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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 facets of the financial sector is showing signs of interest and adoption even among the banking incumbents.
In this article we set out to study the AI applications and innovations at the top banks, helping you to answer the following questions: Through quotes from company executives and data from our AI in Banking Vendor Scorecard and Capability report (interested readers can download the Executive Summary Brief), this article serves to present a concise look at the implementation of AI at seven of America’s top commercial banks by revenue.
First, we’ll provide some insights about the state of AI in banking we discovered through exploring our data and interviews with banking executives Readers with a broader interest in AI’s applications across the financial sector may be interested in reading our article Artificial Intelligence in Finance, which covers a wider array of applications beyond the top US banks.
The full breakdown of AI vendor product offerings by function is provided in the graph below: We should note that banks are likely understating their use of AI for other use-cases, and the banking experts we interviewed for our report and our AI in Banking podcast all agree that banks are investing in AI for compliance and risk monitoring more than any other business area.
At the same time, banks are likely overstating their use of AI for customer service applications, including chatbots because: It will be a while before the technology has advanced enough for chatbots to generate natural language and hold conversations with customers more often than they’re routing customers to customer support agents.
Lastly, our research found a number of top banks referring to AI as an “augmenting” force for their employees, not a “replacement.” To us, this seems to be a necessary move of the communications department, but a disingenuous way to describe AI’s potential impact on jobs, which will most likely involve both “augmenting” and replacing human beings outright.
Unlike many modern tech giants, old banks often have thousands of employees performing mundane paperwork and “legacy” processes, many of which may require complete elimination once machines can replace humans at the desk.
The Emerging Opportunities Engine, introduced in 2015 and discussed in a letter to shareholders, purportedly uses machine learning and natural language processing to help “identify clients best positioned for follow-on equity offerings.” The technology has proven successful in Equity Capital Markets and the company stated their intentions to expand it to other areas, including Debt Capital Markets, but it’s unclear if this has happened yet.
Wells Fargo hasn’t publicized many artificial intelligence initiatives, but Steve Ellis, head of the bank’s Innovation Group, seemed eager to leverage AI in a 2017 press release for a chatbot pilot: AI technology allows us to take an experience that would have required our customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment.
Katherine McGee, Head of Digital Product Management at Wells Fargo, elaborated on one of these prompts in an email to Bank Innovation: If a customer receives an incoming deposit which is not in their usual pattern of transactions and is not needed to meet their normal expenses or scheduled payments, we can highlight the deposit and suggest the customer save the funds.
Consistent with our high-tech, high-touch strategy, we’ll continue evolving our best-in-class digital banking capabilities, including Erica, to provide clients relevant, timely guidance and help make managing their finances easier.
Although the demo video below shows how Feedzai’s software works for eCommerce companies (and is admittedly a little corny), the principles it describes can certainly apply to banks: Feedzai’s software will purportedly monitor customer payment behavior for deviations from that customer’s normal payment activity.
The software is explained further in the video below and a promotional video can be found on US Bank’s website: According to US Bank, using Expense Wizard, a hiring manager can provide a virtual card to a candidate via the app, setting a card limit via US Bank.
PNC invested $1.2.billion over five years, according to its 2016 annual report, into modernizing its “core infrastructure and build[ing] out key technological and operational capabilities,” with the objective of faster, more secure and more stable operations and services.
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.
Examples include “data requests from external auditors” and “funds transfer bots” which help “correct formatting and data mistakes in requests for dollar funds transfers.” The video below provides an explanation for how Blue Prism’s AI software works: Former Senior Executive Vice President and Global Head of Client Service Delivery at BNY Mellon Corp, Doug Shulman, said this about the bank’s investment in RPA: 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.
Surgical robots, new medicines and better care: 32 examples of AI in healthcare
Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. One of the world's highest-growth industries, the AI sector was valued at about $600 million in 2014 and is projected to reach a $150 billion by 2026.
Whether it's used to find new links between genetic codes or to drive surgery-assisting robots, artificial intelligence is reinventing — and reinvigorating — modern healthcare through machines that can predict, comprehend, learn and act.
The company’s deep learning platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs.
The scientists used 25,000 images of blood samples to teach the machines how to search for bacteria. The machines then learned how to identify and predict harmful bacteria in blood with 95% accuracy.
Adam scoured billions of data points in public databases to hypothesize about the functions of 19 genes within yeast, predicting 9 new and accurate hypotheses.
