Over the past year, the tech giant says it has prevented $4 billion in fraud attempts, blocking approximately 1.6 million bot sign-up attempts every hour – showing the scale of this growing threat.

The ninth edition of Microsoft’s Cyber Signals report, titled “AI-powered deception: Emerging fraud threats and countermeasures,” reveals how artificial intelligence has lowered the technical barriers for cybercriminals, enabling even low-skilled actors to generate sophisticated scams with minimal effort.

What previously took scammers days or weeks to create can now be accomplished in minutes.

The democratisation of fraud capabilities represents a shift in the criminal landscape that affects consumers and businesses worldwide.

The evolution of AI-enhanced cyber scams

Microsoft’s report highlights how AI tools can now scan and scrape the web for company information, helping cybercriminals build detailed profiles of potential targets for highly-convincing social engineering attacks.

Bad actors can lure victims into complex fraud schemes using fake AI-enhanced product reviews and AI-generated storefronts, which come complete with fabricated business histories and customer testimonials.

According to Kelly Bissell, Corporate Vice President of Anti-Fraud and Product Abuse at Microsoft Security, the threat numbers continue to increase. “Cybercrime is a trillion-dollar problem, and it’s been going up every year for the past 30 years,” per the report.

“I think we have an opportunity today to adopt AI faster so we can detect and close the gap of exposure quickly. Now we have AI that can make a difference at scale and help us build security and fraud protections into our products much faster.”

The Microsoft anti-fraud team reports that AI-powered fraud attacks happen globally, with significant activity originating from China and Europe – particularly Germany, due to its status as one of the largest e-commerce markets in the European Union.

The report notes that the larger a digital marketplace is, the more likely a proportional degree of attempted fraud will occur.

E-commerce and employment scams leading

Two particularly concerning areas of AI-enhanced fraud include e-commerce and job recruitment scams.In the ecommerce space, fraudulent websites can now be created in minutes using AI tools with minimal technical knowledge.

Sites often mimic legitimate businesses, using AI-generated product descriptions, images, and customer reviews to fool consumers into believing they’re interacting with genuine merchants.

Adding another layer of deception, AI-powered customer service chatbots can interact convincingly with customers, delay chargebacks by stalling with scripted excuses, and manipulate complaints with AI-generated responses that make scam sites appear professional.

Job seekers are equally at risk. According to the report, generative AI has made it significantly easier for scammers to create fake listings on various employment platforms. Criminals generate fake profiles with stolen credentials, fake job postings with auto-generated descriptions, and AI-powered email campaigns to phish job seekers.

AI-powered interviews and automated emails enhance the credibility of these scams, making them harder to identify. “Fraudsters often ask for personal information, like resumes or even bank account details, under the guise of verifying the applicant’s information,” the report says.

Red flags include unsolicited job offers, requests for payment and communication through informal platforms like text messages or WhatsApp.

Microsoft’s countermeasures to AI fraud

To combat emerging threats, Microsoft says it has implemented a multi-pronged approach across its products and services. Microsoft Defender for Cloud provides threat protection for Azure resources, while Microsoft Edge, like many browsers, features website typo protection and domain impersonation protection. Edge is noted by the Microsoft report as using deep learning technology to help users avoid fraudulent websites.

The company has also enhanced Windows Quick Assist with warning messages to alert users about possible tech support scams before they grant access to someone claiming to be from IT support. Microsoft now blocks an average of 4,415 suspicious Quick Assist connection attempts daily.

Microsoft has also introduced a new fraud prevention policy as part of its Secure Future Initiative (SFI). As of January 2025, Microsoft product teams must perform fraud prevention assessments and implement fraud controls as part of their design process, ensuring products are “fraud-resistant by design.”

As AI-powered scams continue to evolve, consumer awareness remains important. Microsoft advises users to be cautious of urgency tactics, verify website legitimacy before making purchases, and never provide personal or financial information to unverified sources.

For enterprises, implementing multi-factor authentication and deploying deepfake-detection algorithms can help mitigate risk.

See also: Wozniak warns AI will power next-gen scams

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Alarming rise in AI-powered scams: Microsoft reveals $4B in thwarted fraud appeared first on AI News.

The world’s third largest economy and host nation for GITEX EUROPE x Ai Everything, Germany’s role as the European economic and technology leader is confirmed as its ICT sector is projected to reach €232.8bn in 2025 (Statista).

GITEX EUROPE x Ai Everything is Europe’s largest tech, startup and digital investment event, and is organised by KAOUN International. It’s hosted in partnership with the Berlin Senate Department for Economics, Energy and Public Enterprises, Germany’s Federal Ministry for Economic Affairs and Climate Action, Berlin Partner for Business and Technology, and the European Innovation Council (EIC).

Global tech engages for cross-border and industry partnerships

The first GITEX EUROPE brings together over 1,400 tech enterprises, startups and SMEs, and platinum sponsors AWS and IBM. Also in sponsorship roles are Cisco, Cloudflare, Dell, Fortinet, Lenovo, NTT, Nutanix, Nvidia, Opswat, and SAP.

GITEX EUROPE x Ai Everything will comprise of tech companies from over 100 countries and 34 European states, including tech pavilions from India, Italy, Morocco, Netherlands, Poland, Serbia, South Korea, UK, and the UAE.

Trixie LohMirmand, CEO of KAOUN International, organiser of GITEX worldwide, said: “There is a sense of urgency and unity in Europe to assert its digital sovereignty and leadership as a global innovation force. The region is paving its way as a centre-stage where AI, quantum and deep tech will be debated, developed, and scaled.”

Global leaders address EU’s tech crossroads

Organisers state there will be over 500 speakers, debating a range of issues including AI and quantum, cloud, and data sovereignty.

Already confirmed are Geoffrey Hinton, Physics Nobel Laureate (2024); Kai Wegner, Mayor of Berlin; H.E. Jelena Begović, Serbian Minister of Science, Technological Development and Innovation; António Henriques, CEO, Bison Bank; Jager McConnell, CEO, Crunchbase; Mark Surman, President, Mozilla; and Sandro Gianella, Head of Europe & Middle East Policy & Partnerships, OpenAI.

Europe’s moves in AI, deep tech & quantum

Europe is focusing on cross-sector AI uses, new investments and international partnerships. Ai Everything Europe, the event’s AI showcase and conference, brings together AI architects, startups and investors to explore AI ecosystems.

