If the information revolution were a marathon, then financial services would be in the beginning of the artificial intelligence leg of the race. That is according to a survey by Deloitte on how firms are leveraging AI across their enterprises.
I wanted to know how wealth management influencers were interpreting the impact of AI across the industry, so I pulled a sampling of their posts on the topic to find out what they’re sharing. There was a wide range of subjects including how leading firms are pulling away from laggards in AI deployment, how AI is changing the way consumers spend and save and how new Banking as a Service (BaaS) startups are shifting the landscape and turning banks from physical silos of branches and people into just another software feature deployed through the cloud.
Why AI is the Future of Financial Services
— LouisColumbus (@LouisColumbus) August 15, 2019
Lots of firms are throwing money at AI projects, but how do we know which ones are working and which ones aren’t? Which ones are helping the bottom line and which ones are vanity projects so that CIO can point to to show that she’s an innovator?
According to a survey by Deloitte, 60% of frontrunner financial services firms are defining AI success by improvements to revenue and 47% by improving customer experience. Frontrunners are defined as those firms that gain the greatest financial returns from their technology implementations versus their peers.
Frontrunner firms are 12X more likely to see and act on the importance of AI to their businesses than late adopters. They recognized AI’s importance to their businesses and sought out preemptive revenue and cost advantages over competitors. One of the keys to the success of frontrunners is their top down approach to implementing new technology. 49% of them have a comprehensive, detailed, companywide strategy in place for AI adoption, which departments are expected to follow.
How is your firm executing their AI strategy?
…so advisors can dedicate more resources to the human aspects of their jobs.https://t.co/k92L17i4Xz
by @vsoloINSART #wealthtech #wealthmanagement #fintech #ArtificialIntelliegnce pic.twitter.com/ehjFrUTXPf
— Jeff Marsden (@Jeff_Marsden) June 8, 2019
Natural language processing (NLP) is an AI function that refers to a machine’s ability to understand, analyze, and respond to human speech. NLP is at the core of chatbots and can also be used to locate important topical words and phrases from documents, emails, texts or any unstructured data. This is commonly known as automatic keyphrase extraction.
Redtail has built the most popular advisor CRM and has also been at the forefront of AI development in wealth management. They have been working hard on leveraging keyphrase extraction in their software to analyze their huge store of client data in the form of advisor notes, emails and texts.
Redtails’ algorithms identifies clients’ interests, behavior trends, and sentiments which advisors can leverage as talking points with their clients or point out those that may need extra hand-holding during a crisis. In addition, these tools can identify potential shifts in client risk tolerance based on life events or other issues.
How AI Will Change the Way You Manage Your Money
Good read: How AI will change the way you manage your money #AI #FinTech #PersonalFinance
cc @Uday_Akkaraju @SpirosMargaris @UrsBolt @craigiskowitz @KMcDSAP @jaypalter @efipm @TheRudinGroup https://t.co/EnXNn14hLI via @financialtimes
— Theodora (Theo) Lau – 劉䂀曼 (@psb_dc) August 17, 2019
How fast advisors get access to AI-driven analysis and recommendations could be a limiting factor on the growth of robo-advisors and automated digital advice services. Technology cuts both ways. Once a new algorithm hits the market, it can be copied and disseminated quickly. Advisory firms need to keep forging ahead or risk falling behind.
AI-powered software can already guide decisions about how quickly to pay off a mortgage and how much to save for the future. All of this will be decided in the context of each client’s goals and circumstances. In theory, such an all-purpose service could become reality says Daniel Hegarty, founder of Habito, an online mortgage broker. “Once you understand a person’s preferences, and have all of the data and all of the products, it’s just arithmetic,” he says. “There will initially be a series of point solutions — for ISAs, current accounts, mortgages, pensions — but as the data infrastructure matures across all of personal finance, that doesn’t sound crazy to me.”
AI is Creating Opportunities for Banks
Love their out-of-the-box way!🤓🚀
— Urs Bolt | ti&m 🇨🇭 (@UrsBolt) April 10, 2019
Envel, a challenger bank that uses AI to manage customers’ expenses and automate their savings in order to help create stability in a world where chaos often dominates. Envel, founded this year at Harvard and with pre-seed funding from the MIT Sandbox, analyzes customers’ data to learn their personal spending habits, bill payment patterns, and other activities and organizes that finances into a series of buckets. The goal is to help customers manage their regular bills and recurring subscriptions, create an emergency savings fund, build a long-term savings balance and create a daily guilt-free spending limit.
The key to the success of their online finance platforms is their ability to integrate via software interfaces to disparate networks including nontraditional lenders, savings apps, robo-advisors and others to scale up banking services. They’re also connected to a deposit network that includes 800 community banks from across the country.
Envel is also affiliated with Joust, a banking platform that offers loans and other services to workers in the gig economy. I predict these tightly integrated, mobile first services will soon dominate consumer finance. These startups are filling niches traditional banks have ignored and will quickly build loyal bases of millions of users that are unlikely to switch back to a bank relationship.
