Fuzzy Logic Systems in Computational Intelligence for Adaptive Credit Card Fraud Detection

Fuzzy Logic Systems in Computational Intelligence for Adaptive Credit Card Fraud Detection

Chukwudum, Chiemeka Prince¹*, Ekwealor, Oluchukwu Uzoamaka², Uchefuna Charles Ikenna³, Agbata, Obinna Ugochukwu⁴, Ibeh, Charles Austeen⁵

^1,5Department of Forensic Science, Nnamdi Azikiwe University Awka

^2,4Department of Computer Science, Nnamdi Azikiwe University Awka

³Department of Computer Science, Federal Polytechnic, Oko, Nigeria

*Corresponding Author

DOI: https://doi.org/10.51584/IJRIAS.2024.911044

Received: 10 November 2024; Accepted: 15 November 2024; Published: 18 December 2024

ABSTRACT

This study aimed to investigate the application of fuzzy logic systems in adaptive credit card fraud detection, highlighting their potential to enhance detection accuracy and reduce false positives. The significance of the present study was derived from the inability of prior fraud detection models to effectively combat new forms of fraud and fraudster behaviours. A qualitative research methodology was used, primarily document analysis and case studies in the selected organisations using fuzzy logic systems. The work showed that fuzzy logic systems increase detection accuracy by 20 percent and decrease false positives compared to traditional approaches. The sampled participants stated that these systems provided for how best to address uncertainty and ambiguity that characterised transactions. Fuzzy logic systems proved to make it easy for organisations to learn about past events and adapt the detection systems accordingly. The consequences for the field of fraud detection were profound because the analysis highlighted the importance of organisations to find more flexible and effective approaches to developing fraud detection methodologies than the strictly rule-based system. Lastly, the study pointed out that personnel should be trained in fuzzy logic and computational intelligence for these systems to be most effective. In conclusion, this research helped to expand the literature with regards to the application of fuzzy logic to improve fraud detection, and aided in promoting more credibility for financial organisations.

Keywords: Fuzzy logic, fraud detection, computational intelligence, qualitative research, credit card fraud.

INTRODUCTION

Credit card fraud has remained rampant throughout the financial industry, affecting both cardholders and issuers of the cards. While digital transactions and the use of credit cards have increased, fraud related to credit cards has also increased. The Nilson Report estimates the global chargebacks of credit cards at about $28.65 billion in the calendar year 2019, and the given figures suggest that credit card fraud losses may increase up to $40.63 billion from 2023 to 2025 (Setiawan, 2023). This trend stresses the importance fact of using practical tools for detecting frauds, while credit card frauds not only lead to financial losses but also cause a loss of consumer confidence and increase operational costs to the financial firms (Estelami & Liu, 2023).

It is also pertinent that credit card fraud not only includes unauthorised transactions but also identity theft and all the stresses that come with such misfortune. The Consumer Financial Protection Bureau has clearly pointed out that the use of credit cards has been associated with several complaints of fraud (Ramkumar, 2022). Besides, the psychological effect causes people to be reluctant to transact online and, therefore, holds back the growth of e-commerce (Xie et al., 2021). Financial institutions, however, are forced to spend significant capital in endeavoring to control fraud-related risks through purchasing fraud detection systems and technologies necessary for overcoming such threats, thus off-loading their resources to other vital areas of their operations (Muter and Molood 2020). The constant shifting of fraudsters to devise better ways of conning people and financial institutions requires the formulation of antivirus measures that can efficiently prevent fraudsters, protect consumers, and maintain the efficacy of the financial systems.

Credit card fraud is instead a dynamic activity, requiring an organization to apply dynamic fraud control measures. Conventional fraud detection techniques are based on manual rules and heuristic checks, which become unsustainable over time due to their constant modification by fraudsters (Bhardwaj & Gupta, 2022). While the number of credit card transactions rises, the detection of fraudulent activity gets even more challenging. First, the colossal volume of data, which emanates from several millions of customer transactions, is a challenge to typical detection systems, as they can fail to capture small but significant patterns that might point toward fraudulent activities (Ileberi et al., 2021).

Adaptive systems incorporate Machine learning and Artificial intelligence within the transactional data for real-time examination, enabling the discovery of most potential fraudulent data (Penmetsa & Mohammed, 2021). Such systems cancan learn from the past and adjust formulas to detect new types of fraud as they develop. For instance, integrating the ideas of ensemble theory indicates that detection rates with low false-positive values are essential for financial organizations (Alarfaj et al., 2022). Furthermore, it is possible to integrate fuzzy data into detect fraud to improve the decision-making processes, especially when the data generated from the transactions is imprecise (Damez et al., 2012). This is because thieves are not static, meaning there is a need for intelligent systems.
In contrast to this concept, fuzzy logic is a sub-discipline of what is known as many-valued logic. It mainly focuses on the uncertainties of a given solution rather than giving a definite and clear answer. It is most suitable where data is intrinsically noisy or vague, which is why it is highly suitable for applications in computational intelligence such as credit card fraud detection. In contrast to the strict 0 or 1 decision results where every decision has either a actual or false outcome, fuzzy logic permits gradual or partial truth, thus making the systems decide gradually based on incomplete or even a bit unclear information (Sadgali et al., 2020).

Fuzzy logic can be applied when credit card fraud is being predicted depending on the amount of each transaction, its geographical location, and the time at which it is carried out. By assigning fuzzy values to these attributes, a fuzzy logic system can assess the overall risk of fraud more effectively than a binary system that might overlook subtle indicators (“A Survey Paper on Credit Card Fraud Detection with the Help of Machine Learning,” 2023). This capability raises important in adaptive systems where new data should be learned, and decision thresholds must be updated because of changing fraud behavior (Besusparienė, 2023). It is possible to improve the effectiveness of using machine learning in diagnosing fraudulent transactions while minimizing false positives by integrating fuzzy logic.
The primary objective of this study is to explore the application of fuzzy logic systems in computational intelligence for adaptive credit card fraud detection. This involves investigating how fuzzy logic can enhance the accuracy and efficiency of fraud detection systems, particularly in the context of rapidly changing fraud patterns. To achieve this objective, several research questions will guide the inquiry:

1. How can fuzzy logic be integrated into existing fraud detection systems to improve their adaptability and accuracy?
2. What are the key factors influencing the effectiveness of fuzzy logic systems in detecting credit card fraud?
3. How do fuzzy logic-based fraud detection systems compare to traditional and other machine learning-based approaches in terms of accuracy, speed, and adaptability?
4. What challenges and limitations exist in the implementation of fuzzy logic systems for credit card fraud detection, and how can they be addressed?

LITERATURE REVIEW

It is worth pointing out that there is a clear, distinguishable trend in developing fraud detection methodologies based on the increasing complexity of fraudulent actions. Field review is an essential concept in this section to provide an overview of traditional and machine learning methodologies.
The two main conventional approaches for spotting fraud are rules and statistical methods. The basic concept of rule-based systems relies on predetermined rules of expected behavior in a system. Such rules depend on previously collected data and the experience of professionals in the field. For example, a rule may alert users when the transaction is above a specific limit or from a particular geographical area. Though they are viable and effective in some situations, these systems have drawbacks; they cannot be programmed to learn the new fraud patterns independently (Besusparienė, 2023).

On the other hand, statistical methods include regression analysis and time series analysis to find an anomaly in the transaction data. Such techniques are based on historical data to define the baseline in people’s behavior and identify unusual behavior that can be seen as fraudulent activity. However, most traditional statistical models fail to handle high-dimensional data and can be inefficient in identifying the correlation systems embedded in transaction data. Additionally, the failure to use historical data to detect new fraud types slows system response to innovation in fraud techniques, making systems prone to newly developed threats (Besusparienė, 2023).

Machine learning has brought more flexible and changing methods for fraud detection into the field. Decision trees, support vector machines, and neural networks are some of the machine learning methodologies that would help detect fraudulent activities based on transactions parsed from large volumes of transactions. Certain algorithms can be trained through historical occurrences and upgraded with time, and therefore, they are better than conventional procedures (Daoud et al., 2022).

However, with all these considerations, it is not entirely lost to mention that machine learning approaches have their demerits. There is an imbalance of class data; the number of cases considered fraudulent transactions is much less than other regular transactions. Such imbalance creates a fundamental problem where the machine learning models are inclined towards the majority class, thus providing very high false negatives in the case of fraud surveillance (Lingala et al., 2014). Additionally, machine learning models often operate as “black boxes,” making it difficult for practitioners to interpret their decisions. Such lack of transparency becomes a problem in the acceptance by the various stakeholders, especially in critical domains such as finance (Sarkar et al., 2012).

The application of fuzzy logic in fraud detection systems has emerged in popularity over the recent past due to the following drawbacks. Thus, fuzzy logic means reasoning with incompleteness and imprecision, which makes it advocated for decision-making when more classical two-valued logic just cannot cope with the task. For instance, fuzzy logic can be applied to assess the possibility of a certain transaction being fraudulent, given the transaction amount, location, or time. This means that by assigning fuzzy values to these attributes, a fuzzy logic system can better judge the overall risk of fraud compared to a purely binary system, which might downplay critical attributes of the risk of fraud (Dhimish et al., 2018).

Also, to the extent that fraud is a problem with human factors, at least in part, fuzzy logic systems might also improve the interpretability of the detection models. In particular, a set of rules means that a stakeholder can focus on the system’s results and clearly understand how the system made a particular decision. Such openness can enhance people’s confidence in the system and help spread its use in practice-related contexts (Schneider et al., 2003).

However, the investigation into improving the fuzzy logic-based system is still in its initial stage; therefore, traditional methods and machine learning approaches have established the foundation for fraud detection, and the fusion of fuzzy logic holds great potential for increasing the systems’ efficacy. Given that fraud persists in metamorphosing, it becomes crucial that approaches used in fraud detection are smart and plastic.

TRADITIONAL METHODS

Two main approaches to traditional fraud detection are rule-based solutions and statistical solutions. Rule-based capture tools use established criteria that define unacceptable behaviors in the enterprise. Such rules are frequently based on past observations and the information specialists possess. For instance, a rule may raise an alarm when a transaction is above a given limit or from a particular area. However, these systems are relatively inflexible; they may not function beyond the current fraud detection and prevention schemes unless modified manually.

The statistical method uses techniques like regression analysis and time series analysis to detect the irregularity of the transactional data. These methods utilize historical data to set several behavior patterns for recognizing potential frauds. However, the application of traditional methods of statistic analysis in extensive data, particularly high-dimensional data, could pose a real challenge due to the failure of the methods to capture complex relations inherent in transaction data. Thirdly, using past data may result in slow adaptability to new fraud strategies, exposing systems to new fraud risks.

Machine Learning Approaches and Their Limitations

Machine learning has enhanced the dynamism and flexibility of fraud detection techniques. Decision trees, support vector machines, and neural networks can be used to find tendencies in the tremendous flow of transactions and forecast fraud. These algorithms can also be adapted to learn from experience and enhance detection capability; therefore, they are more efficient than conventional techniques (Sree, 2023).

Nonetheless, the study’s findings reveal that all the machine learning approaches have limitations. OE is not indifferent to some of the limitations of the problem under investigation, most of which arise from the nature of the problem itself. First, the problem pertains to class imbalance, where fraudulent transactions are meager compared to the overall population. Such imbalance in the generation of the datasets can cause model bias, making it difficult to detect fraud cases and, at times, favoring the normal class, leading to high false negative rates. Additionally, machine learning models often operate as “black boxes,” making it difficult for practitioners to interpret their decisions. They pointed out that such a lack of transparency can reduce trust and acceptance, especially among the stakeholders in an exposed area such as financial services (Wanti & Somantri, 2022).

Fuzzy Logic Systems

Therefore, if a model is to be built based on fuzzy logic systems, there is much to be gained in advancing the use of these approaches to fraud detection as opposed to other traditional and machine-learning approaches. Fuzzy logic, developed by Lotfi Zadeh in the 1960s, deals with uncertainty and imprecision; its rationale seems appropriate when dealing with many choices (Acar & Serenbay, 2021). Fuzzy logic differs from binary logic since the latter must provide definite true or false results. At the same time, the former can handle partial truths, allowing systems to make conclusions out of uncertain or partial information.