Topics presented on stage range from EuroStack ambitions to implications of agentic AI, with speakers including Martin Kon, President and COO, Cohere; Daniel Verten, Strategy Partner, Synthesia; and Professor Dr. Antonio Krueger, CEO of German Research Centre for Artificial Intelligence.

On the show-floor, attendees will be able to experience Brazil’s Ubivis’s smart factory technology, powered by IoT and digital twins, and Hexis’s AI-driven nutrition plans that are trusted by 500+ Olympic and elite athletes.

With nearly €7 billion in quantum investment, Europe is pushing for quantum leadership by 2030. GITEX Quantum Expo (GQX) (in partnership with IBM and QuIC) covers quantum research and cross-industry impact with showcases and conferences.

Speakers include Mira Wolf-Bauwens, Responsible Quantum Computing Lead, IBM Research, Switzerland; Joachim Mnich, Director of Research & Computing, CERN, Switzerland; Neil Abroug, Head of the French National Quantum Strategy, INRIA; and Jan Goetz, CEO & Co-Founder, IQM Quantum Computers, Finland.

Cyber Valley: Building a resilient cyber frontline

With cloud breaches doubling in number and AI-driven attacks, threat response and cyber resilience are core focuses at the event. Fortinet, CrowdStrike, Kaspersky, Knowbe4, and Proofpoint will join other cybersecurity companies exhibiting at GITEX Cyber Valley.

They’ll be alongside law enforcement leaders, global CISOs, and policymakers on stage, including Brig. Gen. Dr. Volker Pötzsch, Chief of Division Cyber/IT & AI, Federal Ministry of Defence, Germany; H.E. Dr. Mohamed Al-Kuwaiti, Head of Cybersecurity, UAE Government; Miguel De Bruycker, Managing Director General, Centre for Cybersecurity Belgium; and Ugo Vignolo Lutati, Group CISO, Prada Group.

GITEX Green Impact: For a sustainable future

GITEX Green Impact connects innovators and investors with over 100 startups and investors exploring how green hydrogen, bio-energy, and next-gen energy storage are moving from R&D to deployment.

Key speakers so far confirmed are Gavin Towler, Chief Scientist for Sustainability Technologies & CTO, Honeywell; Julie Kitcher, Chief Sustainability Officer, Airbus; Lisa Reehten, Managing Director, Bosch Climate Solutions; Massimo Falcioni, Chief Competitiveness Officer, Abu Dhabi Investment Office; and Mounir Benaija, CTO – EV & Charging Infrastructure, TotalEnergies.

Convening the largest startup ecosystem among 60+ nations

GITEX EUROPE x Ai Everything hosts North Star Europe, the local version of the world’s largest startup event, Expand North Star.

North Star Europe gathers over 750 startups and 20 global unicorns, among them reMarkable, TransferMate, Solarisbank AG, Bolt, Flix, and Glovo.

The event features a curated collection of earlys and growth-stage startups from Belgium, France, Hungary, Italy, Morocco, Portugal, Netherlands, Switzerland, Serbia, UK, and UAE.

Among the startups, Neurocast.ai (Netherlands) is advancing AI-powered neurotech for Alzheimer’s research; CloudBees (Switzerland) is the delivery unicorn backed by Goldman Sachs, HSBC, and Lightspeed; and Semiqon (Finland), the world’s first CMOS transistor with the ability to perform in cryogenic conditions.

More than 600 investors with $1tn assets under management will be scouting for new opportunities, including Germany’s Earlybird VC, Austria’s SpeedInvest, Switzerland’s B2Venture, Estonia’s Startup Wise Guys, and the US’s SOSV.

GITEX ScaleX launches as a first-of-its-kind growth platform for scale-ups and late-stage companies, in partnership with AWS.

With SMEs making up 99% of European businesses, GITEX SMEDEX connects SMEs with international trade networks and investors, for funding, legal advice, and market access to scale globally.

Backed by EISMEA and ICC Digital Standards Initiative, the event features SME ecosystem leaders advising from the stage, including Milena Stoycheva, Chairperson of Board of Innovation, Ministry of Innovation and Growth, Bulgaria; and Oliver Grün, President, European Digital SME Alliance and BITMi.

GITEX EUROPE is part of the GITEX global network tech and startup events, taking place in Germany, Morocco, Nigeria, Singapore, Thailand, and the UAE.

For more information, please visit: www.gitex-europe.com.

The post Reigniting the European digital economy’s €200bn AI ambitions appeared first on AI News.

As digital platforms multiply and user-generated content expands very quickly, the need for effective harmful content detection becomes paramount. What once relied solely on the diligence of human moderators has given way to agile, AI-powered tools reshaping how communities and organisations manage toxic behaviours in words and visuals.

From moderators to machines: A brief history

Early days of content moderation saw human teams tasked with combing through vast amounts of user-submitted materials – flagging hate speech, misinformation, explicit content, and manipulated images.

While human insight brought valuable context and empathy, the sheer volume of submissions naturally outstripped what manual oversight could manage. Burnout among moderators also raised serious concerns. The result was delayed interventions, inconsistent judgment, and myriad harmful messages left unchecked.

The rise of automated detection

To address scale and consistency, early stages of automated detection software surfaced – chiefly, keyword filters and naïve algorithms. These could scan quickly for certain banned terms or suspicious phrases, offering some respite for moderation teams.

However, contextless automation brought new challenges: benign messages were sometimes mistaken for malicious ones due to crude word-matching, and evolving slang frequently bypassed protection.

AI and the next frontier in harmful content detection

Artificial intelligence changed this field. Using deep learning, machine learning, and neural networks, AI-powered systems now process vast and diverse streams of data with previously impossible nuance.

Rather than just flagging keywords, algorithms can detect intent, tone, and emergent abuse patterns.

Textual harmful content detection

Among the most pressing concerns are harmful or abusive messages on social networks, forums, and chats.

Modern solutions, like the AI-powered hate speech detector developed by Vinish Kapoor, demonstrate how free, online tools have democratised access to reliable content moderation.

The platform allows anyone to analyse a string of text for hate speech, harassment, violence, and other manifestations of online toxicity instantly – without technical know-how, subscriptions, or concern for privacy breaches. Such a detector moves beyond outdated keyword alarms by evaluating semantic meaning and context, so reducing false positives and highlighting sophisticated or coded abusive language drastically. The detection process adapts as internet linguistics evolve.

Ensuring visual authenticity: AI in image review

It’s not just text that requires vigilance. Images, widely shared on news feeds and messaging apps, pose unique risks: manipulated visuals often aim to misguide audiences or propagate conflict.

