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Surge of Cyber Scams during the COVID19 Pandemic: Analyzing the Shift in Tactics Kacheru, Goutham; Bajjuru, Rohit; Arthan, Nagaraju
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 02 (2022): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

The global society was majorly impacted due to the COVID19 pandemic, introduction of new normal and providing different opportunities for the cyber criminals. The paper looks into the impact of the pandemic on cybercrime, pointing out how attacks have become more commonplace and varied during a time when people are more fearful. It shows how careful examination of cyber-attacks in light of significant events in the real world reveals new techniques being applied by them. At first, there was a certain lag between the start of the pandemic and cyber-attacks related to it. However, they gradually went up until we were receiving multiple unique attacks per day. Based on data from the UK, it shows (and uses published studies where applicable) how cybercriminals took advantage of within these situations during a series of major events or government announcements to identify and create relevantly targeted campaigns. Certain recommendations are made which may be able to mitigate cybersecurity impact and help guides individuals as they deal with the changing challenge landscape.
AI for Intelligent Customer Service: How Salesforce Einstein is Automating Customer Support Bajjuru, Rohit; Kacheru, Goutham; Arthan, Nagaraju
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 05 (2022): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

This paper focuses on the importance of Artificial Intelligence in Customer Relationship Management and how Salesforce Einstein GPT helps in managing our customer relations. This research, taking a qualitative approach using literature review and cases from Spotify and KONE examines how the companies profited by having Salesforce Einstein implementation. The research examines how AI can improve different areas of CRM such as customized customer experiences, predictive analytics and sales process optimization. OUTCOMES: Realizing the benefits of AI enabled CRM solutions in your organization for better customer interactions and relationship management.
Artificial Intelligence (AI) for Low-Code and No-Code Development: Making Non-Developers Developers in 2024 Kacheru, Goutham; Arthan, Nagaraju; Bajjuru, Rohit
Formosa Journal of Multidisciplinary Research Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjmr.v4i1.13369

Abstract

Low-code and no-code development platforms are here to transform the software development landscape by allowing even non-technical users build applications without the need of their advanced programming skills. Artificial Intelligence (AI) is the most crucial player in this evolution; reinvigorating these platforms with intelligent automation, device responsive templates and user-friendly interfaces (2024). Users can design, build, and deploy applications easily with AI-powered features (e.g., Natural Language Processing (NLP), drag-and-drop functionality to-design application & code-generation tools). Democratization of Application Development These innovations democratize application development, thereby allowing businesses to innovate faster, lessen the reliance on professional developers, and meet the surging demand for digital solutions. In this paper, we look at how AI-assisted low-code and no-code platforms are changing the way new apps are being developed, empowering non-developers to participate in software creation and accelerating automation of app development. It also presents the challenges and future aspects that we have to deal with in order to use AI on these platforms, as well as their future role of building a bridge between technical expertise and creative problem-solving.
Artificial Intelligence in Finance: Predictive Analytics, Fraud Detection, and Risk Management in 2024 Kacheru, Goutham; Bajjuru, Rohit; Arthan, Nagaraju
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13398

Abstract

AI is poised to be transformative across virtually all industries, and the financial sector has already experienced major impacts from AI in predictive analytics, fraud detection and risk management among others. This paper also describes the innovation of AI, machine learning and natural language processing (NLP) technologies and their availability in financial services in 2024. Its scope covers richer credit scoring models which harness predictive analytics to assess borrower performance, more sophisticated fraudulent activity detection frameworks that can identify suspicious transactions in real-time, and countless automated trading algorithms which can dynamically adapt to changing market behaviors. Moreover, Algorithms have also deployed in the way financial institutions are evaluating and handling second risk management; AIdriven Risk Management tools have been also there to facilitate decision making process for operational efficiency. We discuss these challenges, and also show how AI will be a crucial part of fundamentally transforming financial analysis from optimizing customer service interactions to stabilizing the economy.
Dark Web and Cyber Scams: A Growing Threat to Online Safety Arthan, Nagaraju; Kacheru, Goutham; Bajjuru, Rohit
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 2 (2023): International Journal of Multidisciplinary Sciences and Arts, Article April 202
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i1.2395

Abstract

Internet as of now can be considered, and often is, a part of people’s lives, a continuation of its existence; the same can be said about Dark Web: Indeed, when I first applied the analysis to the opportunity sample, with the blink of an eye, it can be readily deduced that this type belongs to the high opportunity type as well as the high threat type. Where in its so-called objectives of private investigations it has assigned the alibi for the mask of its authorizing it now becomes an opened invitation for unlawful activities. This papers therefore seeks to identify the dark Web and some of the positive attributes and some of the negative externalities that are inevitably associated with any attempted formal classification of access to and control over browsers. The following are threats that exist within the platform in the above said area described as attacks, exploits and malwares. They also categories type of kind’s offence in the Dark Web in other to let more people know about existence of the areas and how to protect yourself.