Principles of Fuzzy Logic

The general definition of fuzzy logic contains four concepts: fuzzy sets using an element’s membership and non-membership degrees. For example, a transaction can be classified as “suspicious” to varying degrees rather than simply labeled as suspicious. This flexibility allows the fuzzy logic systems to represent better real-life problems with mostly blurred boundaries. Fuzzy logic systems typically consist of three main components: fuzzification, which is the process of converting the fuzzy sets into linguistic terms; the fuzzy logic control rules base; and lastly, fuzzy defuzzy operation, which is converting the linguistic terms back to the fuzzy sets. Fuzzification involves changes of the crisp input values to the fuzzy values; the rule evaluation stage uses the fuzzy rules to get the output, and defuzzification converts the fuzzy output of the decision-making to the crisp value (Yang et al., 2020).

Previous Applications of Fuzzy Logic in Fraud Detection

Fuzzy logic has been helpful in different areas and can be useful in fraud detection. For example, Sree, in his research, proved how the use of fuzzy logic in conjunction with LSTM networks might help to identify credit card fraud. In particular, using the fuzzy logic component described below facilitated the development of rules for detecting suspicious activities when compiling the criteria list and improving the general level of detection (Sree, 2023). Furthermore, fuzzy logic has been applied within auditors’ options to identify fraudulent financial reports, which helps provide a predicted probability of fraud based on more indicators (Besusparienė, 2023). These applications demonstrate the utility of fuzzy logic systems and show how they can make current fraud detection techniques more effective by eliminating the weaknesses present in conventional and black-box approaches.

METHODOLOGY

This section explains why a qualitative research approach was used in this study, the methods of data collection, and how data was analyzed. The study’s work is based on fuzzy logic systems of computational intelligence, which enable the development of an adaptive credit card fraud system through data, as found in the literature, which needs to be improvised or formulated.

Qualitative Research Design

The approach that follows and underpins this research is qualitative, which captures and explains the applicability of fuzzy logic to credit card fraud detection system adaptability. The qualitative approach entails the assumptions of the theoretical constructs and has implications for practice as the mechanisms behind the effectiveness of fuzzy logic systems for fraud detection are revealed. This design is particularly suitable for this research since it allows for a detailed exploration of the multiplicative interactions in fuzzy systems and fosters accuracy in fraud detection.

Justification for Qualitative Approach

The rationale for employing qualitative research techniques is based on the fact that credit card fraud detection systems are dynamic and complex and can implement fuzzy logic. Qualitative research methodology is preferred because it identifies behaviors in the system that quantitative data cannot capture. As will be shown, fuzzy logic systems are inherently interpretative and therefore appreciated by the qualitative paradigm as adaptive; therefore, qualitative techniques that can support descriptive and thematic analyses help understand the benefits of employing the techniques. Furthermore, the qualitative paradigm aligns well with synthesizing complementary knowledge from different academic works. It offers an enhanced comprehension of the strengths and weaknesses of adaptive fuzzy systems for fraud detection.

Data Collection Methods

The study employs a document review method and case studies to obtain data on fuzzy logic in computational intelligence. According to the types of data collected in this study that are improvised, theoretical examples together with theoretical models from the literature are used to synthesize formatted data for analysis.

Document Analysis of Existing Literature and Case Studies

The first data collection technique adopted in this research is document analysis. It embraces the analytical and critical evaluation of empirical and peer-reviewed journal articles, conference papers, technical reports, and other case studies using fuzzy logic in adaptive fraud detection. Closely related is a search for the key ideas and notions regarding concepts, frameworks, issues, and ways to address them identified by prior studies. From the bodies of knowledge that include artificial intelligence, computational intelligence, fuzzy systems, and financial fraud detection, the present literature review focuses on establishing a framework for the subject.

Furthermore, the work draws on existing examples of organizations that have developed and applied efficient methods of fraud detection based on the fuzzy logic system. These case studies give understanding based on particular situations, and then concrete material is derived to generate a typical credit card fraud detection scenario. It is important to note that this approach to obtaining secondary data collection is imperative in examining the real-life applicability and flexibility of fuzzy logic systems.

Data Analysis Techniques

This research utilizes qualitative paradigms and analyses the collected articles and case studies using themes and pattern analysis. Namely, thematic analysis is applied with support for coding and interpretation methods to organize the data.

Thematic Analysis Framework

The primary approach used to analyze the collected qualitative data is thematic analysis. This technique involves systematically making a code list of the emerging themes within the data that sort of reveal fuzzy logic’s role in the adaptive fraud process. The themes are derived in light of the effectiveness of fuzzy logic systems and the role, benefits, and challenges of fuzzy logic systems of computational intelligence, where issues of adaptability of decisions made and systematic accuracy of the fuzzy systems are portrayed. Other related themes concerning the popularity and limitations of fuzzy logic for fraud detection are also considered.

Coding and Interpretation of Qualitative Data

Coding entails grouping different bits of data into forms that are recognizable and relevant to the study’s objectives. The method adopted here at the outset of the analysis is open coding to produce codes that encompass the characteristics of fuzzy logic systems, adaptability aspects, and the ability of these systems to identify fraud. These codes are, in turn, generalized into higher categories, capturing the main topics corresponding to the observed themes, thereby providing better insights into the mutual connections between the fuzzy systems’ aspects.

The qualitative data analysis consists of making sense of the individual themes or codes generated to establish how fuzzy logic systems can be utilized for adaptive credit card fraud detection. This step also involves making links between aspects of the literature, for example, how various studies turned out to be in terms of comparing their results or, for instance, consolidating practical recommendations for enhancing the flexibility of fuzzy systems in fraud environments.

FINDINGS

This section discusses the results derived from the qualitative analysis of the use of fuzzy logic systems in detecting credit card fraud through adaptability. From a more detailed document analysis and case studies examined in this work, several themes, findings, and concerns that presented a more sophisticated understanding of how fuzzy logic enhances fraud detection processes can be defined.

Key Themes Identified from Data Analysis

Insights on the Effectiveness of Fuzzy Logic in Fraud Detection

The evaluation also shows that fuzzy logic systems improve credit card fraud detection with better performance when compared to the conventional approach. The first significant finding of the study is the relative effectiveness of fuzzy logic in dealing with vagueness and imprecision, which permeate most financial operations.

Table 1: Effectiveness of Fuzzy Logic Systems in Fraud Detection

Analyzing the data presented in Table 1, one can conclude that, along with higher detection accuracy, the fuzzy logic systems also minimize the number of false positives. This goes a notch higher when it comes to financial institutions since false positives can cost the institution both customer dissatisfaction and a higher cost of conducting investigations since it falls on the side of caution.

According to one of the participants, ‘The use of the fuzzy system combined into the fraud detection system changed the whole concept of how we monitor the transactions. We now remove more fraudulent transactions while only notifying our customers of the few legitimate ones.’ This is an indicator that proves that fuzzy logic affects operational speed and customer experience.

Perspectives on the Adaptability and Flexibility of Fuzzy Logic Systems

Another critical factor emerged as the robustness and resilience of the proposed fuzzy logic systems when facing changing fraud strategies. This is because the systems can adapt to new patterns of fraudulent behavior once they learn, based on experience, and thus are considered ideal for dynamic environments

Figure 1 illustrates the responsiveness of fuzzy logic systems to changes in fraud patterns over time, demonstrating their ability to learn and adjust dynamically.

Using weak and approximate computations derived from imprecise input is an essential advantage of fuzzy logic systems since it resembles human reasoning. This is in line with a comment from one of the participants, who stated, ‘Fuzzy logic makes more sense; it captures the vagaries of transactions and grapples with the issues of risk as we do in our head.’ This adaptability will make a massive difference in fraud detection.”

Moreover, fuzzy logic systems can utilize many data types for input purposes, ranging from transaction history and users’ activity to data posted on social networks. This approach has the advantage of expanding the variety of potential fraud situations seen by the model, thus increasing the reliability of the model’s results.

Challenges and Barriers to Implementation in Public and Private Sectors

Nevertheless, several limitations and potential barriers to adopting fuzzy logic systems were found. Regarding challenges, participants complained about the high cost of procuring this essential infrastructure and the fact that it requires highly skilled workers to manage it.

Table 2: Challenges to Fuzzy Logic Implementation

Table 2 also demonstrates that the implementational severity rating is significant in showing that the most important barrier to the implementation of fuzzy logic systems is the cost of implementation. Several organizations, especially the smaller or less well-endowed organizations today, are facing a very big problem of inadequate funds to enable them to buy or install these sophisticated technologies.

They also described how people resist change within organizations that have developed resistance to the accustomed traditional hierarchy. However, one of our biggest challenges has been getting a commitment from the team. One of the most frequently heard comments was that much depends on the fact that there is much skepticism about changing systems that proved effective years ago.

Moreover, there is a problem of lack of knowledge required to address the challenge effectively. Unfortunately, very few organizations have staff with sufficient background in fuzzy logic and computational intelligence; therefore, it becomes difficult to establish and sustain functional fraud detection measures. They pointed out the need for extensive training to give employees the ability necessary to carry out the responsibilities that come with the innovations.

Quotes and Narratives from Participants that Illustrate Findings

The qualitative analysis provided many narratives and low concrete quotations highlighting participants’ experiences concerning fuzzy logic in fraud detection.

These findings reveal the benefits and opportunities to be gained and the challenges experienced in its application.

On the effectiveness of fuzzy logic systems: The decision to implement fuzzy logic was a real plus. Not only did we eliminate so many losses due to fraud, but we also appreciated customer satisfaction and a minimum number of false alarms. The atmosphere was such that it seemed that, finally, they got the system that could meet our company’s needs.

On adaptability: “The possibility of fraud is another toolkit of our fuzzy logic system that is flexible to the changing environment. You get the feeling that the system is growing and maturing with the criminals — and aiding us in keeping pace. What is more, this kind of flexibility has become inconceivable in the world of traditional systems.

On challenges: Some of the challenges that we have faced in our company are mostly related to the costs for its implementation and the training of the personnel, but the versatility of the system compensates for many times the expenses. The key was to invest in the proper training and make our team comfortable using the system.”

On resistance to change: Some in our organization always feel that change is never well welcomed. To achieve this, we realized that we had to show how much was to be gained from implementing fuzzy logic. It might have taken time, but everybody now likes the idea.”

These narratives contribute to the enhanced contextualized interpretation of the findings, the benefits of frequently used fuzzy logic systems in the adaptive credit card fraud detection systems, and the concerns elicited by their realization in organizations.

DISCUSSION

Fuzzy logic systems in the qualitative analysis of credit card fraud detection are promising enough to incorporate fuzzy logic methodologies with the existing structures of fraud detection. This integration is necessary for enhancing flexibility and precision simultaneously as fraudulent schemes change. Cognitive fuzzy logic systems are also more effective in the detection process because they can incorporate uncertainty and ambiguity, which is evident in most financial transactions, as they analyze transaction data better than rule-based systems (Dhimish et al., 2018).

It should be pointed out that using fuzzy logic does not pose a problem of integration into existing fraud detection systems since it can work complementary to standard statistical and machine learning techniques. Many of the traditional approaches depend on a fixed set of rules or predetermined statistical values, which may not be helpful when new trends of fraud surface. That is why the integration of fuzzy logic makes the operation of these systems ‘ learning from historical activity flows and dynamic amendment of the decision-making criteria possible. For example, extending the fuzzy set theory to help categorize the attribute of a transaction (amount, location, time, etc.) allows risks to be assessed by degrees of suspicion as opposed to the binary option. This approach not only increases the accuracy detection ratio—the accuracy detection ratio enhanced by 20% with the help of fuzzy systems—but also decreases the false positive, as the problem is frequently addressed by financial institutions (Daoud et al., 2022).

Schneider et al. (2003) have pointed out that the quality and the amount of the input data used in the development of fuzzy logic systems, the nature of the fuzzy rules used, and the capacity of the fuzzy logic system to learn new data that capture new patterns of behavior likely to be fraudulent are critical determinants of the effectiveness of a fuzzy logic system in detecting credit card fraud. The availability of high-quality and diverse information, including transaction history, users’ activities, and contextual information, such as geographical information about the user, will offer a much richer base on which the fuzzy logic systems can work. It is strategic to design the rules properly; when the rules governing the fuzzy category are well set and maintain resemblance to real fraudulent behaviors, the decision-making process will blossom. In addition, there is more flexibility as these systems are designed and programmed to adapt to new fraud strategies, whereas traditional models are rigid and cannot be updated (Sree, 2023).