AI-creators now offer robust tools for image anomaly detection. Here, AI algorithms scan for inconsistencies like noise patterns, flawed shadows, distorted perspective, or mismatches between content layers – common signals of editing or manufacture.

The offerings stand out not only for accuracy but for sheer accessibility. Their completely free resources, overcome lack of technical requirements, and offer a privacy-centric approach that allows hobbyists, journalists, educators, and analysts to safeguard image integrity with remarkable simplicity.

Benefits of contemporary AI-powered detection tools

Modern AI solutions introduce vital advantages into the field:

• Instant analysis at scale: Millions of messages and media items can be scrutinized in seconds, vastly outpacing human moderation speeds.

• Contextual accuracy: By examining intent and latent meaning, AI-based content moderation vastly reduces wrongful flagging and adapts to shifting online trends.

• Data privacy assurance: With tools promising that neither text nor images are stored, users can check sensitive materials confidently.

• User-friendliness: Many tools require nothing more than scrolling to a website and pasting in text or uploading an image.

The evolution continues: What’s next for harmful content detection?

The future of digital safety likely hinges on greater collaboration between intelligent automation and skilled human input.

As AI models learn from more nuanced examples, their ability to curb emergent forms of harm will expand. Yet human oversight remains essential for sensitive cases demanding empathy, ethics, and social understanding.

With open, free solutions widely available and enhanced by privacy-first models, everyone from educators to business owners now possesses the tools to protect digital exchanges at scale – whether safeguarding group chats, user forums, comment threads, or email chains.

Conclusion

Harmful content detection has evolved dramatically – from slow, error-prone manual reviews to instantaneous, sophisticated, and privacy-conscious AI.

Today’s innovations strike a balance between broad coverage, real-time intervention, and accessibility, reinforcing the idea that safer, more positive digital environments are in everyone’s reach – no matter their technical background or budget.

(Image source: Pexels)

The post The evolution of harmful content detection: Manual moderation to AI appeared first on AI News.

Google’s protocol will standardise AI agent communication

Google unveiled its Agent2Agent (A2A) protocol at Cloud Next 2025 in an effort to standardise communication between diverse AI agents. A2A is an open protocol that allows independent AI agents to communicate and cooperate. It complements Anthropic’s Model Context Protocol (MCP), which provides models with context and tools. MCP connects agents to tools and other resources, and A2A connects agents to other agents. Google’s new protocol facilitates collaboration among AI agents on different platforms and vendors, and ensures secure, real-time communication, and task coordination.

The two roles in an A2A-enabled system are a client agent and a remote agent. The client initiates a task to achieve a goal or on behalf of a user, It makes requests which the remote agent receives and acts on. Depending on who initiates the communication, an agent can be a client agent in one interaction and a remote agent in another. The protocol defines a standard message format and workflow for the interaction.

Tasks are at the heart of A2A, with each task representing a work or conversation unit. The client agent sends the request to the remote agent’s send or task endpoint. The request includes instructions and a unique task ID. The remote agent creates a new task and starts working on it.

Google enjoys broad industry support, with contributions from more than 50 technology partners like Intuit, Langchain, MongoDB, Atlassian, Box, Cohere, PayPal, Salesforce, SAP, Workday, ServiceNow, and UKG. Reputable service providers include Capgemini, Cognizant, Accenture, BCG, Deloitte, HCLTech, McKinsey, PwC, TCS, Infosys, KPMG, and Wipro.

How HyperCycle aligns with A2A principles

HyperCycle’s Node Factory framework makes it possible to deploy multiple agents, addressing existing challenges and enabling developers to create reliable, collaborative setups. The decentralised platform is advancing the bold concept of “the internet of AI” and using self-perpetuating nodes and a creative licensing model to enable AI deployments at scale. The framework helps achieve cross-platform interoperability by standardising interactions and supporting agents from different developers so agents can work cohesively, irrespective of origin.

The platform’s peer-to-peer network links agents across an ecosystem, eliminating silos and enabling unified data sharing and coordination across nodes. The self-replicating nodes can scale, reducing infrastructure needs and distributing computational loads.

Each Node Factory replicates up to ten times, with the number of nodes in the Factory doubling each time. Users can buy and operate Node Factories at ten different levels. Growth enhances each Factory’s capacity, fulfilling increasing demand for AI services. One node might host a communication-focused agent, while another supports a data analysis agent. Developers can create custom solutions by crafting multi-agent tools from the nodes they’re using, addressing scalability issues and siloed environments.

HyperCycle’s Node Factory operates in a network using Toda/IP architecture, which parallels TCP/IP. The network encompasses hundreds of thousands of nodes, letting developers integrate third-party agents. A developer can enhance function by incorporating a third-party analytics agent, sharing intelligence, and promoting collaboration across the network.

According to Toufi Saliba, HyperCycle’s CEO, the exciting development from Google around A2A represents a major milestone for his agent cooperation project. The news supports his vision of interoperable, scalable AI agents. In an X post, he said many more AI agents will now be able to access the nodes produced by HyperCycle Factories. Nodes can be plugged into any A2A, giving each AI agent in Google Cloud (and its 50+ partners) near-instant access to AWS agents, Microsoft agents, and the entire internet of AI. Saliba’s statement highlights A2A’s potential and its synergy with HyperCycle’s mission.

The security and speed of HyperCycle’s Layer 0++

HyperCycle’s Layer 0++ blockchain infrastructure offers security and speed, and complements A2A by providing a decentralised, secure infrastructure for AI agent interactions. Layer 0++ is an innovative blockchain operating on Toda/IP, which divides network packets into smaller pieces and distributes them across nodes.

It can also extend the usability of other blockchains by bridging to them, which means HyperCycle can enhance the functionality of Bitcoin, Ethereum, Avalanche, Cosmos, Cardano, Polygon, Algorand, and Polkadot rather than compete with those blockchains.

DeFi, decentralised payments, swarm AI, and other use cases

HyperCycle has potential in areas like DeFi, swarm AI, media ratings and rewards, decentralised payments, and computer processing. Swarm AI is a collective intelligence system where individual agents collaborate to solve complicated problems. They can interoperate more often with HyperCycle, leading to lightweight agents carrying out complex internal processes.

The HyperCycle platform can improve ratings and rewards in media networks through micro-transactions. The ability to perform high-frequency, high-speed, low-cost, on-chain trading presents innumerable opportunities in DeFi.

It can streamline decentralised payments and computer processing by increasing the speed and reducing the cost of blockchain transactions.

HyperCycle’s efforts to improve access to information precede Google’s announcement. In January 2025, the platform announced it had launched a joint initiative with YMCA – an AI app called Hyper-Y that will connect 64 million people in 12,000 YMCA locations across 120 countries, providing staff, members, and volunteers with access to information from the global network.

HyperCycle’s efforts and Google’s A2A converge

Google hopes its protocol will pave the way for collaboration to solve complex problems and will build the protocol with the community, in the open. A2A was released as open-source with plans to set up contribution pathways. HyperCycle’s innovations aim to enable collaborative problem-solving by connecting AI to a global network of specialised abilities as A2A standardises communication between agents regardless of their vendor or build, so introducing more collaborative multi-agent ecosystems.

A2A and Hypercycle bring ease of use, modularity, scalability, and security to AI agent systems. They can unlock a new era of agent interoperability, creating more flexible and powerful agentic systems.

(Image source: Unsplash)

The post Google launches A2A as HyperCycle advances AI agent interoperability appeared first on AI News.

If the performance claims prove accurate, the AI hardware breakthrough might reshape the technology landscape at a time when AI development is continuing worldwide, and despite US efforts to limit China’s access to advanced technology.

300 petaflops: Challenging Nvidia’s hardware dominance

The CloudMatrix 384 Supernode is described as a “nuclear-level product,” according to reports from STAR Market Daily cited by the South China Morning Post (SCMP). The hardware achieves an impressive 300 petaflops of computing power, in excess of the 180 petaflops delivered by Nvidia’s NVL72 system.

The CloudMatrix 384 Supernode was specifically engineered to address the computing bottlenecks that have become increasingly problematic as artificial intelligence models continue to grow in size and complexity.

The system is designed to compete directly with Nvidia’s offerings, which have dominated the global market for AI accelerator hardware thus far. Huawei’s CloudMatrix infrastructure was first unveiled in September 2024, and was developed specifically to meet surging demand in China’s domestic market.

The 384 Supernode variant represents the most powerful implementation of AI architecture to date, with reports indicating it can achieve a throughput of 1,920 tokens per second and maintain high levels of accuracy, reportedly matching the performance of Nvidia’s H100 chips, but using Chinese-made components instead.

Developing under sanctions: The technical achievement

What makes the AI hardware breakthrough particularly significant is that it has been achieved despite the severe technological restrictions Huawei has faced since being placed on the US Entity List.

Sanctions have limited the company’s access to advanced US semiconductor technology and design software, forcing Huawei to develop alternative approaches and rely on domestic supply chains.

The core technological advancement enabling the CloudMatrix 384’s performance appears to be Huawei’s answer to Nvidia’s NVLink – a high-speed interconnect technology that allows multiple GPUs to communicate efficiently.

Nvidia’s NVL72 system, released in March 2024, features a 72-GPU NVLink domain that functions as a single, powerful GPU, enabling real-time inference for trillion-parameter models at speeds 30 times faster than previous generations.

According to reporting from the SCMP, Huawei is collaborating with Chinese AI infrastructure startup SiliconFlow to implement the CloudMatrix 384 Supernode in supporting DeepSeek-R1, a reasoning model from Hangzhou-based DeepSeek.

Supernodes are AI infrastructure architectures equipped with more resources than standard systems – including enhanced central processing units, neural processing units, network bandwidth, storage, and memory.

The configuration allows them to function as relay servers, enhancing the overall computing performance of clusters and significantly accelerating the training of foundational AI models.

Beyond Huawei: China’s broader AI infrastructure push

The AI hardware breakthrough from Huawei doesn’t exist in isolation but rather represents part of a broader push by Chinese technology companies to build domestic AI computing infrastructure.

In February, e-commerce giant Alibaba Group announced a massive 380 billion yuan ($52.4 billion) investment in computing resources and AI infrastructure over three years – the largest-ever investment by a private Chinese company in a computing project.

For the global AI community, the emergence of viable alternatives to Nvidia’s hardware could eventually address the computing bottlenecks that have limited AI advancement. Competition in this space could potentially increase available computing capacity and provide developers with more options for training and deploying their models.

However, it’s worth noting that as of the report’s publication, Huawei had not yet responded to requests for comment on these claims.

As tensions between the US and China continue to intensify in the technology sector, Huawei’s CloudMatrix 384 Supernode represents a significant development in China’s pursuit of technological self-sufficiency.

If the performance claims are verified, this AI hardware breakthrough would mean Huawei has achieved computing independence in this niche, despite facing extensive sanctions.

The development also signals a broader trend in China’s technology sector, with multiple domestic companies intensifying their investments in AI infrastructure to capitalise on growing demand and promote the adoption of homegrown chips.

The collective effort suggests China is committed to developing domestic alternatives to American technology in this strategically important field..

See also: Manus AI agent: breakthrough in China’s agentic AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Huawei’s AI hardware breakthrough challenges Nvidia’s dominance appeared first on AI News.

Machines Can See is staged by the Polynome Group, a machine vision, AI, robotic, and industrial design company based in the city.

This is the third year of the event, and will bring investors, business leaders, and policymakers together to explore AI-centric expansion opportunities. Machines Can See, as the name suggests, will have a particular focus on computer vision.

Each discussion and keynote is designed to be firmly rooted in practical applications of AI technology, but organisers hope that the show will be permeated with a sense of discovery and that attendees will be able to explore the possibilities of the tech on show. “We are not just shaping the future of AI, we are defining how AI shapes the world,” said Alexander Khanin, head of the Polynome Group.

UAE Government officials attending the event include H.E. Omar Sultan Al Olama, UAE Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications, and H.E. Hamad Obaid Al Mansoori, the Director General of Digital Dubai.

Polynome Group has said that X will be the official streaming partner for Machines Can See 2025, and the US company will host workshops titled “X and AI” to show solutions that merge AI and streaming technologies, with GRok X central to those sessions. Via interactive demos, attendees will gain firsthand experience of GRok’s potential in AI delivery, analysis and optimisation.

Investment and business opportunities

UAE’s AI market is projected to grow by $8.4 billion in the next two years, and the summit is designed to serve as a venue for investors to engage with AI startups, existing enterprises, and government decision-makers. Attendees at Machines Can See will get to meet with investors and venture capital firms, be given the opportunity to meet executives from AI companies (including IBM and Amazon), and connect with startups seeking investment.

