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AI in Banking – An Analysis of America’s 7 Top Banks
While tech giants tend to hog the limelight on the cutting-edge of technology, AI in banking and other financial sectors is showing signs of interest and adoption –
In this article we set out to study the AI applications of top banks, helping you to answer the following questions: Through facts and quotes from company executives, this article serves to present a concise look at the implementation of AI by the seven leading commercial banks in the U.S. as ranked by the Federal Reserve.
(Readers with a more broad interest in AI’s applications across the financial sector may be interested in reading our article titles “Machine Learning in Finance“, which covers a wider array of applications beyond the top US banks.) JPMorgan Chase has invested in technology and recently introduced a Contract Intelligence (COiN) platform designed to “analyze legal documents and extract important data points and clauses.” Manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours.
Morgan (@jpmorgan) February 28, 2017 The Emerging Opportunities Engine, introduced in 2015 and discussed in a letter to shareholders, uses automated analysis to help “identify clients best positioned for follow-on equity offerings.” The technology has proven successful in Equity Capital Markets and is currently being expanded to other areas including Debt Capital Markets.
First successfully piloted in 2016, the firm plans to officially roll out its virtual assistant technology which integrates a natural language interface “to respond to employee technology service desk requests.” The initial goal will be 120,000 service tickets with future expansion to efficiently address more of the 1.7 million employee requests the company receives each year.
Matt Zames, CEO, 2016 Annual Report The firm invested over $9.5 billion in technology in 2016, with $3 billion “dedicated toward new initiatives” and a $600 million fraction slated for “emerging fintech solutions.” Specific interests include partnerships with fintech companies and developing new and enhancing current digital and mobile services.
The AI group is nestled under the umbrella of the Payments, Virtual Solutions and Innovation group which has three main goals: “…increase connectivity for the company’s payments efforts, accelerate opportunities with artificial intelligence, and advanced application programming interfaces to corporate banking customers.” In April, the company began piloting an AI-driven chatbot through the Facebook Messenger platform with “several hundred employees.” This virtual assistant communicates with users to provide account information and helps customers reset their passwords.
Officially unveiled at the 2016 Money 20/20 conference in Las Vegas, described as the world’s largest payment and financial services innovation event, erica is a chatbot leveraging “predictive analytics and cognitive messaging” to provide financial guidance to the company’s over 45 million customers.
As an integrated component of the mobile banking experience, erica is designed to be accessible to clients 24/7 and perform “day-to-day transactions” in addition to anticipating the unique financial needs of each customer and helping them reach their financial goals by providing smart recommendations.
Incorporating artificial intelligence into our mobile banking offering will help customers manage their simple banking needs more efficiently and consistently, which then allows our specialists in our financial centers to spend more time with customers to understand their more complex needs and help them improve their financial lives.” -Thong Nguyen, president of Retail Banking, Bank of America The company’s reported $3 billion innovation budget in 2016 and the Annual Technology Innovation Summit held in Silicon Valley “to explore potential partnerships, discuss trends and solve some of today’s challenges” positions the company firmly to maintain a bird’s eye view on the latest innovations in fintech.
“The future of financial services will be transformed by those who can successfully leverage APIs to connect consumers with best-of-breed providers, Citi Ventures is pleased to support Clarity Money, which is committed to responsible finance and is empowering consumers via APIs with actionable insights and third-party products that can help improve their financial health.” –
Still in the early stages of its tech strategy, the company’s initial focus has been on the consolidation of its data centers and a major shift to an “internal cloud environment.” We can presume that the company’s infrastructure upgrades will help them leverage data and implement artificial intelligence and machine learning (indeed companies like Cloudera make their living from getting legacy companies up to speed in AI-ready infrastructure), but no direct mention of AI is made in the annual report.
Examples include “data requests from external auditors” and “funds transfer bots” which help “correct formatting and data mistakes in requests for dollar funds transfers.” “If you think about smart automation, robotics is a piece, workflow is a piece, and we’re combining smart forms, optical character recognition, workflow and robotics to get momentum around automating tasks.” – Doug Shulman, Senior Executive Vice President and Global Head of Client Service Delivery BNY Mellon reports that the implementation of RPA has led to the following results: From a financial perspective, the corporation estimates that the activity of its “funds transfer bots” alone is responsible for $300,000 in annual savings.
The future of artificial intelligence
Sitting at his cluttered desk, located near an oft-used ping-pong table and prototypes of drones from his college days suspended overhead, Gyongyosi punches some keys on a laptop to pull up grainy video footage of a forklift driver operating his vehicle in a warehouse.
It was captured from overhead courtesy of a Onetrack.AI “forklift vision system.” Employing machine learning and computer vision for detection and classification of various “safety events,” the shoebox-sized device doesn’t see all, but it sees plenty.
The mere knowledge that one of IFM’s devices is watching, Gyongyosi claims, has had “a huge effect.” “If you think about a camera, it really is the richest sensor available to us today at a very interesting price point,” he says.