BERG recently presented its findings on Parkinson’s Disease treatment — they used AI to find links between chemicals in the human body that were previously unknown — at the Neuroscience 2018 conference.
Location: Cambridge, Massachusetts How it's using AI in healthcare: Combining AI, the cloud and quantum physics, XtalPi’s ID4 platform predicts the chemical and pharmaceutical properties of small-molecule candidates for drug design and development.
Additionally, the company claims its crystal structure prediction technology (aka polymorph prediction) predicts complex molecular systems within days rather than weeks or months.
Atomwise’s AI technology screens between 10 and 20 million genetic compounds each day and can reportedly deliver results 100 times faster than traditional pharmaceutical companies.
Location: London, England How it's using AI in healthcare: The primary goal of BenevolentAI is to get the right treatment to the right patients at the right time by using artificial intelligence to produce a better target selection and provide previously undiscovered insights through deep learning.
A 2016 study of 35,000 physician reviews revealed 96% of patient complaints are about lack of customer service, confusion over paperwork and negative front desk experiences.
New innovations in AI healthcare technology are streamlining the patient experience, helping hospital staff process millions, if not billions of data points, faster and more efficiently.
The company’s technology helps hospitals and clinics manage patient data, clinical history and payment information by using predictive analytics to intervene at critical junctures in the patient care experience.
Location: Cleveland, Ohio How it's using AI in healthcare: The Cleveland Clinic teamed up with IBM to infuse its IT capabilities with artificial intelligence. The world-renowned hospital is using AI to gather information on trillions of administrative and health record data points to streamline the patient experience.
Since implementing the program, the facility has seen a 60% improvement in its ability to admit patients and a 21% increase in patient discharges before noon, resulting in a faster, more positive patient experience.
Additionally, the inability to connect important data points is slows the development of new drugs, preventative medicine and proper diagnosis. Many in healthcare are turning to artificial intelligence as way to stop the data hemorrhaging.
Location: Seattle, Washington How it's using AI in healthcare: KenSci combines big data and artificial intelligence to predict clinical, financial and operational risk by taking data from existing sources to foretell everything from who might get sick to what's driving up a hospital’s healthcare costs.
The company’s software helps pathology labs eliminate bottlenecks in data management and uses AI-powered image analysis to connect data points that support cancer discovery and treatment.
How it's using AI in healthcare: When IBM’s Watson isn’t competing on Jeopardy!, it's helping healthcare professionals harness their data to optimize hospital efficiency, better engage with patients and improve treatment.
Location: Shenzhen, China How it's using AI in healthcare: ICarbonX is using AI and big data to look more closely at human life characteristics in a way they describe as “digital life.' By analyzing the health and actions of human beings in a “carbon cloud,' the company hopes its big data will become so powerful that it can manage all aspects of health.
Robots equipped with cameras, mechanical arms and surgical instruments augment the experience, skill and knowledge of doctors to create a new kind of surgery. Surgeons control the mechanical arms while seated at a computer console while the robot gives the doctor a three dimensional, magnified view of the surgical site that surgeons could not get from relying on their eyes alone.
Being the first robotic surgery assistant approved by the FDA over 18 years ago, the surgical machines feature cameras, robotic arms and surgical tools to aide in minimally invasive procedures.
Under a physician’s control, the tiny robot enters the chest through a small incision, navigates to certain locations of the heart by itself, adheres to the surface of the heart and administers therapy.
Location: Eindhoven, The Netherlands How it's using AI in healthcare: MicroSure’s robots help surgeons overcome their human physical limitations. The company's motion stabilizer system reportedly improves performance and precision during surgical procedures.
Location: Caesarea, Israel How it's using AI in healthcare: Surgeons use the Mazor Robotics' 3D tools to visualize their surgical plans, read images with AI that recognizes anatomical features and perform a more stable and precise spinal operation.
This Week In AI: Algolia Raises $110M, OpenAI Debuts Rubik’s Cube Solving Bot, Kleiner Perkins Backs Cell Therapy Startup
Artificial intelligence has long been a major focus for tech leaders across industries. Big corporations across every sector, from retail to agriculture, are trying to integrate machine learning into their products.
Apple’s AI acquisition spree, which has helped it overtake Google in recent years, was essential to the development of new iPhone features. For example, FaceID, the technology that allows users to unlock their iPhone X just by looking at it, stems from Apple’s M&A moves in chips and computer vision, including the acquisition of AI company RealFace.
