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Putting the (artificial) intelligence back into banking

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Financial services and technology vendors make for uneasy bedfellows. While tech has formed banking's bedrock since the Big Bang deregulation of the 1980s, in the last decade financial services (FS) organisations have seen the new "masters of the universe" steadily – almost stealthily – encroach on their patch. Established tech vendors and new start-ups have introduced a range of financial services from money transfer apps to mobile payments, crowdfunding to share trading and investments. These new services are perfectly suited to a generation who have grown up with smartphones and expect instant access to digital services, combined simplicity and a great user experience. While over the last few years there has been an exponential increase in the structured data that is collected and used, the inclusion of unstructured data sets, pictures, images and videos along with structured data has been increasingly important in driving both strategic and operational business decisions.


Rabobank Australia and New Zealand Inks Deal with nCino

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This partnership will benefit the bank's Australian and New Zealand employees and customers, representing a multi-currency, cross-country commitment to provide a better banking experience. "By partnering with nCino, we will optimise our financial spreading analysis," said Alexa Glynn, Chief Operating Officer at RANZ. "This relationship will provide an excellent opportunity for RANZ to support our growing customer base and modernise our systems. We're delighted that nCino's technology will enable us to offer our customers and employees a better banking experience." The world's leading specialist food and agribusiness bank, Rabobank is one of Australia and New Zealand's largest agricultural lenders and a major provider of business and corporate banking services to the country's food and agribusiness sector. By adopting the nCino Bank Operating System, RANZ gains a digital solution that intelligently transforms the process of spreading financials by leveraging machine learning and optical character recognition (OCR).


Distinguished Machine Learning Engineer- Remote Eligible - Remote Tech Jobs

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Locations: VA – Richmond, United States of America, Richmond, Virginia Distinguished Machine Learning Engineer- Remote Eligible Distinguished Machine Learning Engineer (Director-level) At Capital One, we believe that machine learning represents the biggest opportunity in financial services today, and is a chance to revolutionize the industry. Capital One's commitment to machine learning has sponsorship from the CEO, the Board of Directors, and the executive committee of the company. The Center for Machine Learning is at the heart of this effort, and is leading the way towards building responsible and impactful tools, platforms, and solutions that leverage ML. As a Capital One Machine Learning Engineer, you'll be providing technical leadership to engineering teams dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.


Using Federated Learning to Bridge Data Silos in Financial Services

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Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine learning (ML). For instance, traditional ML methods assume all data can be moved to a central repository. This is an unrealistic assumption when dealing with data sovereignty and security considerations or sensitive data like personally identifiable information. More practically, it ignores data egress challenges and the considerable cost of creating large pooled datasets. Massive internal datasets that would be valuable for training ML models remain unused.


Artificial intelligence use poses an ESG headache for global financial industry

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Artificial intelligence (AI) is often touted as the cure-all for financial services firms' ability to deal with the looming data onslaught stemming from environmental, social & governance (ESG) regulation. Yet ESG also poses an existential threat to the financial services industry's use of AI The European Union's Sustainable Finance Disclosure Regulation has required asset management firms to begin collecting millions of data points from the companies in which they invest, and the forthcoming Corporate Sustainable Reporting Directive will only add to the volume of data points. Further, there is the data being collected under the Task Force on Climate-Related Financial Disclosures (TCFD) initiative and the International Sustainability Standards Board's plans to create a baseline for ESG reporting. Taken all together and it becomes clear that AI-enabled systems will be essential to firms' efforts to make sense of -- and profit from -- all these requirements. The carbon footprint from storing and processing data is enormous and growing, algorithms have already been shown to discriminate against certain groups in the population, and a lack of technology skills in both senior management ranks and the general workforce leave firms vulnerable to mistakes.


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.


AI for good: Research insights from financial services

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Artificial intelligence and machine learning analyses are driving critical decisions impacting our lives and the economic structure of our society. These complex analytical techniques--powered by sophisticated math, computational power, and often vast amounts of data--are deployed in a variety of critical applications, from making healthcare decisions to evaluating job applications to informing parole and probation decisions to determining eligibility and pricing for insurance and other financial services. The risk that these algorithms make unreliable, unfair, or exclusionary predictions is a foundational concern for a variety of highly sensitive use cases. Furthermore, it raises core questions about whether we can sufficiently understand and manage these models in the immediate and the longer term. Yet artificial intelligence (AI) and machine learning (ML), if carefully overseen and deployed with representative data, also have the potential to increase accuracy and fairness over current models by identifying data relationships that current models cannot detect.


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.