Here’s another: Tesla founder and tech titan Elon Musk recently donated $10 million to fund ongoing research at the non-profit research company OpenAI — a mere drop in the proverbial bucket if his $1 billion co-pledge in 2015 is any indication.
This, however, is not: After more than seven decades marked by hoopla and sporadic dormancy during a multi-wave evolutionary period that began with so-called “knowledge engineering,” progressed to model- and algorithm-based machine learning and is increasingly focused on perception, reasoning and generalization, AI has re-taken center stage as never before.
There’s virtually no major industry modern AI — more specifically, “narrow AI,” which performs objective functions using data-trained models and often falls into the categories of deep learning or machine learning — hasn’t already affected.
That’s especially true in the past few years, as data collection and analysis has ramped up considerably thanks to robust IoT connectivity, the proliferation of connected devices and ever-speedier computer processing.
With companies spending nearly $20 billion collective dollars on AI products and services annually, tech giants like Google, Apple, Microsoft and Amazon spending billions to create those products and services, universities making AI a more prominent part of their respective curricula (MIT alone is dropping $1 billion on a new college devoted solely to computing, with an AI focus), and the U.S. Department of Defense upping its AI game, big things are bound to happen.
Of the former, he warned: “The bottom 90 percent, especially the bottom 50 percent of the world in terms of income or education, will be badly hurt with job displacement…The simple question to ask is, ‘How routine is a job?’ And that is how likely [it is] a job will be replaced by AI, because AI can, within the routine task, learn to optimize itself.
And the more quantitative, the more objective the job is—separating things into bins, washing dishes, picking fruits and answering customer service calls—those are very much scripted tasks that are repetitive and routine in nature.
In the matter of five, 10 or 15 years, they will be displaced by AI.” In the warehouses of online giant and AI powerhouse Amazon, which buzz with more than 100,000 robots, picking and packing functions are still performed by humans — but that will change.
“One of the absolute prerequisites for AI to be successful in many [areas] is that we invest tremendously in education to retrain people for new jobs,” says Klara Nahrstedt, a computer science professor at the University of Illinois at Urbana–Champaign and director of the school’s Coordinated Science Laboratory.
In the future, if you don’t know coding, you don’t know programming, it’s only going to get more difficult.” And while many of those who are forced out of jobs by technology will find new ones, Vandegrift says, that won’t happen overnight.
“The transition between jobs going away and new ones [emerging],” Vandegrift says, “is not necessarily as painless as people like to think.” 'In the future, if you don’t know coding, you don’t know programming, it’s only going to get more difficult.” Mike Mendelson, a “learner experience designer” for NVIDIA, is a different kind of educator than Nahrstedt.
While some of these uses, like spam filters or suggested items for online shopping, may seem benign, others can have more serious repercussions and may even pose unprecedented threats to the right to privacy and the right to freedom of expression and information (‘freedom of expression’).
Speaking at London’s Westminster Abbey in late November of 2018, internationally renowned AI expert Stuart Russell joked (or not) about his “formal agreement with journalists that I won’t talk to them unless they agree not to put a Terminator robot in the article.” His quip revealed an obvious contempt for Hollywood representations of far-future AI, which tend toward the overwrought and apocalyptic.
Once we have that capability, you could then query all of human knowledge and it would be able to synthesize and integrate and answer questions that no human being has ever been able to answer because they haven't read and been able to put together and join the dots between things that have remained separate throughout history.” That’s a mouthful.
More than a few leading AI figures subscribe (some more hyperbolically than others) to a nightmare scenario that involves what’s known as “singularity,” whereby superintelligent machines take over and permanently alter human existence through enslavement or eradication.
The late theoretical physicist Stephen Hawking famously postulated that if AI itself begins designing better AI than human programmers, the result could be “machines whose intelligence exceeds ours by more than ours exceeds that of snails.” Elon Musk believes and has for years warned that AGI is humanity’s biggest existential threat.
“I think that maybe five or ten years from now, I’ll have to reevaluate that statement because we’ll have different methods available and different ways to go about these things.” While murderous machines may well remain fodder for fiction, many believe they’ll supplant humans in various ways.
As MIT physics professors and leading AI researcher Max Tegmark put it in a 2018 TED Talk, “The real threat from AI isn’t malice, like in silly Hollywood movies, but competence — AI accomplishing goals that just aren’t aligned with ours.” That’s Laird’s take, too.
“I think that’s science fiction and not the way it’s going to play out.” What Laird worries most about isn’t evil AI, per se, but “evil humans using AI as a sort of false force multiplier” for things like bank robbery and credit card fraud, among many other crimes.
Referencing the rapid transformational effect of nuclear fission (atom splitting) by British physicist Ernest Rutherford in 1917, he added, “It’s very, very hard to predict when these conceptual breakthroughs are going to happen.” But whenever they do, if they do, he emphasized the importance of preparation.
- On 21. oktober 2021
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