AI acquisitions saw a more than 6x uptick from 2013 to 2018, including last year’s record of 166 AI acquisitions — up 38% year-over-year. In 2019, there have already been 140+ acquisitions (as of August), putting the year on track to beat the 2018 record at the current run rate.
Part of this increase in the pace of AI acquisitions can be attributed to a growing diversity in acquirers. Where once AI was the exclusive territory of major tech companies, today, smaller AI startups are becoming acquisition targets for traditional insurance, retail, and healthcare incumbents.
Recent examples include McDonald’s $300M acquisition of personalization platform Dynamic Yield, Ulta Beauty’s acquisitions of virtual makeover startup GlamST and customer engagement software company QM Scientific, and Nike’s acquisitions of inventory management company Celect and guided shopping experience platform Invertex.
Our definition of AI companies includes startups selling AI SaaS (such as machine learning-based cyber defense software), using AI algorithms to develop their core products (such as AI-enabled diagnostic devices), developing hardware to support AI workloads (startups building AI processors for edge and cloud data centers), and AI “consultancies”
AI 50: America’s Most Promising Artificial Intelligence Companies
Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak.
As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: “Everyone knows you have to have machine learning in your story or you’re not sexy.” The inherently broad term gets bandied about so often that it can start to feel meaningless and gets trotted out by companies to gussy up even simple data analysis.
To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so.
To be included on the list, companies needed to show that techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to “understand” written or spoken language), or computer vision (which relates to how machines “see”) are a core part of their business model and future success.
Only eight startups were founded or cofounded by women, reflecting trends in venture funding, where software startups run by men have received the lion’s share of investment dollars.
Its software cross-references CT images of a patient’s brain with its database of scans and can alert specialists in minutes to early signs of large vessel occlusion strokes that they may have otherwise missed or taken too long to spot.
CEO Wout Brusselaers says the company’s software can pull data from electronic medical records to create patient graphs that allow researchers to filter for specific conditions and traits, leading to matches in “minutes, instead of months.” The system’s language understanding engine has been trained so that it can infer some conditions even if they’re not explicitly mentioned in notes, and Deep 6 says it has more than 20 health system or pharmaceutical customers.
CEO and cofounder Dhananjay Sampath launched Armorblox into the saturated cybersecurity market two years ago with the aim of protecting customers from socially engineered attacks, like phishing emails, that take advantage of human missteps.
“However, the real-world use cases tend to move in the opposite direction—demanding solutions with less compute, memory and power.” They set about trying to create a system where complex algorithms could run on simple hardware and spun out of the Allen Institute, which was cofounded by the late Paul Allen of Microsoft, in 2016.
The company admits that its AI agent, Chloe, is still in its infant stage right now—it can complete simple tasks like reading the instructions on a pill bottle — but it has big ambitions for more robust computer-vision-based navigation.
While chat bots burst onto the scene with a lot of promise (remember how they were going to take over Facebook Messenger?), they never quite reached mainstream adoption, due in part to disenchantment with their limited scope and conversational rigidity.
it just happens to be stuck in a research paper somewhere.” He and longtime Microsoft employee Diego Oppenheimer banded together to devise an easier way for data scientists to discover and work with machine learning models.
The app can build a 3D map of a space in roughly 30 seconds and uses computer vision to recognize real-world objects, so that objects created in AR can interact with them like they would in the real world.
The company recently raised a fresh round of funding led by automotive company Aptiv with the hope that its technology could one day be integrated into smart cars (imagine a vehicle that could issue a warning to a drowsy-looking driver).In the meantime, it’s also being used to test consumer feedback on ads and TV programming.
Blue Hexagon, led by longtime Qualcomm executive Nayeem Islam, spent more than a year and a half building a deep learning system to analyze network traffic that it says can detect and block threats in under a second.
It evaluates data from hundreds of online and offline data sources including credit bureaus, carrier phone records, IP addresses, social networks and more, to monitor for any suspicious behavior.
Chief technology officer Sarjoun Skaff says it has taken iteration after iteration since 2013 to figure out how to let its robots maneuver safely around shoppers and interpret billions of images in a way that was accurate, timely and reliable.
Pymetrics gleans key emotional and cognitive traits for different roles so when job seekers apply to work at one of those companies and complete the challenges themselves, they’re paired with jobs that are the best fit.
