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Visualize AI: Solve Challenges and Exploit Opportunities - ValueWalk

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Every day, new organizations announce how AI is revolutionizing the industry with disruptive results . As more and more business decisions are based on AI and advanced data analytics it is critical to provide transparency to the inner workings within that technology. McKinsey Global InstituteHarvard Business Review According to a recent McKinsey Global Institute analysis, the financial services sector is a leading adopter of AI and has the most ambitious AI investment plans. In a related article by the Harvard Business Review, adoption will center on AI technologies like neural-based machine learning and natural language processing because those are the technologies that are beginning to mature and prove their value. Below, we explore a challenge and opportunity that is unique to the rapid adoption of machine learning.


How ML Model Explainability Accelerates the AI Adoption Journey for Financial Services - KDnuggets

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Financial services firms are increasingly employing artificial intelligence to better not just their operational operations, but also business-related tasks, including assigning credit scores, identifying fraud, optimizing investment portfolios, and supporting innovations. AI improves the speed, precision, and efficacy of human efforts in these operations, and it can automate data management chores that are currently done manually. However, as AI advances, new challenges arise. The real issue is transparency: when individuals don't comprehend or only a few people understand the reasoning behind AI models, AI algorithms may inadvertently bake in bias or fail. This has accelerated the need for explainability in ML models across industries.


FICO Announces Winners of Inaugural xML Challenge

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FICO, the leading provider of analytics and decision management technology, together with Google and academics at UC Berkeley, Oxford, Imperial, UC Irvine and MIT, have announced the winners of the first xML Challenge at the 2018 NeurIPS workshop on Challenges and Opportunities for AI in Financial Services. Participants were challenged to create machine learning models with both high accuracy and explainability using a real-world dataset provided by FICO. Sanjeeb Dash, Oktay Gu nlu k and Dennis Wei, representing IBM Research, were this year's challenge winners. The winning team received the highest score in an empirical evaluation method that considered how useful explanations are for a data scientist with the domain knowledge in the absence of model prediction, as well as how long it takes for such a data scientist to go through the explanations. For their achievements, the IBM team earned a $5,000 prize.


Juppiter AI Labs

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We'll assist you in choosing the suitable technologies as per your project, set up the exemplary architecture, and leverage emerging tools and trends without negating the idea and vision behind the product development. Juppiter AI Labs is one of the most trusted and reliable software development companies that offer outstanding and efficient business-driven solutions to small, medium-sized, and financial services industries. The company clutches on the latest technologies for innovative solutions.AI labs promises guaranteed product delivery and transparency at each step in the development of the product. Juppiter AI Labs is an IT solutions provider and proven expert in providing skilled consultants to meet any business need. We specialize in custom software development, cloud computing, mobile application development, artificial intelligence solutions, machine learning, IT project support services and emerging technology development.


Adversarial machine learning explained: How attackers disrupt AI and ML systems

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As more companies roll out artificial intelligence (AI) and machine learning (ML) projects, securing them becomes more important. A report released by IBM and Morning Consult in May stated that of more than 7,500 global businesses, 35% of companies are already using AI, up 13% from last year, while another 42% are exploring it. However, almost 20% of companies say that they were having difficulties securing data and that it is slowing down AI adoption. In a survey conducted last spring by Gartner, security concerns were a top obstacle to adopting AI, tied for first place with the complexity of integrating AI solutions into existing infrastructure. According to a paper Microsoft released last spring, 90% of organizations aren't ready to defend themselves against adversarial machine learning.


State of AI in Financial Services

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Recently, Nvidia released a new report called the State of AI in Financial Services. To learn more, I caught up with Pahal Patangia, Global Developer Relations Lead for Consumer Fintech at Nvidia. Below is the transcript of our conversation (slightly edited for clarity). Theodora: Now, I know oftentimes when we think about Nvidia, we think about graphics cards. Nvidia is also a full stack, accelerated computing platform company that has been in the financial services space for 15 years.


Senior Machine Learning Scientist

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Apex Fintech Solutions (AFS) powers innovation and the future of digital wealth management by processing millions of transactions daily, to simplify, automate, and facilitate access to financial markets for all. Our robust suite of fintech solutions enables us to support clients such as Stash, Betterment, SoFi, and WeBull, and more than 20 million of our clients' customers. Collectively, AFS creates an environment in which companies with the biggest ideas in fintech are empowered to change the world. We are based in Dallas, TX and also have offices in Austin, New York, Chicago, Los Angeles, Portland, and Belfast. If you are seeking a fast-paced and entrepreneurial environment where you'll have the opportunity to make an immediate impact, and you have the guts to change everything, this is the place for you.


Radicant And Squirro To Add More Muscle To Fintech With New AI Commitments

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Radicant, the first sustainable, digital and collaborative internet-based financial services company which is aligned with the UN's 17 Sustainable Development Goals, is working closely with Squirro. The integration of technology enables radicant to automate decision-making processes and improve customer understanding and service quality. The Swiss tech company Squirro, recognized as a "Visionary" in Gartner Magic Quadrant for Insight Engines, links and analyzes data to provide new insights as a basis for decision-making. So far, this artificial intelligence (AI)-based technology has been made available to decision-makers in companies. In collaboration with radicant, these insights and recommendations are now offered not only within the company but also to radicant's customers.


Global Big Data Conference

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Nearly two years after a global pandemic sent most banking customers online, the majority of financial institutions appear to be embracing digital transformation. But many still have a long way to go. For example, a recent survey of mid-sized U.S. financial institutions by Cornerstone Advisors found that 90% of respondents have launched, or are in the process of developing, a digital transformation strategy--but only 36% said they are halfway through. I believe that one of the reasons behind the lag in uptake is many banks' new reluctance to use artificial intelligence (AI) and machine learning technologies. The responsible application of explainable, ethical AI and machine learning is critical in analyzing and ultimately monetizing the manifold customer data that is a byproduct of any institution's effective digital transformation.


La veille de la cybersécurité

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It's simple: In financial services, customer data offers the most relevant services and advice. But, oftentimes, people use different financial institutions based on their needs – their mortgage with one; their credit card with another; their investments, savings and checking accounts with yet another. And in the financial industry more so than others, institutions are notoriously siloed. Largely because the industry is so competitive and highly regulated, there hasn't been much incentive for institutions to share data, collaborate or cooperate in an ecosystem. Customer data is deterministic (that is, relying on first-person sources), so with customers "living across multiple parties," financial institutions aren't able to form a precise picture of their needs, said Chintan Mehta, CIO and head of digital technology and innovation at Wells Fargo.