AI-DRIVEN ENCRYPTION TECHNIQUES IN BIOMETRIC PAYMENT SYSTEMS FOR E-COMMERCE SECURITY

Abstract

AI-DRIVEN ENCRYPTION TECHNIQUES IN BIOMETRIC PAYMENT SYSTEMS FOR E-COMMERCE SECURITY The method for the development of AI-powered real-time analysis lowers fraud detection latency, increasing security while causing the least amount of disruption to actual transactions. In addition to selecting appropriate models, feature engineering is used in predictive AI implementation to maximize data and deployment in real-world settings. The integration of supervised and unsupervised learning methods for fraud detection in eCommerce payment systems is discussed in this paper, along with how AI can help with data privacy, customer authentication, and ongoing learning about new cyberthreats. Predictive analytics to foresee and lessen possible threats, machine learning algorithms for tailored security measures, and AI-driven chatbots for safe customer interactions are examples of emerging trends. Using facial recognition technology that examines facial features, the system will automatically verify the user's identity. Additionally, the user must make a variety of gestures that the system will analyze. Before being entered into the database, this and other private data will be encrypted and transformed into a number. AI-driven IDS can improve e-commerce systems' resistance to cyberattacks by utilizing cutting-edge machine learning techniques like anomaly detection and deep learning.

Specification

Description:AI-DRIVEN ENCRYPTION TECHNIQUES IN BIOMETRIC PAYMENT SYSTEMS FOR E-COMMERCE SECURITY
Technical Field
[0001] The embodiments herein generally relate to a method for AI-driven encryption techniques in biometric payment systems for e-commerce security.
Description of the Related Art
[0002] The E-commerce is the result of a significant change brought about by the incorporation of digital technologies into conventional business models. It entails the exchange of information, goods, and services via electronic channels, with the internet typically leading the way. The foundation for this change was established by early computer networking initiatives and the global expansion of the internet, which allowed businesses to move from physical stores to online marketplaces. Numerous transactions between companies and their customers, as well as between companies and other companies, peers, and customers themselves, are made possible by e-commerce systems. Just as a password can be matched, a financial transaction utilizing a digital payment system that only uses biometric authentication cannot be adequately secured. Therefore, it is necessary to conceal payment information. Big data and artificial intelligence (AI) technologies promise better security measures by introducing information concealment for biometric authentication-based financial transactions. As AI learns and adapts to evade detection, it is anticipated that malware powered by AI will become increasingly complex and challenging to identify. The supply chain is an essential part of e-commerce and must function efficiently, securely, and quickly. One of the logistic industry's most competitive weapons is e-commerce. By developing and identifying unique distribution and sales strategies, e-commerce businesses are currently competing for a bigger share of the retail industry. Increased cash usage is possible with an efficient supply chain.
[0003] The digital platform that acts as a conduit between buyers and sellers is one of the main components of e-commerce. These vary from independent, armed e-commerce stores to complex multi-vendor marketplaces where major digital commerce activities are going on. These platforms use cutting-edge technologies and web development frameworks to offer an interactive, responsive user interface. This implies that front-end development should be done in a way that ensures consumer choice, navigation, and transactions are maintained across all device types.
[0004] The development of new alternatives to the conventional cash form of payment has been influenced by the payment technologies that technological advancements have produced. Near Field Communication (NFC) and other technologies have become viable alternatives to cash in African nations. Despite the effectiveness of integrating new payment technologies into existing payment systems, fraudulent activities such as signal interception during transit and PIN compromise have led to security concerns. Businesses can use information and communication technology to integrate internal and external business operations through electronic commerce. To conduct such commercial activities, businesses use intranets, extranets, and the Internet. E-commerce allows businesses to reduce costs, reach a wider audience, and develop stronger relationships. Payment gateways, which act as a link between financial institutions and online platforms, facilitate financial transactions in e-commerce. These gateways manage bank transfers, credit card payments, and even digital wallets to guarantee the safety and security of all transactions involving the transfer of money from buyer to seller.
SUMMARY
[0001] In view of the foregoing, an embodiment herein provides a method for AI-driven encryption techniques in biometric payment systems for e-commerce security. In some embodiments, wherein another essential component of e-commerce systems, which combine digital transactions with physical fulfillment procedures, are logistics networks. These networks cover the handling, storing, and shipping of goods from warehouses to customers. For orders to be completed correctly and on time, modern logistics rely on automated inventory management and real-time tracking. Through low costs and easy access to financing, fintech technologies improve social equity by better accommodating socioeconomically disadvantaged social groups. However, there is a cascading effect whereby disruptions in financial literacy can intensify the dynamics of social exclusion. In light of recent events in the digital payment service that have increased public anxiety regarding fraud and privacy concerns, the biometric system must be redesigned using a combination of artificial intelligence (AI) and big data analytics in service delivery networks for financial service provision. Therefore, by incorporating biometric authentication into digital payment systems, this study seeks to improve payment system security. Since e-commerce has always been a cutting-edge industry, staying ahead requires taking chances and embracing new ideas. Bold e-commerce decisions have frequently changed the online shopping environment, from the early adoption of mobile-friendly websites to the incorporation of personalized product recommendations and same-day delivery services.
[0002] In some embodiments, wherein the core of an e-commerce platform's functionality is data collection and analysis, which aids businesses in comprehending user behavior and streamlining their processes. E-commerce platforms gather a lot of information about user behavior, browsing habits, and past purchases. This then feeds the analytics engines, which extract information on consumer preferences and market trends using statistical techniques and machine learning algorithms. To enhance user experience and boost conversion rates, these insights drive targeted ads, dynamic pricing models, and recommendations. Artificial intelligence (AI) has emerged as a transformative force across industries, and digital payment systems are no exception. The growing integration of AI technologies into digital payment systems has the potential to completely transform how transactions are carried out, making them quicker, safer, and more user-friendly. Through chatbots, predictive analytics, robotic process automation, and machine learning, artificial intelligence is quickly improving and altering the digital payment process. Global financial institutions make significant investments in AI-powered digital payment systems and frameworks. AI is essential to preserving and improving the security of online financial transactions. The integration of artificial intelligence technologies into online retail operations is known as AI in e-commerce. This includes applying AI to a range of tasks, such as marketing concepts, inventory management, and customer service and product recommendations.
[0003] In some embodiments, wherein increasing online sales has also been greatly aided by the integration of e-commerce platforms with different digital marketing strategies. E-commerce site SEO, social media advertising, and content marketing strategies are some of the tactics used to increase e-commerce sites' visibility and draw in potential clients. These rely on algorithms that examine user engagement metrics and search trends, then produce and refine content based on each search engine's ranking standards. Large volumes of transaction data can be analyzed by AI algorithms, which can then evaluate each transaction based on risk before accepting or rejecting it. There are several ways to combat fraud, including the rule-based method for identifying the most traditional signs of fraud, the machine learning method where the model is trained and learns from experience, and a combination of the two approaches to take advantage of the flexibility of machine learning and the expertise of human-policed rules. With the help of big data architectures and recent AI solutions like deep learning neural networks, a model can be trained using a vast bank of past transactions and continuously adjust to new threats in real time. Customers can upload images of products they like and use AI-powered image recognition to locate similar items in the store's inventory. This feature is particularly useful for fashion and home decor retailers, helping shoppers find exactly what they're looking for without knowing specific product names or categories.
[0004] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0001] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0002] FIG. 1 illustrates a method for AI-driven encryption techniques in biometric payment systems for e-commerce security according to an embodiment herein; and
[0003] FIG. 2 illustrates a method for flow of feature engineering in fraud detection according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0001] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0002] FIG. 1 illustrates a method for AI-driven encryption techniques in biometric payment systems for e-commerce security according to an embodiment herein. In some embodiments, the platforms can effectively store and retrieve user and transaction data across multiple nodes for low-latency responses and high availability thanks to distributed databases, which are more frequently modeled using NoSQL. This infrastructure serves as a dependable foundation for all electronic transactions, allowing the various e-commerce systems to operate without hiccups. Since artificial intelligence (AI) increases security and reduces expenses, it is being swiftly embraced and incorporated into digital payment systems. The best defense against fraud is to use artificial intelligence to continuously monitor accounts or transactions. Fraud entails stealing money or making purchases using someone else's information. Hackers use stolen credit card or debit card details via phishing attacks to commit fraud. Fraud can also happen in a variety of other ways, like creating a new account in someone else's name or altering the account's personal information to avoid paying, among other things. Virtual try-ons are made possible by AI and AR technology for items like furniture, makeup, and glasses. Before making a purchase, customers can view how items will appear on them or in their space, which lowers return rates and boosts confidence in the purchase.
[0003] In some embodiments, one of the fundamental underlying frameworks that enables e-commerce is provided by network security protocols. As a result, it is possible to guarantee the security of the data both during transmission and storage. Secure channels of communication between clients and servers are made possible by the use of HTTPS and SSL/TLS protocols, which also protect sensitive data in terms of confidentiality and integrity, including user credentials and payment information. Furthermore, digital signatures and certificate-based authentication methods ensure that the identities of entities involved in transactions are verified, thereby lowering the frequency of fraudulent activities. Before allowing a transaction to proceed, payment systems would go through this procedure. Chatbots in payment systems improve attendance and customer experience. Companies can let customers engage with bots instead of paying staff to complete every task associated with a single customer. Bot assistants with AI capabilities are designed to assist in resolving client concerns. They answer the question about the problem and take the appropriate action to fix it. Chatbots increase transaction completion rates and significantly reduce costs. Chatbot assistants should be included in payment systems for users who access the service around-the-clock. By doing this, users could eventually remain active with the service rather than stopping because they couldn't get customer support. Chatbots and virtual assistants that can answer consumer questions, process orders, and offer real-time personalized recommendations are made possible by NLP, which gives AI the ability to comprehend and react to human language.
[0004] In some embodiments, the E-commerce has also grown steadily as a result of the application of machine learning and artificial intelligence techniques, which automate some digital transaction processes. By handling customer support and inquiries using natural language processing (NLP), chatbots and virtual assistants lessen the need for human intervention in everyday interactions. The continued use of biometric identification technologies in digital payment methods demonstrates their potential for security and usability. Big data and artificial intelligence (AI) are crucial technologies that banks and other financial institutions can use in addition to biometric authentication to strengthen the security of digital payment systems. Big data technologies like Hadoop and the cloud can facilitate the processing and analysis of vast amounts of data that arrive from mobile devices and application servers in real time. To forecast future trends, improve inventory control, and target marketing campaigns, machine learning algorithms examine past sales data and consumer behavior. This facilitates the making of well-informed decisions that improve business performance and the satisfaction of online shoppers.
[0005] FIG. 2 illustrates a method for flow of feature engineering in fraud detection according to an embodiment herein. In some embodiments, the E-commerce is also susceptible to "friendly fraud," also known as chargeback fraud, in which actual consumers contest the charges made to their credit cards even after they have received the goods or services they purchased. This might be the result of a buyer's regret, intentional deception, or a misunderstanding. Despite coming from actual customers rather than outside attackers, this kind of fraud has grown in frequency due to its misuse of the consumer protection measures intended to guard against real unauthorized transactions. With a variety of peer-to-peer payment services and digital wallets, digital payment systems have proliferated in the current context of increasing online payment and financial services usage. The reliability of these services has been called into question due to security risks to user payment information. A biometric-based mobile app with AI and big data support is being proposed to address the issue of fraudulent transactions that can be started using credentials that have been stolen. This can assist in making sure that users can only initiate their transactions using their card details, such as their PIN and card number, rather than having others impersonate them. AI can improve supply chain operations and inventory management by forecasting demand, maximizing stock levels, and cutting down on inefficiencies. To predict which product will be in demand, it examines market conditions, sales trends, and customer data. Businesses can prevent overstock and stockouts by using this predictive capability to ensure that popular items are always available.
[0006] In some embodiments, the nature of fraud detection has changed significantly as a result of machine learning models, which have improved accuracy and scalability. These models are especially important in banking, eCommerce, and financial services because of the high transaction volume and the need for real-time fraud monitoring. In order to identify and stop fraud in digital payment systems, data analytics is essential. Real-time analysis of massive amounts of transactional data has been made possible by the development of cutting-edge technologies like big data and artificial intelligence (AI). As a result, it is easier to spot suspicious irregularities linked to fraud that describe transactions. Early detection of fraudulent activities is essential to minimizing the harm that fraud could cause to trust in digital payment systems. Data analytics combined with biometric authentication strengthens anti-fraud efforts even more. AI fraud detection in e-commerce examines transaction patterns and consumer behavior to identify irregularities. Unusual activities, like repeated unsuccessful login attempts, inconsistent buying patterns, or irregular payment methods, can be automatically flagged as possible fraud by machine learning algorithms.
[0007] In some embodiments, the random forests increase the stability and accuracy of fraud detection by reducing the inherent variance in single decision trees while combining the outputs of numerous trees. Step-by-step, GBMs construct an ensemble of decision trees, each one attempting to fix the mistakes of the one before it. GBM can achieve a high level of accuracy in this iterative process, particularly in situations where fraud patterns evolve over time. The need to improve security is urgent given the quick uptake of digital payment methods. Digital payment system security lapses can have serious repercussions for businesses, including invasions of customer privacy, diminished competitiveness, and monetary losses. The need for more secure payment methods has been brought to light by recent cases of online transaction fraud. By using distinctive biological characteristics like fingerprints, facial recognition, or iris recognition, biometric authentication can greatly increase the security of digital payment systems. Numerous companies have already integrated biometric authentication into their payment systems, such as Apple, Google, and American Express. AI technologies frequently necessitate major adjustments to existing workflows and infrastructure. Make sure that the AI tools are compatible with their e-commerce platforms and that your current systems can manage their integration. Additionally, operational disruptions may result from the intricacy of integration.



, Claims:1. A method for AI-driven encryption techniques in biometric payment systems for e-commerce security, wherein the method comprises;
providing encryption techniques strengthen biometric payment systems by dynamically adapting to emerging threats, ensuring robust protection for sensitive consumer data;
leveraging machine learning algorithms, these systems can detect and mitigate fraudulent activities in real time, reducing the risk of unauthorized transactions;
allowing the creation of encryption schemes tailored to individual users' biometric patterns, adding an additional layer of security to payment systems;
scaling of e-commerce platforms, enabling secure processing of high transaction volumes;
identifying and respond to potential vulnerabilities autonomously, ensuring continuous protection against advanced cyberattacks;
ensuring compatibility and secure data exchange across various devices and platforms, enabling seamless user transactions; and
improving the accuracy and speed of biometric verification, ensuring swift and secure user authentication during e-commerce transactions.

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