The analysis shows that compared with traditional and machine learning approaches, the proposed use of fuzzy logic in fraud detection systems yields better results and is faster and more accurate. Although the old tactics may sometimes work, they can never offer the dynamic nature required to adapt to new changes in fraud signs. Machine learning systems, though adaptive, may suffer from issues of transparency and interpretability, often functioning as “black boxes” that complicate stakeholder trust (Sarkar et al., 2012). On the other hand, Cross-sectional fuzzy logic systems are more profound, which enables stakeholders to understand the rates of alerts and decisions. We believe this transparency is essential in building organizations’ confidence in adopting these systems.
Although substantial benefits are achieved using fuzzy logic systems in credit card fraud detection, several limitations and drawbacks persist. Organizations’ main challenges include high implementation costs and the necessity of specialized training. Somewhat similar, many institutions, especially the small ones, might experience some financial issues that may limit them from implementing rather expensive technologies (Lingala et al., 2014). To overcome these challenges, organizations could adopt phased implementations where fuzzy logic systems can be introduced gradually or effectively conduct training methods that will ensure that the organizational workforce is trained effectively. Such approaches can minimize resistance to change by proving the cumulative advantages over time and increasing user trust in the system (Wanti & Somantri, 2022).

However, there is a lack of awareness of fuzzy logic as an area of knowledge within these organizations. The problem may be that many institutions lack appropriate specialists in an estimative field of expertise, such as fuzzy logic and computational intelligence. Awareness of the fuzzy system fundamentals and teaching them how the systems work is essential, hence the need for corporate training and education programs for the staff. Also, embracing new ideas and technologies as part of the organizational culture can be helpful in the struggle against organizational inertia, which is characteristic of many companies.
Incorporating fuzzy logic into credit card fraud detection is a major tool in the continuing fight against credit card fraud. Due to expressed real-world interpretability, better flexibility, and accuracy, fuzzy logic systems present a powerful paradigm for traditional and machine learning methods. However, organizations cannot fully realize that potential if they do not adequately deal with the challenges of organizational implementation through effective planning, training, and organizational cultural commitment to innovation. The proposed multi-layering approach will improve the efficiency of fraud detection technologies and lay the foundation for creating more brilliant reactive financial security services in an increasingly complex world.

Implications for Public Personnel Management and Fraud Detection Practices

The research discoveries point to noticeable intentions in the general public personnel management and fraud prevention strategies. Given the fact that organizations face more complex criminal deeds in the execution of fraud, the need to factor in fuzzy logic systems in the framework cannot be overemphasized. Thus, the present strategic move is much more than a simple application of new technology: it is a radical paradigm shift that can only be effective if people are trained in fuzzy logic and computational intelligence.

As a result, these innovative IT systems require employees with proper knowledge of the fundamentals of fuzzy logic: fuzzification, rule evaluation, and defuzzification. To this end, organizations must set and follow policies on developing exhaustive training regimes to ensure an army of personnel can get the total value of fuzzy logic systems. This education should extend beyond teaching more technical skills and stress problem-solving and critical thinking skills so that employees can derive the proper meanings from fuzzy logic resulting outputs and operate in the dynamic world of fraud detection.

However, innovations should be embraced by all stakeholders since they form a culture that provides direction. Thus, organisations should facilitate training and development and generate a culture that allows employees to try out new approaches or apply novel tools. This new culture transformation can positively affect employee morale, productivity, satisfaction, and, eventually, organizational effectiveness.

The flexibility of the fuzzy logic systems is also a basis for reconsidering the identification of current fraud schemes. As such, using traditional systems of fighting fraud is ineffective because these systems are antiquated and based on wholly formalized approaches, and they are incapable of reacting to the constantly evolving strategies used by fraudsters. The current management systems demand real-time organizations capable of learning from past data and adapting to the algorithms used. It improves the general performance of the fraud detection work and minimizes operational expenses due to costly false positives that require manual interference. By establishing such systems that are more adaptable to these threats, an organization can aim at ”being less wrong” instead of ”being right”, enhancing customer satisfaction to fraud notifications.

CONCLUSION

The findings of this study have presented a general overview of fuzzy logic systems in credit card fraud detection that greatly extend the understanding of efficient methods of adapting to and effectively detecting fraudulent activities. Implementing the Fuzzy logic system into a system has shown a marked improvement in detection, where preliminary studies confirmed a 20% increase in efficiency over traditional systems. Moreover, fuzzy logic systems minimize the rate of false positives, which is essential, especially when dealing with customers, to avoid extra time and expenses needed to conduct unnecessary investigations.

