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
Customer Relationship Management (CRM) systems have changed significantly from their original purpose as mere contact management tools. In fact, today they are highly sophisticated data-intensive tools which, sometimes, even get integrated with Business Intelligence (BI) to assist top-level decision-making. In the light of this, the paper explores the role of modern CRM systems in enabling organizations to turn unprocessed customer data into insightful information that leads to a better business performance and superior customer experiences. The goal of this paper is twofold: to explore the capabilities of CRM systems as excellent data analytics tools and to discover how organizational leaders may use these technologies to make productive decisions. The authors of the paper adopted a qualitative and analytical approach and used literature, case studies, and reports of different industries to study the combination of CRM and BI technologies. Also, they took real-life examples from several industries to highlight commonalities and good practices. It has been found that CRM in combination with BI could lead to deeper customer understanding, forecasting capabilities, and making marketing strategies customer-specific. Such organizations typically get higher levels of customer satisfaction, better sales forecasting, and faster decision-making. On the other hand, this piece of research points to problems such as data being incorrect, difficulty in merging systems, and scarcity of expert individuals. The results of this study have practical value for enterprises in that they should consider not only sophisticated CRM equipment but also data management and human resources to be able to fully benefit from BI interfacing. To sum up, the paper stresses that switching from simply gathering data to using data for making decisions is absolutely vital in today's world and CRM systems are the leading players in making this gap productive and influenceable.
Satyendra Kumar Vanapalli· International Journal of App...· 0 citations
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.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations