Generative AI-Powered Document Processing at Scale with Fraud Detection for Large Financial Organizations

The world of health insurance and Medicare has traditionally been perceived as complex and difficult to navigate. Fortunately, the application of Generative AI to virtual agents has begun to transform the industry. Large language and image, AI models, also known as generative AI or foundation models, have opened up new prospects for organizations and people involved in content creation. Once trained, a generative model can be "fine-tuned" for a certain content domain with far less data.

With the growing proliferation of generative AI, discussions about the societal implications of AI, including opportunities and risks, have intensified. Ultimately, the success of initiatives to integrate (generative) AI into news production and dissemination will depend on the concerns, trust, and willingness of citizens to accept new AI-driven solutions. This study explores attitudes toward the use of AI in journalism, perceptions of generative AI, and how these factors influence trust in and credibility of information. Using a survey on a representative sample of the Dutch population (N = 1478), we analyze perceived benefits and concerns about AI and explore individual acts of resistance against the application of AI in journalism (e.g., unwillingness to pay for news that AI produces). With this study, we extend previous research on attitudes towards AI by also considering general attitudes towards generative AI, individuals’ risk perceptions towards generative AI, and policy support regarding regulating AI. More importantly, this study also investigates individual follow-up actions in the form of acts of resistance against the use of AI in journalism. The findings of this paper are particularly significant due to the rapid growth of generative AI, its integration into the news cycle, and international policy developments.

There is increasing evidence that, like other institutions, libraries are integrating and using generative AI to support their operations and service delivery. However, scoping reviews to comprehensively establish evidence on the application of AI in libraries are limited. This scoping review aimed to provide insights into the findings from scholarly works published between 1990 and August 2023. The study revealed a tremendous increase in research focusing on the application of generative AI in libraries in recent years. Number of publications on the topic varies significantly across different regions. Geographical regions like Asia have recorded a noticeable number of publications compared to America, Africa, and Europe. The review also found that descriptive, exploratory, and mixed research designs were the most common in the publications. Generative AI technologies such as Chatbots and Robots were widely reported to support multiple library operations and services. While the application of AI in libraries presents many opportunities, it also brings various challenges to libraries and librarians. To stay relevant and important, librarians should not lag behind in integrating and using AI for library operations and service delivery.

Large service companies process an excessive amount of drilling mud reports daily, requiring engineers to perform labor-intensive, costly, and error-prone manual analysis work. Generative AI offers an ideal solution to automate this routine task. This study proposes an innovative yet resource-efficient mud report processing framework using generative AI. Within this framework, an automated pipeline is established to capture and process daily mud reports from varied sources such as emails or pdfs. Mud reports are tabular-rich document and its contents are extracted using Optical Character Recognition (OCR) technologies and Generative AI. The extracted data is stored in structured databases, and then visualized on an interactive business intelligence (BI) dashboard to generate business values and insights. Observations confirm that the proposed method efficiently handles daily drilling mud reports while maintaining near-perfect accuracy with negligible computational time and high consistency of the results. The architecture of the model is designed to effectively handle reports from various existing drilling fluid vendors. Furthermore, it is built to process reports from new, previously unseen vendors in a plug-and-play manner, without requiring any modifications to the existing model. The system offers full transparency in measuring operational efficiency and cost, processing hundreds of mud reports in a fraction of the time compared to traditional manual methods. Detailed analysis shows that the implementation of Generative AI has improved processing efficiency by reducing the time required per report by 99%, resulting in a significant boost in overall productivity. In conclusion, this Generative AI architecture offers a reliable, cost-effective, and scalable solution, revolutionizing the automation of large-scale daily drilling mud report processing within the energy industry. Additionally, methods for estimating Generative AI costs and operational cost reductions are discussed, further highlighting the potential for profitability in the petroleum industry through Generative AI-driven automation.

This paper delves into the realm of recent advancements in artificial intelligence, with a particular focus on Generative AI. Generative AI, an emerging field within AI, leverages machine learning algorithms and neural networks to generate original content across various mediums such as images, music, speech, and text. Its potential to revolutionize industries like advertising, gaming, and healthcare through personalized content creation, task automation, and enhanced accuracy in complex endeavors like drug discovery and medical diagnosis is profound. We explore different models of Generative AI, highlighting their strengths and limitations. Despite being in its early stages, Generative AI presents a promising avenue for research and development, offering numerous unexplored opportunities. Examples of prominent Generative AI models such as ChatGPT and DALL-E are provided, elucidating their applications across diverse domains. Looking forward, the potential applications of Generative AI are vast, including the development of virtual assistants for human interaction, bolstering cybersecurity, and designing intelligent robots for industrial tasks. As Generative AI continues to advance, it holds the promise of driving innovation and transformation across industries, paving the way for growth and progress in the future. Key Words: Generative AI, artificial intelligence, content generation, machine learning, neural networks, industry applications, innovation.

In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From creative tasks such as text summarization, idea generation, and source code generation, to the streamlining of medical support tasks like diagnostic report generation and summarization, AI is now deeply involved in many areas. Today’s breadth of AI applications is clearly distinct from what was seen before generative AI gained widespread recognition. Representative generative AI services include DALL·E 3 (OpenAI, California, USA) and Stable Diffusion (Stability AI, London, England, UK) for image generation, ChatGPT (OpenAI, California, USA), and Gemini (Google, California, USA) for text generation. The rise of generative AI has been influenced by advances in deep learning models and the scaling up of data, models, and computational resources based on the Scaling Laws. Moreover, the emergence of foundation models, which are trained on large-scale datasets and possess general-purpose knowledge applicable to various downstream tasks, is creating a new paradigm in AI development. These shifts brought about by generative AI and foundation models also profoundly impact medical image processing, fundamentally changing the framework for AI development in healthcare. This paper provides an overview of diffusion models used in image generation AI and large language models (LLMs) used in text generation AI, and introduces their applications in medical support. This paper also discusses foundation models, which are gaining attention alongside generative AI, including their construction methods and applications in the medical field. Finally, the paper explores how to develop foundation models and high-performance AI for medical support by fully utilizing national data and computational resources.

PurposeMost recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.Design/methodology/approachFindings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.Findings1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovationResearch limitations/implicationsWe are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.Practical implicationsBusiness application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.Social implicationsTo alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.Originality/valueThis research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.

Generative artificial intelligence has become prevalent in discussions of educational technology, particularly in the context of mathematics education. These AI models can engage in human‐like conversation and generate answers to complex questions in real‐time, with education reports accentuating their potential to make teachers' work more efficient and improve student learning. This paper provides a review of the current literature on generative AI in mathematics education, focusing on four areas: generative AI for mathematics problem‐solving, generative AI for mathematics tutoring and feedback, generative AI to adapt mathematical tasks, and generative AI to assist mathematics teachers in planning. The paper discusses ethical and logistical issues that arise with the application of generative AI in mathematics education, and closes with some observations, recommendations, and future directions.

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.

Purpose In the contemporary business environment, organizations continue to increase their application of generative AI (GenAI) to enhance efficiency and productivity. Therefore, it becomes important to understand the impacts of GenAI on employees’ behaviors and organizations’ outcomes. In this research, we examine the impact of employee experience with GenAI on knowledge sharing, organizational resilience and agility and the role of emotional intelligence as a mediator. Design/methodology/approach The data gathered from 272 employees in various organizations using Qualtrics were analyzed through structural equation modeling to address questions about how employee experience with GenAI influences knowledge-sharing behavior within organizations. Additionally, it examines how knowledge sharing and resilience mediate the relationship between employee experience with GenAI and agility and how emotional intelligence moderates the relationship between employee experience with GenAI and its outcomes. Findings The findings indicate that GenAI is not just a tool but a resource that affects knowledge sharing, resilience and agility. The results indicate that knowledge sharing mediates the relationship between employee experience with GenAI and resilience and employee experience with GenAI and agility. Emotional intelligence emerged as a moderator between GenAI experience and resilience, with no moderation on knowledge sharing or agility. Originality/value The research guides organizations on how to engage GenAI and why it is important to embrace emotional intelligence to improve the outcomes realized from AI integration.

This article provides a critical synthesis and analysis of the research on the application of generative AI (GenAI) in second language (L2) writing. It conceptualizes GenAI literacy, synthesizes the research on written feedback, establishes a framework for prompt engineering, critiques the research examining the validity of GenAI ratings in writing assessment, and summarizes empirical evidence on the differences between GenAI and human writing. Specifically, the following findings and arguments are presented and discussed. GenAI literacy consists of four components pertaining to users’ competence and knowledge of GenAI basics, effective use, output evaluation, and ethics. The research on written feedback shows that teacher feedback focuses more on content, while GenAI feedback focuses more on organization. This research also suggests a need for criteria-based feedback and feedback evaluation. Prompt engineering is discussed along three dimensions: input, task, and output, followed by snapshots of prompts used in feedback research. The studies on writing assessment reveal that GenAI ratings are more consistent with human ratings when GenAI is trained using a large number of scored essays and when the rating criteria are well-defined. Comparisons of GenAI and human writing demonstrate that GenAI writing is more formal, academic, and impersonal, while human writing is more personal, creative, and linguistically accessible. This article concludes by making sense of the research findings, identifying future directions, and proposing three principles that may guide the research, practice, and theory construction for GenAI: individualization, domain-specificity, and writer agency.

This paper describes the design of an Online Management Information System using a website connected to a portal with the adaptation of an OCR (Optical Character Recognition) Technology for Philippine Accounting Firm. The company often handles manual written and printed forms for preserving customer profiles and records of clients' registration required to various government agencies entities such as BIR, SSS, Philhealth and Pag-ibig Forms which were manually kept in the storage upon registration and if needed for data gathering or update, documents were retrieved in the storage manually by the employee since there is no software application developed yet to track the records easily for adding, updating and monitoring each client’s details. This proposed system composed of two parts: a website and a portal where the website will be deployed in the World Wide Web with a unique domain and server which the clients can view and contact for the firm’s services and a connected external link to access the portal’s main system accessed by the firm’s employees (upon registration and approval for signing in) only. The portal has two ways to manage the client’s record using manual keying or adapting the OCR Technology using a scanner as an input device to scan the forms and convert directly to text input in the portal without inputting manually and individually for easy data storage and update. This system provides an accurate, complete monitoring and online management portal recording system with the adaptation of OCR Technology, eliminates the time spent for manual adding, updating and retrieval of client’s records, and tracks the records easily using its enhanced system to easily add, update and monitor each client’s details. This system will also serve as a draft or a starting point for the OCR Technology for application on accounting businesses and benefit other researchers who wish to have similar studies related to OCR Technology.

As generative AI rapidly gains prominence across society, its application and perception are undergoing significant shifts. Even the judiciary, typically slow to adopt advanced technologies, is actively embracing generative AI, signaling a change in perception and the technology's maturity. This suggests that institutional changes for integrating generative AI into civil dispute resolution are likely. For a rational transition, a precise understanding of generative AI's unique characteristics is crucial, along with a preliminary review of essential foundational elements to properly reflect or limit these characteristics. Generative AI possesses advanced characteristics such as the potential for continuous improvement through learning and refinement, the ability for easy and free communication through multimodality, and the capacity for integrated thinking through adaptability and creativity. These advanced characteristics form the basis for utilizing generative AI in civil dispute resolution. However, generative AI also carries inherent limitations like hallucinations, bias, opacity, and personal information exposure. These limitations are critical in the conflict-driven field of civil dispute resolution and must be mitigated or overcome. To effectively utilize generative AI in civil dispute resolution, the institutional framework should be designed to enhance its advanced features while mitigating or overcoming its limitations. This framework should primarily align with the current technological landscape, but also be adaptable to future advancements. Key considerations include: 1. Continuous and Stable Data Acquisition & Timely Refinement: Ensuring a steady supply of high-quality data related to civil dispute resolution, alongside timely adjustments. 2. Court Involvement in Data Acquisition: The judiciary's involvement is necessary for securing high-quality civil dispute resolution data. 3. Court-Led Refinement: Timely adjustments should be spearheaded by the courts. 4. Establishment of a Specialized Closed System: A closed system specifically tailored for civil dispute resolution is required. 5. Court-Led Conciliation or Judicial Settlement as Most Effective Types: At present, the most effective applications of generative AI are in court-led conciliation or judicial settlements. 6. Judges' and Judicial Members' Technical Competence: Judges and other judicial personnel need to acquire technical proficiency. 7. Recognizing Generative AI as a Future Collaborator: Generative AI should be viewed as a future collaborator in civil dispute resolution processes.