The emergence of Open Banking represents a significant shift in financial data management, influencing financial institutions' market dynamics and marketing strategies. This increased competition creates opportunities and challenges, as institutions manage data inflow to improve products and services while mitigating data outflow that could aid competitors. This study introduces a framework to predict customers' propensity to share data via Open Banking and interprets this behavior through Explanatory Model Analysis (EMA). Using data from a large Brazilian financial institution with approximately 3.2 million customers, a hybrid data balancing strategy incorporating ADASYN and NEARMISS techniques was employed to address the infrequency of data sharing and enhance the training of XGBoost models. These models accurately predicted customer data sharing, achieving 91.39% accuracy for inflow and 91.53% for outflow. The EMA phase combined the Shapley Additive Explanations (SHAP) method with the Classification and Regression Tree (CART) technique, revealing the most influential features on customer decisions. Key features included the number of transactions and purchases in mobile channels, interactions within these channels, and credit-related features, particularly credit card usage across the national banking system. These results highlight the critical role of mobile engagement and credit in driving customer data-sharing behaviors, providing financial institutions with strategic insights to enhance competitiveness and innovation in the Open Banking environment.

Sam's Club is going register-free and introducing an all-digital, AI-powered shopping experience for its customers, a move that has privacy advocates worried that the new AI tool could be used to unfairly target some customers with higher-priced items based on their shopping habits. The all-digital approach started with the reconstruction of a Sam's Club in Grapevine, a suburb of Dallas, that was severely damaged in 2022 by a tornado. When the retail location opened two years later it was the first of its kind to ditch its registers for a "Scan and Go" program that allowed customers to scan each item placed in their physical cart and pay through a mobile app. This program has since been piloted in nine Dallas metro locations and one store in Missouri, Retail Dive reported. Instead of handing a receipt to a Sam's Club employee to review before leaving the store, customers walk through an arch that's equipped with AI-powered cameras to capture images of the items in the cart and electronically match them with the items paid for through the app. Sam's Club did not disclose when the AI technology would be coming to California stores but Sam's Club has outlets in Torrance, Fountain Valley, El Monte and Riverside.

Slack trains machine-learning models on user messages, files and other content without explicit permission. The training is opt-out, meaning your private data will be leeched by default. Making matters worse, you'll have to ask your organization's Slack admin (human resources, IT, etc.) to email the company to ask it to stop. Welcome to the dark side of the new AI training data gold rush. Corey Quinn, an executive at DuckBill Group, spotted the policy in a blurb in Slack's Privacy Principles and posted about it on X (via PCMag).

Zoom has made changes to its terms of service after online blowback over recent updates to the company's fine print allowing AI training on customer data. A report from StackDiary over the weekend highlighted how the changes, which rolled out in March without fanfare, appeared to grant the company sweeping control over customer data for AI training purposes. In response, Zoom published a blog post today claiming it wouldn't do what its terms said it could do; the company then updated its terms in response to the continued blowback. It now says it doesn't train AI models on consumer video, audio or chats "without customer consent." At least part of the issue stemmed from Zoom's experimental AI tools, including IQ Meeting Summary (ML-powered summarizations) and IQ Team Chat Compose (AI-powered message drafting).

Private video calls, text messages and meetings on Zoom might be used to'train' artificial intelligence models. The San Jose company's new terms and conditions - which came into force in March but were spotted this month - have sparked a wave of outrage online, with users threatening to cancel their accounts over the change. In one section of the new T C's, it says that customers consent to Zoom using data for purposes such as'machine learning or artificial intelligence (including for the purposes of training and tuning of algorithms and models).' Artificial intelligence models are commonly trained with large amounts of publicly available data, often taken from the internet - but Zoom's move would use private customer data, raising privacy fears. The changes came in paragraph 10.4 of Zoom's Terms and Conditions (Zoom) Zoom has responded with a blog post this week, claiming that the data is only used to train AI models to summarize meetings more accurately, and only with customer consent. In a blog post, Zoom's Chief Product Officer Smita Hashim wrote: 'To reiterate: we do not use audio, video, or chat content for training our models without customer consent.'

Cryptocurrency forensics became standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations. On the other hand, it can have far-reaching consequences for the rights of those affected. Argumentation schemes could remedy this untenable situation by rendering underlying premises transparent. Additionally, they can aid in critically evaluating the probative value of any results obtained by cryptocurrency deanonymisation techniques. In the argumentation theory and AI community, argumentation schemes are influential as they state implicit premises for different types of arguments. Through their critical questions, they aid the argumentation participants in critically evaluating arguments. We specialise the notion of argumentation schemes to legal reasoning about cryptocurrency deanonymisation. Furthermore, we demonstrate the applicability of the resulting schemes through an exemplary real-world case. Ultimately, we envision that using our schemes in legal practice can solidify the evidential value of blockchain investigations as well as uncover and help address uncertainty in underlying premises - thus contributing to protect the rights of those affected by cryptocurrency forensics.

We have known personalisation to be a winning tactic for years. But what could we do if we go beyond the possibilities of yesterday, and create new content and experiences on the fly? With personalisation as the starting point, brief AI to generate new content that is totally personalised? This is what Generative Personalisation is all about. And as this is a new concept and application of technology, there was no term for it.

AI is changing the way; we engage with technology and are not only for the consumer, but it transforms businesses. AI apps relate to personalized suggestions to chatbots. There are many AI solutions in reality that help optimize workflows, expedite procedures, and improve the performance of businesses. It is one of the powerful tools driving conversions. It offers a welcoming feel upon hearing their names, and there is a sense of connection.

Artificial Intelligence (AI) is rapidly changing the business landscape by offering new and innovative ways to streamline operations, enhance customer experience, and drive growth. As an entrepreneur or small business owner, you may be wondering what AI tools are available to help you optimise your business processes. Here are 3 of the most popular AI tools that can help you achieve your business goals. IBM Watson is a comprehensive AI tool that offers a range of services, including natural language processing, speech-to-text conversion, image recognition, and predictive analytics. For example, Watson can help you optimize your marketing campaigns by analysing customer interactions on social media and identifying trends and patterns in consumer behaviour.

Artificial Intelligence is creating a buzzword with a significant aspect in the Finance sector. The financial sector around the world is trying to adopt & implement AI in its finance service capabilities. Exponential growth in the finance sector is measured in the last few years using Predictive Analysis. AI/machine learning technologies are helping bank business services to engage their potential customers. The rising popularity of messaging apps and the higher demands of customers in the banking, health, or wellness industry is giving chatbots a boost.