Six emerging trends in media and communications - occasional paper

This paper examines six emerging trends in media and communications, which highlight that consumers are increasingly using personalised access pathways to communications and content services that cut across different networks, devices and services. Introduction The ACMA monitors industry and consumer data to identify changes in the media and communications environment and their impact on regulatory settings. Previous ACMA research, such as Broken concepts1 and the Emerging issues2 series of papers, has identified areas of regulatory strain resulting from changes in this environment. The ACMA’s 2014 data collection program highlighted six further trends that are of particular interest as they indicate challenges to the regulatory frameworks within which the ACMA works. These trends illustrate how developments in communications device technologies and over-the-top (OTT) services and content offer both: new opportunities for businesses and individuals as consumers and citizens potential challenges to confident and optimal use of these new services. The evolving media and communications environment offers new ways to understand and achieve policy objectives, and may expose alternatives to ’black-letter’ regulation. However, changes in media and communications can also strain the effectiveness and efficiency of existing regulatory settings designed in an environment where content and communication services have been delivered by network owners over dedicated networks and devices. The selected trends highlight that consumers are increasingly using personalised access pathways to communications and content services that cut across different networks, devices and services. This paper looks at the implications of these six trends for existing regulatory settings

London Creative and Digital Fusion

date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000The London Creative and Digital Fusion programme of interactive, tailored and in-depth support was designed to support the UK capital’s creative and digital companies to collaborate, innovate and grow. London is a globally recognised hub for technology, design and creative genius. While many cities around the world can claim to be hubs for technology entrepreneurship, London’s distinctive potential lies in the successful fusion of world-leading technology with world-leading design and creativity. As innovation thrives at the edge, where better to innovate than across the boundaries of these two clusters and cultures? This booklet tells the story of Fusion’s innovation journey, its partners and its unique business support. Most importantly of all it tells stories of companies that, having worked with London Fusion, have innovated and grown. We hope that it will inspire others to follow and build on our beginnings.European Regional Development Fund 2007-13

Surveillance and identity: conceptual framework and formal models

Surveillance is recognised as a social phenomenon that is commonplace, employed by governments, companies and communities for a wide variety of reasons. Surveillance is fundamental in cybersecurity as it provides tools for prevention and detection; it is also a source of controversies related to privacy and freedom. Building on general studies of surveillance, we identify and analyse certain concepts that are central to surveillance. To do this we employ formal methods based on elementary algebra. First, we show that disparate forms of surveillance have a common structure and can be unified by abstract mathematical concepts. The model shows that (i) finding identities and (ii) sorting identities into categories are fundamental in conceptualising surveillance. Secondly, we develop a formal model that theorizes identity as abstract data that we call identifiers. The model views identity through the computational lens of the theory of abstract data types. We examine the ways identifiers depend upon each other; and show that the provenance of identifiers depends upon translations between systems of identifiers

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