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Marcus Invest by Goldman Sachs just became much more accessible to investors

ZDNet

Goldman Sachs announced that beginning Wednesday, June 29, it is lowering its account minimums and management fees for Marcus Investing accounts. The account minimum is being reduced drastically, from $1,000 to $0, and the minimum investment is now just $5. In addition, the company is reducing its portfolio management fee from 0.35% to 0.25%. These are welcomed changes to the robo-advisory investment platform. A robo-advisor investment platform manages a selection of portfolios on behalf of investors.


Informatica Launches Intelligent Data Management Cloud for Financial Services

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Informatica, an enterprise cloud data management leader, announced the Intelligent Data Management Cloud (IDMC) for Financial Services, an end-to-end integrated data management cloud that enables the entire data lifecycle, including data discovery, ingestion, integration of data and applications, quality improvement, single views and business 360 applications, governance, privacy, and data sharing and democratization. IDMC for Financial Services leverages Informatica's cloud native solutions as an integrated platform to help financial services companies access and leverage Fit for Business Use data to support their top business priorities including: Improve Customer Experience: IDMC for Financial Services allows companies to access and deliver clean, trusted and valid data between the systems that support customer engagement and interaction across any channel, device or business unit. In addition, it enables companies to organize, relate and deliver a 360-degree view of the business for everyone from customer service, sales, and financial advisors to insurance agents to deliver exceptional customer service at their time of need. Grow the Business: IDMC for Financial Services helps marketing and sales organizations identify new cross-sell opportunities to expand wallet share with existing customers to help drive revenue streams and retain customer relationships. It enables users to obtain clean, valid and holistic data about each customer relationship, the accounts or policies they own, and how they are related to other customers, employees or business entities.


Transforming Financial Services with Data-Driven Insights - HPCwire

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Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.


AI in the Canadian Financial Services Industry

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In recent years, players within Canada's financial services industry, from banks to Fintech startups, have shown early and innovative adoption of artificial intelligence ("AI") and machine learning ("ML") within their organizations and services. With the ability to review and analyze vast amounts of data, AI algorithms and ML help financial services organizations improve operations, safeguard against financial crime, sharpen their competitive edge and better personalize their services. As the industry continues to implement more AI and build upon its existing applications, it should ensure that such systems are used responsibly and designed to account for any unintended consequences. Below we provide a brief overview of current considerations, as well as anticipated future shifts, in respect of the use of AI in Canada's financial services industry. At a high level, Canadian banks and many bank-specific activities are matters of federal jurisdiction.


IBM launches Watson-backed virtual assistant for TD Securities

ZDNet

IBM unveiled a new AI-based virtual assistant powered by IBM Watson Assistant that is designed to help customers answer crucial questions. The first company to take advantage of the tool is TD Securities, which will use the virtual agent system for its TD Precious Metals digital store. The TD Precious Metals digital store allows customers to digitally purchase physical gold, silver and platinum bullion, and coins. The virtual assistant is available now and offers both written responses and links to other guides or FAQs, according to IBM. The virtual assistant is designed to handle frequently asked questions like "How is pricing determined?" "How will items be shipped?" or "Is there a minimum or maximum product count/dollar value when making a purchase?"


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


How AI improves AML efforts across the financial services industry - Banking Exchange

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Money laundering and other types of financial crime have plagued the financial industry for years. Banks and other financial institutions have consistently found themselves one step behind criminals looking to take advantage of the holes within banks' security and monitoring systems and carry out criminal activity undetected. In response, many of these organizations have put in place anti-money laundering (AML) solutions. However, it's no secret that these systems still leave much to be desired. Attempting to stop money laundering without concern for accuracy can create real challenges.


Seven technologies shaping the future of fintech

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Technological progress and innovation are the linchpins of fintech development, and will continue to drive disruptive business models in financial services. McKinsey estimates that artificial intelligence (AI) can generate up to $1 trillion additional value for the global banking industry annually. Banks and other financial institutions are tipped to adopt an AI-first mindset that will better prepare them to resist encroachment onto their territory by expanding technology firms. In financial services, automatic factor discovery, or the machine-based identification of the elements that drive outperformance, will become more prevalent, helping to hone financial modeling across the sector. As a key application of AI semantic representation, knowledge graphs and graph computing will also play a greater role.


Why is XAI at core for the success of 'AI' in Financial Institutions? And what is Arya-xAI?

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It is imperative for next generation applications to have AI at the core. With almost all major tech players offering AI enabled solutions, we see it as a default feature in any upcoming software products. Many financial institutions have already started their innovation programs by automating existing rule sets with machine learning models to automate/augment existing processes. These models can make automated decisions across vast quantities of data. Even then, organizations are somewhat apprehensive in deploying these systems into the core process, since AI solutions carry a, probably justified, reputation for being'black boxes' characterized by poor transparency.


How the Financial Industry Can Apply AI Responsibly

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THE INSTITUTE Artificial intelligence is transforming the financial services industry. The technology is being used to determine creditworthiness, identify money laundering, and detect fraud. AI also is helping to personalize services and recommend new offerings by developing a better understanding of customers. Chatbots and other AI assistants have made it easier for clients to get answers to their questions, 24/7. Although confidence in financial institutions is high, according to the Banking Exchange, that's not the case with AI.