AI-Driven Competitive Intelligence and Next-Generation Security

For forecasting and risk management, the financial sector has historically depended on statistical models and historical data. Artificial intelligence (AI), on the other hand, is poised to completely transform traditional methods. This book examines how artificial intelligence (AI) can revolutionize risk management and financial forecasting. The book explores the ways in which artificial intelligence (AI) methods, such as machine learning and deep learning, may analyse enormous volumes of data from various sources to spot intricate patterns and relationships that conventional approaches might overlook. As a result, risk assessments and projections become more precise, empowering financial institutions to make more educated choices. The book also discusses the difficulties in integrating AI in finance, such as obstacles related to regulations, data quality, and model explainability. It offers suggestions on how to get past these obstacles and successfully incorporate AI into financial processes. Financial organizations can obtain a major competitive edge by utilizing AI. Anyone interested in the future of finance and the potentially disruptive power of artificial intelligence should read this book.

The paper explores supply chain management, emphasizing the importance of accurate demand forecasting. The word supply chain refers to the product journey from supplier to consumer. Demand forecasting is the anticipation of the market’s demand for a product. Various models that can be used for accurate prediction include ARIMA, LSTM, STL, Prophet, GBM, SVM, KNN, Neural Networks, etc.This paper compares five algorithms from the available algorithms pool: ARIMA, LSTM, FBProphet, XGBoost, and Decision Tree Regressor. Two datasets, sourced from Yahoo Finance, spanning a decade and including real-time sales data of Walmart and Amazon, are used for the study. The optical model for demand forecasting depends upon specific evaluation metrics and the nature of the dataset. Taking MAE (Walmart sales data: 0.036588, Amazon sales data: 0.066181) into account, FB-Prophet is a suitable model for both datasets. ARIMA can also be considered an appropriate model for linear data. Moreover, considering the diverse evaluation metrics, LSTM distinguishes itself as a formidable choice among other models for capturing complex relationships and predicting accurate demand.

This research presents a groundbreaking Deep Learning-Based Model for Financial Distress Prediction, leveraging advanced neural network architectures to enhance the accuracy and reliability of forecasting financial distress. The model is designed to autonomously learn intricate patterns and relationships within extensive financial datasets, addressing the limitations of traditional methods in capturing complex, non-linear dependencies. Key to its efficacy is the incorporation of different deep learning models like CNN, ANN, LSTM, BiLSTM and XGBoost, enabling the model to grasp sequential dependencies within financial time series data. This temporal awareness is crucial for understanding the evolving nature of financial indicators over time, providing a more nuanced perspective on potential distress signals. The model's strength lies in its ability to automatically extract relevant features, eliminating the need for manual feature engineering, which is a common challenge in traditional models. As financial markets continue to grow in complexity, the Deep Learning-Based Model offers a proactive and innovative response to the evolving challenges of risk management.

Bank loans are essential finance tools that enable individuals to capitalize on investments, purchase, or improve the family's economy by covering unseen expenses. Nowadays, banks are actively offering loans to support business growth through various investment strategies—ranging from low to high risk—with the aim of improving individuals' lifestyles and social status. Transactions play an essential role in processing debit, credit, internet banking, and other modes of payment and play a vital role in today’s processing either in bank or gold transfer or online transfer and even in digital currencies and market trends to predict share market business stocks. The number of loan applications is increasing daily for loan processing to verify the customer base for loan approval. Bank policies are also stringent in selecting genuine or fake applicants by selecting various parameters for loan approval. Based on a few parameters about eligibility and the Cibil score, the bankers must decide whether they are eligible for loan approval. In this study, machine learning algorithms were applied to predict whether an individual is eligible or ineligible for a loan based on previous related records of the person on whom the loan was previously accredited and initiated by the naive Bayes classifier and supported Support Vector classifier. This study employed further classification using an extreme gradient classifier algorithm and proceeded by boosting this classifier using XGB boosting methods for the prediction with maximum choices with ensemble learning. Finally, text analysis methods are used to identify the accuracy, false positive rates, true favorable, and F1 scores. The proposed prediction model provides 98.5% accuracy for bank loan approval with the educated level, namely graduated and un-graduated levels.

This paper presents a comprehensive review of the literature on the adoption of electronic payment (E-Payment) systems in the Indian context. Through an extensive examination of existing studies, the paper explores the various factors influencing E-Payment adoption, including perceived usefulness, ease of use, facilitating conditions, and system credibility. Moreover, their study investigates the role of demographic factors—such as gender, education, employment status, age, and technological literacy—as moderators influencing individuals' attitudes toward epayment. A key finding is the need for targeted interventions to enhance digital payment literacy among young consumers, suggesting opportunities for educational initiatives and user experience enhancements. The paper concludes with recommendations for policy makers, businesses, and researchers to address the identified gaps and foster wider adoption of E -Payment solutions in India's evolving digital economy. The findings highlight the significance of addressing trust, security concerns, and technological literacy to foster greater acceptance of E-Payment systems. Moving forward, further research is recommended to explore emerging trends and innovations in the digital payment landscape, ensuring the continued relevance and effectiveness of interventions aimed at driving E-Payment adoption in India.

The increasing prevalence of cyberattacks and intrusions in the digital era has made it crucial for organizations to invest in adequate security systems. Intruder Detection Systems (IDS) are designed to identify and prevent unauthorized access to networks and sensitive information. Network intrusion detection systems are a crucial component of network security, enabling the prevention and detection of network attacks. Traditional ML methods for intruder detection systems, such as rule-based systems, have limitations in detecting complex and evolving threats. These systems rely on predefined rules and signatures, making them less adaptable to detect new attacks. Deep learning is emerging as a powerful technique in IDS, significantly enhancing their ability to secure networks and devices. This review paper examines DL-based applications in intruder detection systems (IDS) and describes how Deep Learning techniques can detect intrusions in cyber-attacks.

The proliferation of social networking sites has significantly improved longdistance communication and fostered a more interconnected global community. However, it also allows intruders to engage in surveillance and fraudulent activities. There has been a significant increase in such instances involving fake profiles, especially in recent years. To detect such profiles, this work presents a novel iterative greedy approach for detecting counterfeit profiles on social media networks. The work has considerable societal implications, as most antisocial behaviors on social networks are primarily carried out through fake accounts. The proposed methodology is implemented on three popular datasets. During the experimental setup, the base classifiers used are Support Vector Machine, Decision Tree, Logistic Regression, Gaussian Naïve Bayes, and k-Nearest Neighbor. The optimized ensemble model utilizes two decision trees and one k-Nearest Neighbor. The yielded ensemble model is also compared with base classifiers and traditional ensemble models, and it is observed that the proposed ensemble model outperforms base classifiers and traditional ensemble models in terms of F-score and accuracy. 

Mobile Ad Hoc Networks (MANETs) are a dynamic and self-configuring wireless communication system that is essential in a wide range of applications, including military operations and disaster recovery situations. The effectiveness and dependability of communication in MANETs are greatly contingent upon the routing algorithms utilized. This study provides a thorough examination of the efficacy of MANET routing algorithms, using the capabilities of machine learning methodologies. The chapter begins by examining current MANET routing protocols, highlighting their fundamental attributes and challenges in dynamic and unpredictable network environments. To adapt to the changing characteristics of Mobile Ad hoc Networks (MANETs), machine learning algorithms are employed to forecast network circumstances, including connection quality, node mobility, and congestion. These factors have a substantial influence on the efficiency of routing. To assess the suggested method, comprehensive simulations are carried out employing well-known MANET routing protocols and diverse machine learning techniques. Performance measures, such as packet delivery ratio, end-to-end latency, and network throughput, are used to evaluate the efficiency of routing algorithms in various situations. The simulations yield valuable insights on the flexibility and robustness of routing protocols when combined with machine learning predictions. Moreover, the research examines the consequences of incorporating machine learning into MANET routing algorithms, taking into account aspects such as the computational burden and resources limitations inherent in mobile devices. Additionally, it examines the possibility of utilizing adaptive learning techniques to modify routing algorithms in response to current network circumstances flexibly. The study findings contribute to the ongoing discussion on enhancing the efficiency of Mobile Ad hoc Networks (MANETs) by providing a detailed understanding of how machine learning can be utilized to improve routing algorithms. The suggested method presents a hopeful path for future investigation and advancement in the field of mobile ad hoc networks, with possible uses in enhancing communication dependability and efficiency in various changing situations.

The core objective was to create an innovative Autonomous Surveillance and Patrol System, uniting the realms of computer vision and drone technology. It addresses critical issues such as manual tracking in crowded or hard-to-reach areas, as well as tracking suspects within densely populated zones. The project distinguishes itself by incorporating YOLO v8, a state-of-the-art computer vision model, for precise object detection and facial recognition, thereby enabling effective facial tracking. Coupled with the versatility of the DJI Tello Nano drone, this offers a comprehensive solution to these challenges, adaptable to specific needs, all within a single integrated framework.

The chapter on “Security and Privacy Concerns in Smart Systems” explores the complex landscape of securing interconnected smart technologies. It begins with an elucidation of smart systems, encompassing Internet of Things (IoT), smart homes, cities, and industrial applications, emphasizing their efficiency and convenience. Subsequently, the narrative shifts to outlining the multifaceted security and privacy challenges inherent in these systems, emphasizing the imperatives of user trust and technological optimization. A detailed exposition on common security threats, including cyberattacks and data breaches, is provided, accompanied by illustrative case studies. Privacy concerns, such as data collection, user consent, and surveillance, are scrutinized, and bolstered by pertinent examples. The chapter culminates in a discussion of best practices, emerging technologies, and future trends, advocating for a holistic approach to fortify the security and privacy posture of smart systems in a rapidly evolving digital landscape.

 In the rapidly evolving landscape of technology, the intersection of smart systems and machine learning holds immense potential for shaping the future. This chapter explores the visionary outlook for smart systems and machine learning, envisioning transformative possibilities and pathways to their realization across diverse domains, while addressing the challenges and opportunities that accompany these groundbreaking technologies.

Digital investigations in cybersecurity are crucial for spotting and addressing cyber threats like Distributed Denial of Service (DDoS) attacks. With DDoS attacks becoming more complex and widespread, traditional forensic techniques struggle to keep up with these evolving threats. To tackle this challenge, this chapter suggests a new method for forensics investigation centered on DDoS attacks. By drawing on insights from studies on DDoS attack detection and transfer learning methods, the proposed approach aims to boost the effectiveness and precision of investigations within networking environments. The proposed framework is designed for conducting investigations that cater to the unique aspects of DDoS attacks by integrating expert systems, fuzzy logic, and deep transfer learning. A clustering-enhanced Transfer Learning strategy named CeHTL is implemented to detect connections between known attacks. This method was tested when the test dataset consisted of types or subtypes of attacks not found in the training data. Standard classification models like decision trees and KNN, alongside innovative transfer learning techniques, are used as benchmarks. The outcomes demonstrated enhancements in performance with the proposed CeHTL, utilizing trapezoidal Membership function generation methods, which showcased its effectiveness in identifying new network threats. 

This chapter examines the prevalence and impact of cybercrime in India, focusing on various cyber threats and their consequences on individuals, businesses, and society. Drawing on data collected from a diverse sample of participants aged 15 to 67, the study reveals alarming statistics: over 70% of respondents reporting exposure to cybercrime. The most prevalent forms of cybercrime include online payment fraud, identity theft, phishing, and malware attacks. The chapter also explores the emotional and psychological impact of cybercrime, highlighting its profound effects on victim trust and mental well-being. In response to cyber threats, the study finds that individuals often bolster their security measures or decrease their online activity, reflecting a heightened vulnerability and distrust in digital platforms. Regression analysis reveals correlations between age groups and cybercrime victimization, emphasizing the need for targeted interventions and digital literacy initiatives. The findings underscore the urgent need for comprehensive cybersecurity measures, user education, and support structures to mitigate the impact of cybercrime and foster a safer online environment for all.