2022· International Journal of Machine Learning and Predictive Analytics· 0 citations
TL;DR
This paper describes a system for the detection of fraud, which is both dynamic and adaptable and which is obtained through the synthesis of machine learning techniques and the CRM data streams and shows how this unified method can lead to an increase in detection performance, shortening of the time for the response, and higher customer confidence in comparison to the existing systems.
Abstract
Financial fraud has aggressively transformed along with the digital era, this change being largely driven by the growing reach of internet banking and mobile payments as well as cyber threats, which have been getting more advanced and which exploit not only technical weaknesses but also people's behavior. Old-fashioned detection systems that run on rules still do have their merits; however, they mostly cannot cope with the amount and intricacy of fraud patterns that we see nowadays. That is why we have seen the rise of artificial intelligence (AI), which is a very potent means of spotting irregularities, understanding how people behave, and facilitating instant decisions in fraud detection. On the other hand, Customer Relationship Management (CRM) systems have become indispensable to financial environments through the gathering of customer information, records of contacts, and insights on behavior. This paper looks at how these two fields overlap and points out ways in which the blending of AI-based analytics in CRM systems can lead to a remarkable rise in proactive fraud prevention. Using customer-focused data, such as their record of purchases, modes of communication, and levels of engagement, AI programs are capable of spotting very subtle changes that could signal an attempt at fraud and at the same time, they are able to significantly reduce the number of false alarms. The technique suggested here describes a system for the detection of fraud, which is both dynamic and adaptable and which is obtained through the synthesis of machine learning techniques and the CRM data streams. Through a case-based study, it is shown how this unified method can lead to an increase in detection performance, shortening of the time for the response, and higher customer confidence in comparison to the existing systems.
Financial fraud is moving very swiftly in today’s technologically advanced where everything is connected. This makes it extremely challenging for banks and other institutions of finance to follow the rules, preserve their customer trust, and run their businesses with integrity. Traditionally based on rules, detection systems can miss both small and big dangers when the number of transactions goes up and schemes for fraud get more intricate. Combining Machine Learning (ML) with Customer Relationship Management (CRM) data models is a powerful and versatile technique to stop fraud in this instance. Machine learning algorithms can identify hidden problems and anticipate fraud faster and more precisely by integrating information from CRM systems about past interactions with customers, behavior, and transactions in general. This work investigates a methodology that incorporates supervised and unsupervised methods of learning with enhanced CRM datasets in order to create sophisticated detection of fraud models. The methodology demonstrates data preprocessing, standardized feature engineering, and model training based on real financial parameters, including transaction frequency, alterations in typical customer behavior, and assessment of risk ratings. It also says that integrating CRM makes it less difficult for businesses to see the big picture of their customers, which enables these individuals to go from checking transactions by themselves to making decisions according to the situation. The suggested technique is to continually acquire knowledge and enhance the model so that it can keep up with the latest fraud strategies while minimizing the number of false positives that might adversely affect actual customers. This paper demonstrates the fact that banks may find fraud, minimize risks before they happen, and make their clients happy by combining ML and CRM standard data models together in an effective manner.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations
One of the major fraud risks that financial institutions and consumers around the world face is credit card Fraud. This now requires major advanced methods to identify and prevent this from reoccurring. This study compares three main approaches to fraud detection: machine learning, traditional statistical methods, and a combination of both. This research will show how each method works, the cost implications of each method, and the threats and problems that are faced when setting these methods up. We will be highlighting real examples from banks and fin tech companies in the United States, Europe, and West Africa, the research will take us through the day to day standing operating processes. It also points out how results and adoption differ across regions. The findings go to show that machine learning and hybrid approaches generally do a better job at spotting fraud and adapting to new patterns than traditional statistical techniques. However, putting these advanced systems in place is not always easy, as there are issues such as poor data quality, strict regulations, and limited resources, especially in developing regions. This often slows down implementation. The study also finds that using more advanced fraud detection systems can reduce financial losses and increase customer trust, which supports overall economic stability. At the same time, challenges remain, including constantly changing fraud tactics, difficulties integrating new systems with older technology, and a shortage of skilled professionals. Overall, this research provides practical guidance for financial institutions look ing to improve their fraud detection systems and highlights the need to tailor solutions to specific regional and operational conditions.
