The AI Revolution: Opportunities and Challenges for the Finance Sector

This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance

Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision.  Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness.  The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively.  A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process

IS2020 A Competency Model for Undergraduate Programs in Information Systems: The Joint ACM/AIS IS2020 Task Force

The IS2020 report is the latest in a series of model curricula recommendations and guidelines for undergraduate degrees in Information Systems (IS). The report builds on the foundations developed in previous model curricula reports to develop a major revision of the model curriculum with the inclusion of significant new characteristics. Specifically, the IS2020 report does not directly prescribe a degree structure that targets a specific context or environment. Rather, the IS2020 report provides guidance regarding the core content of the curriculum that should be present but also provides flexibility to customize curricula according to local institutional needs

TOWARDS A GENERIC ONTOLOGY FOR SOLAR IRRADIANCE FORECASTING

The growth of solar energy resources in recent years has led to increased calls for accurate forecasts of solar irradiance for the reliable and sustainable integration of solar into the national grid. A growing body of academic research has developed models for forecasting solar irradiance, identified metrics for comparing solar forecasts, and described applications and end users of solar forecasts. In recent years, many disciplines are developing ontologies to facilitate better communication, improve inter-operabiity and refine knowledge reuse by experts and users of the domain. Ontologies are explicit and formal vocabulary of terms and their relationships. This report describes a step towards using ontologies to describe the knowledge, concepts and relationships in the domain of solar irradiance forecasting to develop a shared understanding for diverse stakeholders that interact with the domain. A preliminary ontology on solar irradiance forecasting was created and validated on three use cases

Sentiment Analysis in Digital Spaces: An Overview of Reviews

Sentiment analysis (SA) is commonly applied to digital textual data, revealing insight into opinions and feelings. Many systematic reviews have summarized existing work, but often overlook discussions of validity and scientific practices. Here, we present an overview of reviews, synthesizing 38 systematic reviews, containing 2,275 primary studies. We devise a bespoke quality assessment framework designed to assess the rigor and quality of systematic review methodologies and reporting standards. Our findings show diverse applications and methods, limited reporting rigor, and challenges over time. We discuss how future research and practitioners can address these issues and highlight their importance across numerous applications.Comment: 44 pages, 4 figures, 6 tables, 3 appendice

A Method for Generating Dynamic Responsible AI Guidelines for Collaborative Action

To improve the development of responsible AI systems, developers are increasingly utilizing tools such as checklists or guideline cards to ensure fairness, transparency, and sustainability. However, these tools face two main challenges. First, they are static and are not meant to keep pace with the latest responsible AI literature and international standards. Second, they tend to prioritize individual usage over fostering collaboration among AI practitioners. To overcome these limitations, we propose a method that enables easy updates of responsible AI guidelines by incorporating research papers and ISO standards, ensuring that the content remains relevant and up to date, while emphasizing actionable guidelines that can be implemented by a wide range of AI practitioners. We validated our method in a case study at a large tech company by designing and deploying a tool that recommends interactive and actionable guidelines, which were generated by a team of engineers, standardization experts, and a lawyer using our method. Through the study involving AI developers and engineers, we assessed the usability and effectiveness of the tool, showing that the guidelines were considered practical and actionable. The guidelines encouraged self-reflection and facilitated a better understanding of the ethical considerations of AI during the early stages of development, significantly contributing to the idea of "Responsible AI by Design" -- a design-first approach that considers responsible AI values throughout the development lifecycle and across business roles.Comment: 26 pages, 5 figures, 4 table