, A., Bazarbash, M., & Griffin, M. N. N. (2023). Fintech payments in public financial management: benefits and risks. International Monetary Fund.https://www.imf.org/en/Publications/WP/Issues/2023/02/03/Fintech-Payments-in-Public-Financial-Management-Benefits-and-Risks-529100
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