Sungho Kim, Rejoan Rafi, Shahadat Hossain et al.· International Journal of Gri...· 0 citations
Given the rapid commoditization of generative artificial intelligence and high-velocity digital
transactions, the current rules-based models for detecting fraud have become obsolete. Scientific
writings which already exists focuses on highly focused and uni-modal approaches such as graph neural
networks for detecting camouflage in specific contexts or hybrid models for clustering data in tabular
form. These models, though mathematically sound, lack the required multimodal capabilities and lowlatency response times required for execution within digital transaction environments. To overcome these
encounters, a new four-pillar architecture is proposed, integrating generative artificial intelligence
defense mechanisms, machine learning models, threat intelligence systems, and alert management
systems. The research investigates whether integrating various open-source machine learning models
will outperform existing academic models for detecting fraud in transaction environments. The
methodology involves a comparative analysis of the existing academic models and the proposed
architecture in terms of processing latency, explainability, and the extent of the protection offered.
Results indicate that while academic models effectively detect fraud rings in digital transactions offline,
the proposed multimodal model achieves higher throughput and protects the authentication perimeter
of the transaction system from synthetic media and prompt injection attacks. Through this research, a
critical gap in the literature between academic models and real-world implementations is bridged. This
paper provides a comprehensive structure for enterprise systems to develop multimodal, explainable
machine learning models for fraud detection in high-velocity digital transactions.
David Dimitriu· Business Administration Stud...· 0 citations
A conceptual model demonstrating how AI-enabled analytics techniques -- encompassing supervised machine learning, unsupervised anomaly detection, deep learning, and graph-based network analytics -- directly and indirectly enhance fraud detection accuracy, response speed, and organisational risk posture is developed.
Prof. Roopa U Prof. Roopa U, Shrushti S Nelogi Shrushti S Nelogi· International Scientific Jou...· 0 citations
The rapid digitalization of financial services has transformed the global financial ecosystem, enabling faster transactions, enhanced customer experiences, and greater financial inclusion. However, this digital transformation has simultaneously increased the complexity, scale, and sophistication of financial fraud. Traditional rule-based fraud detection systems often struggle to identify evolving fraud patterns, resulting in delayed responses, increased false positives, and substantial financial losses. Artificial Intelligence (AI)-powered real-time fraud monitoring systems have emerged as a transformative solution capable of detecting suspicious activities instantly through advanced data analytics, machine learning, deep learning, natural language processing, and behavioral intelligence. These systems continuously analyze vast volumes of transactional and non-transactional data, enabling financial institutions to identify anomalies, predict fraudulent behavior, and automate risk management processes with unprecedented accuracy and speed. This literature review examines the evolution, applications, technological foundations, benefits, challenges, and future directions of AI-powered real-time fraud monitoring systems in modern financial services. The review highlights how AI enhances fraud detection capabilities across banking, payment systems, insurance, digital wallets, cryptocurrencies, and investment platforms while discussing critical concerns related to privacy, algorithmic bias, explainability, cybersecurity, and regulatory compliance. The findings demonstrate that AI-driven fraud monitoring represents a fundamental component of modern financial security infrastructure and will continue to shape the future of fraud prevention in increasingly digital financial environments.
G. Onyarin· International Journal For Mu...· 0 citations
This study contributes a trust-centered, infrastructure-aware AI adoption pathway specifically designed for emerging economies, offering policymakers, fintech developers, and financial institutions a pragmatic roadmap for responsible AI-enabled fraud management in Nepal.
Y. Pant, Aditya Pudasaini, R. Shrestha et al.· Islington Journal of Multidi...· 0 citations
