Artificial intelligence (AI) is the tech to watch. IDC forecasts that spending on AI technologies will grow to $97.9 billion in 2023--more than two and a half times the spending level of 2019. Where is AI's growth coming from? He says AI's explosive growth is happening in consumer finance and capital markets. Banks, insurance companies, payment firms and others are investing in AI Centers of Excellence (CoE).

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.

We've reached a point "where AI and machine learning are converging" and it's helping drive operational efficiencies and customer experience (CX) innovation for a wide range of businesses. That's how author and veteran IT expert Tom Davenport began a wide-ranging discussion about the state of AI and automation in the financial services sector at a recent virtual salon with senior industry executives. Another speaker, Rob Krugman, Chief Digital Officer at financial services firm Broadridge, said his firm is "increasingly using AI models to define and select the attributes and information that is important to their customers". The session was hosted by Bill Wright, Head of AI Machine Learning and Edge Innovation for Red Hat. It was moderated by CDX.

The rapid advancement of Artificial Intelligence (AI) technologies has already transformed business operations across the globe. From customer service chat-bots to adaptive cybersecurity, the applications of AI are nearly limitless. When properly designed, AI can help minimize paperwork, reduce costs, and drive better business decisions by increasing the predictive accuracy of future outcomes and mitigating the cognitive biases inherent in human decision making. In the financial services industry, AI has the potential to expand access to affordable credit for consumers and small businesses and combat fraud and financial crimes, but many financial institutions remain reluctant to deploy AI to its maximum potential without clear guidance from US regulatory agencies. Like many new technologies, the current AI landscape lacks a depth of established legal and regulatory precedent to rely on.

Turn around in almost any city, and you're likely to see a bank or lender or brokerage on the corner. In fact, in my family's small town, we have two financial institutions by the same name on either side of a two-lane street. And, while I love the personal experience I get from visiting my hometown banker, I also appreciate being able to conduct my business after the bankers have gone home to dinner, and knowing that my fraud protection never sleeps. Financial services institutions (FSIs) of all sizes recognize that they are in fierce competition to deliver differentiated services while meeting stringent regulatory and compliance requirements. Among the earliest adopters of digital transformation, FSIs satisfy these requirements with a range of emerging technologies, including artificial intelligence (AI).

Dr. Iain Brown is the Head of Data Science for SAS UK&I and Adjunct Professor of Marketing Analytics at the University of Southampton. For the last decade he has been working closely with the financial services sector, providing thought leadership on the topics of risk, AI and machine learning. During his time at SAS he has been involved in driving innovation in AI and the corresponding fields of machine learning, computer vision and natural language understanding through the delivery of numerous projects. He is also a contributor to SAS' blog and an active member of the AI community on Twitter. In a wide-ranging conversation about the applications of machine learning in the financial services sector, Iain offered some helpful advice around the integration of AI into business models.

A holistic view of automation capabilities can help organize and galvanize a team to avoid the common robotics and cognitive automation pitfalls and ultimately achieve scale. Start by articulating the robotics and cognitive automation mission based on key value drivers and establish a clear and compelling business case. Create a broad vision and roadmap. Establish robust, right-sized governance, select an appropriate operating model, and collaborate across boundaries. You also want to gain access to the necessary specialized skills and talent.

The financial services industry has always been among the first sectors to embrace leading-edge information technology, so it should not come as a surprise that artificial intelligence (AI) is no exception. After all, trading firms have been taking advantage of various algorithms to programmatically trade stocks in matter of microseconds for years now. But the rise of AI does present the financial services sector with some unique challenges. Financial services firms have made massive investments in data centers to run some of the most latency-sensitive applications in the world. And yet, for AI to be accurate, the models that get constructed need access to massive amounts of data that is costly to store locally.

Newswire) Autonomous Research, a global research firm focused on financial services, has announced the publication of Augmented Finance and Machine Intelligence, an in-depth look at the way artificial intelligence is transforming the financial services industry. Autonomous estimates that over $1 trillion of today's financial services cost structure could be replaced by machine learning and AI. This would affect 2.5 million employees in the US alone. This shift will impact operations across all functions and segments of the financial industry, from bank tellers to portfolio managers to insurance underwriters. By 2030, Autonomous expects $490 billion in costs to be exposed to AI in distribution, $350 billion in the middle office, and $200 billion in financial product manufacturing.

The financial services industry is undergoing one of the largest transformational shifts in decades, driven by the development of new digital products and services, broadening availability of powerful computing solutions, and increased customer adoption of cloud, mobile, web-based and AI technologies. As the financial industry increasingly realizes the impact of faster analytical insights on overall business strategy, AI techniques like machine learning are permeating nearly every industry. Deep learning, the fastest-growing field in machine learning, leverages many-layered deep neural networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text. This technique is showing great promise for automating a variety of operational processes and ushering in disruptive new business models for the industry. However, these newfound capabilities are quickly pushing conventional computing architectures to their limits.