The summit is supported by Amazon Prime Video & Studios, Amazon Web Services, Dubai Police, MBZUAI, IBM, SAP, Adia Lab, QuantumBlack and Yango. The involvement of many organisations and large-scale enterprises should provide many opportunities for funding and collaborations that extend the commercial use of AI.

Local and international investors include Eddy Farhat, Executive Director at e& capital, Faris Al Mazrui, Head of Growth Investments at Mubadala, Major General Khalid Nasser Alrazooqi General Director of Artificial Intelligence, Dubai Police UEA, and Dr. Najwa Aaraj, the CEO of TII.

Speakers and insights

The summit will feature several US-based AI professionals, including Namik Hrle, IBM Fellow and Vice President of Development at the IBM Software Group, Michael Bronstein, DeepMind Professor of AI at Oxford University, Marc Pollefeys, Professor of Computer Science at ETH Zurich, Gerard Medioni, VP and Distinguished Scientist at Amazon Prime Video & Studio, and Deva Ramanan, Professor at the Robotics Institute of Carnegie Mellon University.

The event will feature a ministerial session composed of international government representatives to discuss the role of national IT development.

Among speakers already confirmed for the event are Gobind Singh Deo, Malaysia’s Minister of Digital, H.E. Zhaslan Madiyev, Minister of Digital Development, Innovation, and Aerospace Industry of Kazakhstan, and H.E. Omar Sultan Al Olama, UAE Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications.

Event organisers expect to announce more representatives from overseas in the coming days. Read more here.

The post Machines Can See 2025 – Dubai AI event appeared first on AI News.

The craze began picking up momentum after the viral Studio Ghibli-style portraits sees users sharing images of themselves as boxed dolls – complete with accessories and job-themed packaging.

There are several variations in the latest wave of AI-generated self-representation. The most common format is similar to a traditional action figure or Barbie doll, with props like coffee mugs, books, and laptops reflecting users’ professional lives. The images are designed to resemble toy store displays, complete with bold taglines and personalised packaging.

The movement gained initial attention on LinkedIn, where professionals used the format to showcase their brand identities more playfully. The “AI Action Figure” format, in particular, resonated with marketers, consultants, and others looking to present themselves as standout figures – literally. Popularity of the service has since trickled into other platforms including Instagram, TikTok, and Facebook, though engagement remains largely centred around LinkedIn.

ChatGPT’s image tool – part of its GPT-4o release – serves as the engine. Users upload a high-resolution photo of themselves, usually full-body, with a custom prompt describing how the final image should look. Details frequently include the person’s name, accessories, outfit styles, and package details. Some opt for a nostalgic “Barbiecore” vibe with pink tones and sparkles, while others stick to a corporate design that reflects their day job.

Refinements are common. Many users go through multiple image generations, changing accessories and rewording prompts until the figure matches their wanted personality or profession. The result is a glossy, toy-style portrait that crosses the line between humour and personal branding.

While the toy-style trend hasn’t seen the same viral reach as the Ghibli portrait craze, it has still sparked a steady flow of content across platforms. Hashtags like #AIBarbie and #BarbieBoxChallenge have gained traction, and some brands – including Mac Cosmetics and NYX – were quick to participate. A few public figures have joined in too, most notably US Representative Marjorie Taylor Greene, who shared a doll version of herself featuring accessories like a Bible and gavel.

Regardless of the buzz, engagement levels are different. Many posts receive limited interaction, and most well-known influencers have avoided the trend. Nevertheless, it highlights ChatGPT’s growing presence in mainstream online culture, and its ability to respond to users’ creativity using relatively simple tools.

The is not the first time ChatGPT’s image generation tool has overwhelmed the platform. When the Ghibli-style portraits first went viral, demand spiked so dramatically that OpenAI temporarily limited image generation for free accounts. CEO Sam Altman later described the surge in users as “biblical demand,” noting a dramatic rise in daily active users and infrastructure stress.

The Barbie/action figure trend, though at a smaller scale, follows that same path – using ChatGPT’s simple interface and its growing popularity as a creative tool. As with other viral AI visuals, the trend has also raised broader conversations about identity, aesthetics, and self-presentation in digital spaces. However, unlike the Ghibli portrait craze, it hasn’t attracted much criticism – at least not yet.

The format’s appeal lies in its simplicity. It offers users a way to engage with AI-generated art without needing technical skills, and satisfies an urge for of self-expression. The result is something like part professional head-shot, part novelty toy, and part visual joke, making it a surprisingly versatile format for social media sharing.

While some may see the toy model phenomenon as a gimmick, others view it as a window into what’s possible when AI tools are placed directly in users’ hands.

For now, whether it’s a mini-me holding a coffee mug or a Barbie-style figure ready for the toy shelf, ChatGPT is again changing how people choose to represent themselves in the digital age.

(Photo by Unsplash)

See also: ChatGPT hits record usage after viral Ghibli feature – Here are four risks to know first

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post ChatGPT got another viral moment with ‘AI action figure’ trend appeared first on AI News.

What are digital twins?

Digital twins, originally developed to aid in the design of complex machinery have evolved significantly over the last two decades. They track and analyse live systems in real-time by processing device telemetry, detecting shifting conditions, and enhancing situational awareness for operational managers. Powered by in-memory computing, they enable fast, actionable alerts. Beyond real-time monitoring, digital twins also can simulate intricate systems like those for use in airlines and logistics, supporting strategic planning and operational decisions through predictive analytics.

Integrating digital twins with generative AI creates new opportunities for both technologies: The synergy can boost the prediction accuracy of generative AI, and can enhance the value of digital twins for system monitoring and development.

Proactively identifying anomalies with AI-powered digital twins

Continuous, real-time monitoring is a strategic necessity for organisations that manage complex live systems, like transportation networks, cybersecurity systems, and smart cities. Emerging problems must never be overlooked because delayed responses can cause small problems to become large ones.

Enhancing digital twins with generative AI reshapes how real-time monitoring interprets massive volumes of live data, enabling the reliable and immediate detection of anomalies that impact operations. Generative AI can continuously examine analytics results produced by digital twins to uncover emerging trends and mitigate disruptions before they escalate. While AI enhances situational awareness for managers, it can also pinpoint new opportunities for optimising operations and boosting efficiency.

At the same time, real-time data supplied by digital twins constrains the output of generative AI to avoid erratic results, like hallucinations. In a process called retrieval augmented generation, AI always uses the most up-to-date information about a live system to analyse behaviour and create recommendations.

Transforming data interaction with AI-driven visualisations

Unlocking insights from digital twin analytics should be intuitive, not technical. Generative AI is redefining how teams interact with massive datasets by enabling natural language-driven queries and visualisations. Instead of manually constructing intricate queries, users can simply describe their needs, and generative AI immediately visualises relevant charts and query results that provide new insights. This capability simplifies interactions and gives decision-makers the data they need. As organisations handle increasingly complex live systems, AI-powered intelligence allows them to efficiently sift through vast data pools, extract meaningful trends, and optimise operations with greater precision. It eliminates technical barriers, enabling faster, data-driven decisions that have a strategic impact.

Incorporating machine learning with automatic retraining

Digital twins can track numerous individual data streams and look for issues with the corresponding physical data sources. Working together, thousands or even millions of digital twins can monitor very large, complex systems. As messages flow in, each digital twin combines them with known information about a particular data source and analyses the data in a few milliseconds. It can incorporate a machine learning algorithm to assist in the analysis and find subtle issues that would be difficult to describe in hand-coded algorithms. After training with data from live operations, ML algorithms can identify anomalies and generate alerts for operational managers immediately.

Once deployed to analyse live telemetry, an ML algorithm will likely encounter new situations not covered by its initial training set. It may either fail to detect anomalies or generate false positives. Automatic retraining lets the algorithm learn as it gains experience so it can improve its performance and adapt to changing conditions. Digital twins can work together to detect invalid ML responses and build new training sets that feed automatic retraining. By incorporating automatic retraining, businesses gain a competitive edge with real-time monitoring that reliably delivers actionable insights as it learns over time.

Looking forward

Integrating digital twin technology with generative AI and ML can transform how industries monitor complex, live systems by empowering better real-time insights and enabling managers to make faster, more informed decisions. ScaleOut Software’s newly-released Digital Twins Version 4 adds generative AI using OpenAI’s large language model and automatic ML retraining to move real-time monitoring towards the goal of fully-autonomous operations. 

(Image source: Unsplash)

The post Transforming real-time monitoring with AI-enhanced digital twins appeared first on AI News.

As one of the first authors to publish a book on generative AI, she has emerged as a sought-after speaker helping global leaders, businesses, and institutions understand and adapt to this transformative moment.

We spoke to Nina to explore the future of AI-driven innovation, its ethical and political dimensions, and how organisations can lead in this rapidly evolving landscape.

In your view, how will generative AI redefine the foundational structures of business and economic productivity in the coming decade?

I believe generative AI is absolutely going to transform the entire economy as we know it. This moment feels quite similar to around 1993, when we were first being told to prepare for the Internet. Back then, some thirty years ago, we didn’t fully grasp, in our naivety, how profoundly the Internet would go on to reshape business and the broader global economy.

Now, we are witnessing something even more significant. You can think of generative AI as a kind of new combustion engine, but for all forms of human creative and intelligent activity. It’s a fundamental enabler. Every industry, every facet of productivity, will be impacted and ultimately transformed by generative AI. We’re already beginning to see those use cases emerge, and this is only the beginning.

As AI and data continue to evolve as forces shaping society, how do you see them redefining the political agenda and global power dynamics?

When you reflect on just how profound AI is in its capacity to reshape the entire framework of society, it becomes clear that this AI revolution is going to emerge as one of the most important political questions of our generation. Over the past 30 years, we’ve already seen how the information revolution — driven by the Internet, smartphones, and cloud computing — has become a defining geopolitical force.

Now, we’re layering the AI revolution on top of that, along with the data that fuels it, and the impact is nothing short of seismic. This will evolve into one of the most pressing and influential issues society must address over the coming decades. So, to answer the question directly — AI won’t just influence politics; it will, in many ways, become the very fabric of politics itself.

There’s been much discussion about the Metaverse and immersive tech — how do you see these experiences evolving, and what role do you believe AI will play in architecting this next frontier of digital interaction?

The Metaverse represents a vision for where the Internet may be heading — a future where digital experiences become far more immersive, intuitive, and experiential. It’s a concept that imagines how we might engage with digital content in a far more lifelike way.

But the really fascinating element here is that artificial intelligence is the key enabler — the actual vehicle — that will allow us to build and scale these kinds of immersive digital environments. So, even though the Metaverse remains largely an untested concept in terms of its final form, what is clear right now is that AI is going to be the engine that generates and populates the content that will live within these immersive spaces.

Considering the transformative power of AI and big data, what ethical imperatives must policymakers and society address to ensure equitable and responsible deployment?

The conversation around ethics, artificial intelligence, and big data is one that is set to become intensely political and highly consequential. It will likely remain a predominant issue for many years to come.

What we’re dealing with here is a technology so transformative that it has the potential to reshape the economy, redefine the labour market, and fundamentally alter the structure of society itself. That’s why the ethical questions — how to ensure this technology is applied in a fair, safe, and responsible manner — will be one of the defining political challenges of our time.

For business leaders navigating digital transformation, what mindset shifts are essential to meaningfully integrate AI into long-term strategy and operations?

For businesses aiming to digitally transform, especially in the era of artificial intelligence, it’s critical to first understand the conceptual paradigm shift we are currently undergoing. Once that foundational understanding is in place, it becomes much easier to explore and adopt AI technologies effectively.

If companies wish to remain competitive and gain a strategic edge, now is the time to start investigating how generative AI can be thoughtfully and effectively integrated into their business models. This includes identifying priority areas where AI can deliver long-term value — not just short-term.

If you put together a generative AI working group to look into this, your business will be transformed and able to compete with other businesses that are using AI to transform their processes.

As one of the earliest voices to articulate the societal implications of generative AI, what catalysed your foresight to explore this space before it entered the mainstream conversation?

My interest in AI didn’t come from a technical background. I’m not a techie. My experience has always been in analysing macro trends that shape society, geopolitics, and the wider world. That perspective is what led me to AI, as it quickly became clear that this technology would have far-reaching societal implications.

I began researching and writing about AI because I saw it as more than just a technological shift. Ultimately, this isn’t only a story about innovation. It’s a story about humanity. Generative AI, as an exponential technology built and directed by humans, is going to transform not just the way we work, but the way we live. It will even challenge our understanding of what it means to be human.

Photo by Heidi Fin on Unsplash

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

The post Nina Schick, author: Generative AI’s impact on business, politics and society appeared first on AI News.

But forget about 2033: in the here and now, AI is already fueling transformation in industries as diverse as financial services, manufacturing, healthcare, marketing, agriculture, and e-commerce. Whether it’s autonomous algorithmic ‘agents’ managing your investment portfolio or AI diagnostics systems detecting diseases early, AI is fundamentally changing how we live and work.

But cynicism is snowballing around AI – we’ve seen Terminator 2 enough times to be extremely wary. The question worth asking, then, is how do we ensure trust as AI integrates deeper into our everyday lives?

The stakes are high: A recent report by Camunda highlights an inconvenient truth: most organisations (84%) attribute regulatory compliance issues to a lack of transparency in AI applications. If companies can’t view algorithms – or worse, if the algorithms are hiding something – users are left completely in the dark. Add the factors of systemic bias, untested systems, and a patchwork of regulations and you have a recipe for mistrust on a large scale.

Transparency: Opening the AI black box

For all their impressive capabilities, AI algorithms are often opaque, leaving users ignorant of how decisions are reached. Is that AI-powered loan request being denied because of your credit score – or due to an undisclosed company bias? Without transparency, AI can pursue its owner’s goals, or that of its owner, while the user remains unaware, still believing it’s doing their bidding.

One promising solution would be to put the processes on the blockchain, making algorithms verifiable and auditable by anyone. This is where Web3 tech comes in. We’re already seeing startups explore the possibilities. Space and Time (SxT), an outfit backed by Microsoft, offers tamper-proof data feeds consisting of a verifiable compute layer, so SxT can ensure that the information on which AI relies is real, accurate, and untainted by a single entity.

Space and Time’s novel Proof of SQL prover guarantees queries are computed accurately against untampered data, proving computations in blockchain histories and being able to do so much faster than state-of-the art zkVMs and coprocessors. In essence, SxT helps establish trust in AI’s inputs without dependence on a centralised power.

Proving AI can be trusted

Trust isn’t a one-and-done deal; it’s earned over time, analogous to a restaurant maintaining standards to retain its Michelin star. AI systems must be assessed continually for performance and safety, especially in high-stakes domains like healthcare or autonomous driving. A second-rate AI prescribing the wrong medicines or hitting a pedestrian is more than a glitch, it’s a catastrophe.

This is the beauty of open-source models and on-chain verification via using immutable ledgers, with built-in privacy protections assured by the use of cryptography like Zero-Knowledge Proofs (ZKPs). Trust isn’t the only consideration, however: Users must know what AI can and can’t do, to set their expectations realistically. If a user believes AI is infallible, they’re more likely to trust flawed output.

To date, the AI education narrative has centred on its dangers. From now on, we should try to improve users’ knowledge of AI’s capabilities and limitations, better to ensure users are empowered not exploited.

Compliance and accountability

As with cryptocurrency, the word compliance comes often when discussing AI. AI doesn’t get a pass under the law and various regulations. How should a faceless algorithm be held accountable? The answer may lie in the modular blockchain protocol Cartesi, which ensures AI inference happens on-chain.

Cartesi’s virtual machine lets developers run standard AI libraries – like TensorFlow, PyTorch, and Llama.cpp – in a decentralised execution environment, making it suitable for on-chain AI development. In other words, a blend of blockchain transparency and computational AI.

Trust through decentralisation

The UN’s recent Technology and Innovation Report shows that while AI promises prosperity and innovation, its development risks “deepening global divides.” Decentralisation could be the answer, one that helps AI scale and instils trust in what’s under the hood.

(Image source: Unsplash)

The post Web3 tech helps instil confidence and trust in AI appeared first on AI News.

What’s more intriguing is the bounce back of Nvidia, now the world’s leading chipmaker. Its stock price has surged by 1635% in the past five years, placing it third in terms of value in market capitalisation, slightly behind Apple and Microsoft.

But with AI running hot over the past two years, most investors are questioning how far the AI narrative can extend. Are we close to an extended pullback or are there some fundamentals to keep the AI narrative alive?

While we may witness a period of slowed growth in the medium term, there are several reasons why the AI trend will likely continue much longer. The next section of this article will highlight four key signals that point to this likelihood:

High adoption rate of new AI tech

ChatGPT set the record for the fastest growing user base in the tech sector, eclipsing the 100 million user mark two months after its launch data. Today, there are over 400 million ChatGPT users weekly.

ChatGPT’s latest image generation feature has witnessed massive demand, with users coming to the platform to create Ghibli-style AI: “It’s super fun seeing people love images in ChatGPT, but our GPUs are melting,” said Altman in an X post.

While there have been criticisms about copyright issues, what’s worth noting is the rate at which the world is experimenting with AI innovations. Gone are the days when AI was a niche carved out for tech bros; AI is mainstream.

Advances in digital human innovations

Imagine a digital doppelgänger that can interact with other internet users on your behalf at the same level of personalisation you would. This has long been a dream for many AI enthusiasts and it may finally be becoming a reality.

It is possible to create a hyper-realistic and intelligent digital twin through an AI-powered SaaS platform like Antix. Unlike conventional avatars which lack human qualities, Antix’s digital humans offer a new level of digital interaction. They are designed to deliver realistic interactions, letting users engage in more meaningful and profound ways.

This advance in digital human innovation may transform today’s digital landscape. For the first time, it will be possible for internet users anywhere to create or acquire a digital twin that can evolve or hold conversations according to a user’s preferences and personality. For example, Antix’s digital humans give the flexibility to customise key human attributes, including style, appearance, emotions and voice.

Increased funding in the AI space

According to Statista, global funding in AI startups hit a new high of $100 billion in 2024. The trend will likely continue in 2025 as more countries allocate funding to AI innovations. Here are two key developments this year:

• President Trump recently announced a $500 billion private sector investment in funding AI infrastructure. The goal is to make the US a leading contender in the AI race.

• China has allocated $8.2 billion into a new AI fund following a move by the US to go ahead with additional chip export restrictions, targeting Chinese firms.

Accelerated global AI race

A global AI race has been brewing between the US and China. While it may seem like negative engagement, a competitive ecosystem is exactly what the AI space needs for growth.

For example, China’s DeepSeek R1 model was developed at only $6 million, a fraction of the cost to train models like Google’s Gemini and ChatGPT. While some critics have argued it couldn’t have cost that little, the Chinese model has challenged its Western counterparts to be leaner in their AI development processes.