The company’s consumer app draws on a data set of more than 2 billion anonymized medical records, finding subtle patterns in the data to give users personalized health advice.
In July, K announced a partnership with insurance provider Anthem to let members see how doctors diagnose and treat similar people with similar symptoms for free (though they’ll be charged to chat with an actual doctor).
Computers were too slow and data too expensive to make AI practical at the time, but roughly three decades later, Pratt teamed up with investment firm TPG to help it identify a data analytics company to buy or invest in.
The company uses vast quantities of nontraditional data—like signals from cellphones, internet-of-things devices and street cameras—to issue hyperlocal “street-by-street, minute-by-minute” weather forecasts.
Its image processing technology, called Cortex, works with its own 3D camera, as well as a selection ofcheaper360-degree cameras, to let users create virtual versions of their space.The company’s leaning into the real estate market, showcasing how agents can use it to give 3D tours.
Beck, who spent more than five years as a pathologist at Harvard, wants to make it easier for other pathologists to diagnose diseases like cancer by using machine learning to more quickly and accurately analyze images of cells.
Its overhead cameras track individuals and items continuously (notably, its so-called entity cohesion doesn’t rely on facial recognition, which it says gives shoppers more privacy).
A lineup of cloud-connected security cameras equipped with AI-driven features like object and movement detection has driven growth at Verkada, whose cofounders are three Stanford computer science graduates and the cofounder of enterprise cloud company Meraki, which sold to Cisco for more than $1 billion.
Among the company’s wide-ranging list of clients are fitness club Equinox, the $1.1 billion Vancouver Mall and more than 500 school districts, which use the cameras for anything from monitoring student safety to tracking food deliveries.
SentinelOne CEO Tomer Weingarten says he and his cofounder started the company in 2013 because antivirus software at the time was “some flavor of bad, incomplete, ineffective, and /or painful to deploy and operate.” They spent the past six years figuring out how to make endpoint security (which focuses on data coming from laptops, phones, and other network-connected devices) smarter, training machine learning models to detect malware in files and running in applications.
“Until now, the most complex operations in manufacturing have been too difficult for blind and dumb robots to perform with the same precision and fidelity as humans,” says CEO Amar Hanspal, adding that advances in computer vision and machine learning have changed the game.
Upstart CEO Dave Girouard admits that most of the early team of former Google employees had no history in financial services when they came up with the idea to apply advanced data science to the credit process in 2012: Only the belief that the current system was antiquated and exclusionary.
By using data not typically found in a person’s credit history to find more nuanced risk patterns, Girouard says Upstart’s lending model has higher approval rates and lower interest rates than traditional methods, with loss rates that are “less than half” of those of peer platforms.
Former Oculus cofounder Palmer Luckey is back after his dramatic exit from Facebook (he has hinted that the company fired him from the virtual reality unit for his political views, which it denies) with a defense technology startup called Anduril Industries, founded in 2017.
It has contracts with the Marine Corps and UK’s Royal Navy, as well as with Customs and Border Protection for what has been described as a controversial “virtual border wall.” Following a report that it became a unicorn after a recent fundraise, the company confirmed to Forbes that it now has $180 million in total funding and a near-billion valuation.
Alexandr Wang’s data labeling startup Scale AI has gained so much attention from customers — particularly autonomous transportation companies, which need gobs of well-labeled data to train their systems — that he’s running a unicorn company before his 23rd birthday.
It pulls public data about a property to automatically answer many of the questions a typical insurer would ask, which means it can quickly dole out quotes, and pulls data from aerial images and smart home sensors to detect issues that could lead to claims in real-time.
Dataminr ingests public internet data, like social media posts, and uses deep learning, natural language processing, and advanced statistical modeling to send users tailored alerts.
Uptake CEO Brad Keywell says his company is in the business of making sure things work, “whether it’s the U.S. Army’s Bradley Fighting Vehicle, or the components that make up Rolls-Royce’s fleet of market-leading engines.” It’s brought in more than 100 industrial customers on its way to a $2.3 billion valuation.
trifecta of autonomy and transportation experts from Tesla, Uber, and Google came together to build Aurora, a self-driving car company that plans to sell its system to automakers instead of operating its own fleet (it currently has a deal with Hyundai to provide software for its future Kia models).
- On Saturday, April 4, 2020
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