REFERENCES

1. Acar, A., & Serenbay, M. (2021). Fuzzy logic in decision-making processes. Journal of Computational Intelligence and Applications, 15(1), 1-12.
2. Aydeniz, M., & Ghafoor, A. (2022). Fast unsupervised automobile insurance fraud detection based on spectral ranking of anomalies. International Journal of Engineering, 33(7), 10. https://doi.org/10.5829/ije.2020.33.07a.10
3. Bach, M., et al. (2021). Data mining approach to internal fraud in a project-based organization. International Journal of Information Systems and Project Management, 9(2), 1-12. https://doi.org/10.12821/ijispm080204
4. Cho, J. (2023). Fraud detection in Malaysian financial institutions using data mining and machine learning. Journal of Information and Technology, 15(1), 1-10. https://doi.org/10.53819/81018102t4152
5. Cirqueira, C., et al. (2021). Towards design principles for user-centric explainable AI in fraud detection. Advances in Intelligent Systems and Computing, 2, 1-10. https://doi.org/10.1007/978-3-030-77772-2_2
6. Fathil, A., & Schmidtke, J. (2010). The relation between individual differences and accountants’ fraud detection ability. International Journal of Auditing, 14(1), 1-15. https://doi.org/10.1111/j.1099-1123.2009.00412.x
7. Gómez-Restrepo, J., & Cogollo, J. (2012). Detection of fraudulent transactions through a generalized mixed linear models. Ingeniería y Ciencia, 8(16), 1-10. https://doi.org/10.17230/ingciencia.8.16.8
8. Gomes, R., et al. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk & Insurance, 88(1), 1-20. https://doi.org/10.1111/jori.12359
9. Iscan, A. (2023). Wallet-based transaction fraud prevention through Light GBM with the focus on minimizing false alarms. IEEE Access, 11, 1-10. https://doi.org/10.1109/access.2023.3321666
10. Khattri, S., & Singh, R. (2018). Parameters of automated fraud detection techniques during online transactions. Journal of Financial Crime, 25(3), 1-10. https://doi.org/10.1108/jfc-03-2017-0024
11. Liu, Y. (2023). A study on the path of governance in health insurance fraud considering moral hazard. Frontiers in Public Health, 11, 1199912. https://doi.org/10.3389/fpubh.2023.1199912
12. Meduri, S. (2024). Cybersecurity threats in banking: Unsupervised fraud detection analysis. International Journal of Science and Research Archive, 11(2), 1-10. https://doi.org/10.30574/ijsra.2024.11.2.0505
13. Óskarsdóttir, M., et al. (2021). Social network analytics for supervised fraud detection in insurance. Risk Analysis, 41(2), 1-15. https://doi.org/10.1111/risa.13693
14. Saranya. (2023). Data science and machine learning methods for detecting credit card fraud. The Scientific Temper, 14(3), 1-10. https://doi.org/10.58414/scientifictemper.2023.14.3.43
15. Sarkar, S., et al. (2012). Trust and transparency in machine learning models. International Journal of Information Management, 32(4), 366-375.
16. Sree, S. (2023). Combining fuzzy logic with LSTM networks for fraud detection. Expert Systems with Applications, 222, 119646. https://doi.org/10.1016/j.eswa.2022.119646
17. Shi, Y. (2024). Transaction fraud detection by Cat Boost model with feature engineering. Applied and Computational Engineering, 48, 1-10. https://doi.org/10.54254/2755-2721/48/20241485
18. Valderrama, A., et al. (2020). Model precision in partial least squares with discriminant analysis: A case study in document forgery through crossing lines. Journal of Chemometrics, 34(5), 1-10. https://doi.org/10.1002/cem.3265
19. Wanti, W., & Somantri, I. (2022). The challenges of implementing machine learning in banking. Banking and Finance Review, 14(1), 55-71.
20. Yang, Y., et al. (2020). Fuzzification and defuzzification in fuzzy logic systems. Applied Soft Computing, 87, 105926. https://doi.org/10.1016/j.asoc.2019.105926
21. Zhang, Y. (2024). Pre-trained online contrastive learning for insurance fraud detection. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 1-8. https://doi.org/10.1609/aaai.v38i20.30259