We’re also seeing the likes of Open AI stepping up to roll out more advanced features such as the image generator currently making waves.

Conclusion

AI is one of the four industrial technologies that have gained traction recently. While there are a number of issues yet to be ironed out, including ethical concerns, it is evident that the AI narrative will be a while in fading away. The four factors above are a glimpse of what might sustain this narrative. As of writing, there are more AI innovations than most of us or regulators can keep up with. That alone should be a signal that the nascent industry is just getting started.

The post 4 signals AI will continue to be a narrative in 2025 appeared first on AI News.

While a lot of emerging technologies are playing a role in the evolution of the finance industry, the AI revolution is one of the most prominent. With that in mind, let us look at the ways AI is transforming financial platforms, starting with a brief overview of the role AI plays in financial services.

Understanding AI in financial services

AI is changing the landscape of the financial services sector, as in other industries. The integration of AI in the financial services industry extends to changes in how companies operate, customer interactions, agentic AI, and risk management.

The three core AI-related technologies that play an important role in the finance sector, are:

• Natural language processing (NLP): The NLP aspect of AI helps companies understand and interpret human language, and is used for sentiment analysis or customer service automation through chatbots.

• Machine learning (ML): AI can let financial systems learn from past data and improve performance with minimal human intervention. ML algorithms can analyse large data volumes and make important predictions about investment opportunities and market trends.

• Predictive analytics: Businesses can use machine learning techniques and AI algorithms to identify the likelihood of certain outcomes based on historical data. Companies can use predictive analytics for better accuracy in fraud detection or risk assessment.

It also helps that AI has already been adopted to a certain extent in the financial sector. Around 70% of financial institutions and companies currently invest in AI technologies, according to a 2024 report by Gartner. Moreover, around 58% of finance functions use AI in some capacity.

AI-integrated strategies in finance

For AI integration in the finance sector to be truly successful and unlock the untapped potential in a company, strategies using the technology must be well-defined. With robust strategies, finance companies, and service providers can ensure AI prepares them for a more profitable future.

Among other areas, three important areas of or strategies for financial services that currently use AI at a much bigger scale, are:

Risk management

While risk management is an essential business function in many companies and industries, it is especially important for financial institutions. With the help of advanced algorithms and data analytics, financial organisations can take a proactive approach to identifying, assessing, and mitigating risks. As a result, you can avoid issues like leakage of revenue or loss of important data.

AI models can help with credit risk assessment of businesses and individuals by analysing large datasets. Financial companies can also use AI-based systems to monitor transactions in real-time and identify unusual patterns pointing to fraudulent activity. Additionally, financial analysts use AI to conduct market risk analysis and predict market volatility by processing extensive market data.

Compliance and regulatory monitoring

As the financial industry faces increasing regulatory scrutiny, organisations have to invest and implement robust strategies for compliance management. AI systems can help organisations automate the checking of transactions for compliance with anti-money laundering laws, and flag down suspicious activity.

Many financial service providers are developing AI-driven risk assessment frameworks that help them identify and prevent compliance risks. Plus, they also use AI to streamline reporting processes to ensure timely submission of regulatory documents and the generation of compliance reports. Lastly, processes must align with the necessary AI regulations.

Personalisation of communication and products/services

AI can also help financial organisations provide highly-personalised services to customers by analysing their preferences and requirements. By using data analytics, banks and financial organisations can provide tailored financial products that meet their specific needs. AI-powered chatbots and virtual assistants help customers get instant support and answers to queries in real-time.

Financial companies should conduct continuous and consistent analysis of transactions and customer interactions to identify robust trends and deliver targeted and highly relevant marketing and promotional messages to customers.

AI-powered tools on financial platforms

The aforementioned strategies help financial companies provide unique and high-quality services to customers. Most financial platforms offer different kinds of AI-powered tools that add several value-adding features and abilities.

Here are some of the AI-powered financial tools to know about:

AI chatbots and virtual assistants

The quality of customer service is important to the success of any financial institution or organisation. Most financial companies use AI-powered chatbots and virtual assistants to provide excellent service to customers. Chatbots can ensure timely communication, helping companies humanise AI responses, and resolve queries for customers.

Enterprise AI agents

For larger financial organisations that offer multiple services, products or operate in many locations, an enterprise management strategy is a must. A lot of companies implement enterprise AI agent platforms that help automate repetitive actions and tasks when an event or feature is triggered.

Fraud detection system

Most financial platforms use a fraud detection system to monitor transactions in real-time and flag any suspicious instances to combat fraud. The systems also help companies monitor market conditions and user behaviour to detect any unusual patterns immediately.

Data mining tools

Most financial platforms handle large volumes of financial data that can be analysed and monitored to generate valuable insights. Data mining tools can help navigate this situation by extracting insights from large data volumes with the help of machine learning algorithms. It is possible to identify patterns and trends to inform strategic and financial decisions.

Automated trading systems

AI-powered automated trading systems help companies execute trades based on predetermined criteria. Automated trading systems aid financial organisations enhance efficiency in trades and react to market changes faster than humans.

The future of AI in financial systems and services

As the financial services industry evolves, so do the role and applications of AI in the industry. Companies should keep track of emerging trends to steer the success of financial service provision.

When integrating AI technologies into financial processes, it is important for companies to choose the right platforms to ensure smooth and efficient implementation. This brings us to a comparison of Sitecore vs. WordPress – two web platforms popular in the financial services space.

While Sitecore offers a highly personalised experience for customers, making it ideal for large financial institutions with complex needs, WordPress provides an affordable and scalable solution for smaller institutions or those just beginning their AI integration journey. Understanding the strengths and limitations of each platform can help financial organisations choose the best way to adopt AI solutions.

Some industry solutions include personalised financial services tailored to the preferences and risk appetite of customers, and decentralised finance solutions that could automate lending, borrowing and trading decisions effectively.

Many financial companies are looking to implement advanced risk management tools that use AI to assess risks and predict market disruptions proactively.

The integration of AI in financial processes may be slow but it is inexorable, making it important for companies to consider implementing the technology sooner rather than later. With effective AI integration, financial companies can enjoy better operational efficiency and enhanced customer experience in the long-term.

Conclusion

The role of AI in the financial industry has been discussed and debated for some time. While most financial applications and platforms use AI to strengthen or automate certain processes, others use it to add new functions and features to the existing platform.

(Image source: Unsplash)

The post How AI transforms financial platforms: Tools and strategies appeared first on AI News.