Banking Can No Longer Ignore The Power of AIhttps://t.co/14zM2vY7ja#banking #fintech #finserv #AI #machinelearning@FinancialBrand @DeepLearn007 @MikeQuindazzi @SpirosMargaris @ipfconline1 @Fisher85M @rshevlin @psb_dc @DeepMindAI @andi_staub @OpenAI @responsiveai @antgrasso pic.twitter.com/QG2cG9YLhO
— Jim Marous 💯 (@JimMarous) August 19, 2019
The Deloitte Center for Financial Services surveyed more than 200 financial services executives who were already using AI technology. Their research identified three key characteristics of organization that have seen the best results:
- Integrate AI into Strategic Plans: To enable an enterprise-wide deployment of AI capabilities, leading organizations embed advanced analytics as part of the overall strategic plan. The greater the importance within the strategic plan, the higher the investment in big data and AI solutions.
- Use of AI for Revenue and CX Initiatives: In addition to using advanced analytics for cost savings, the leading AI organizations are tracking how advanced analytics can be utilized for revenue and customer experience opportunities.
- Looking Outside the Institution: Rather than trying to build all AI applications internally, leaders are leveraging outside partnerships that provide access to talent and solutions. This improves speed to market at a time when available talent is in short supply.
AI Revolutionizes Mobile Payment
This isn’t a wealth management story, but I included it because it’s a disruptive idea that we won’t need cash or cards or even our phones to buy things. We’ll only need our face.
#China leveraging #FacialRecognition to authorize #Payments in vending machines >>> @Diply via @MikeQuindazzi >>> #AI #Autonomous #FinTech #Automation #MachineLearning #DeepLearning pic.twitter.com/9wXLuiIxEl
— Mike Quindazzi ✨ (@MikeQuindazzi) August 18, 2019
Biometrics-as-a-Service (BaaS) offers a cost-effective way for financial institutions to integrate facial recognition technology into their operations. The web services run from a cloud enable organizations to perform biometric comparisons without installing software. BaaS allows banks to acquire biometric capabilities without investing in a custom solution and paying monthly subscription fees instead of up-front licensing payments. (See How Cetera’s Risk Profiling With Facial Recognition Can Turn Any Advisor into Dr. Phil)
Facial recognition replaces most of the login and verification processes currently in use that have been compromised by hackers. Passwords, which can be too weak, or cracked, or copied from other systems, and security questions, which can be defeated using social media scans.
Some of the most popular vendors of facial recognition software for banks include:
- BioID – Offers ‘Biometrics as a Service’ providing multimodal biometric technology that can identify users through face, eye and voice recognition.
- FaceFirst – Claims that their systems can scan every person entering a bank location and identify anyone with a criminal record. The firm reports that 1 in 3 bank robberies are carried out by a repeat offender.
- FinFace Pro – Face biometrics that can be deployed to branches and ATMs as well as mobile devices to secure remote banking services.
- Sight Corp – Provides a software development kit (SDK) that can be used to integrate their facial recognition technology that they claim can detect and measure facial expressions such as happiness, sadness, disgust, surprise anger, and fear.
I’m waiting for the first facial recognition payment vending machine to be setup in the US. Will it be accepted as an amazing advancement towards cashless/cardless/deviceless society or rejected due to its creepy authoritarian Big Brother-like control potential?
US Leads the AI Race with China Closing Fast
Center for Data Innovation: #US leads #AI race, with #China closing fast and #EU lagging https://t.co/1d0o0D8l1e #fintech #ArtificialIntelligence #MachineLearning #DeepLearning @OBRIEN @VentureBeat @psb_dc @pierrepinna @HaroldSinnott @Ronald_vanLoon @YuHelenYu @diioannid pic.twitter.com/DhsHxZqWYt
— Spiros Margaris (@SpirosMargaris) August 19, 2019
I was shocked to hear that the US still leads in artificial intelligence! I thought that China had already passed us, based on their tremendous investment over the past few years and the number of AI startups and innovations being pumped out by the Chinese economy.
This report from the Center for Data Innovation compares China, the EU, and the US in terms of their relative standing in the AI economy by examining six categories of metrics:
- Talent (US leads)
- Research (US leads)
- Development (US leads)
- Hardware (US leads)
- Adoption (China leads)
- Data (China leads)
Out of 100 total available points in this report’s scoring methodology, the US leads with 44.2 points, followed by China with 32.3 and the EU with 23.5.
There has been growing concern among U.S. tech companies and policymakersthat China’s initiative to make it dominant in AI by 2030 is allowing it to dictate this critical field.
The US lead in AI for several reasons:
- The US has the most AI start-ups, with its AI start-up ecosystem having received the most private equity and venture capital funding.
- The US leads in the development of both traditional semiconductors and the computer chips that power AI systems.
- The US produces the highest-quality AI scholarly papers, even though the EU and China publish higher volumes.
- The level of AI talent in the US is more elite, even though we have less actual people in AI.
I’m seeing a trend here of quality of quantity that’s keeping the US ahead in AI. But how long can that last? Will we be overrun by sheer numbers from China considering that their population is over 4X of ours? Or will our qualitative edge overcome the mass wave attack?
The report authors recommend that the US should focus on policies that grow its domestic talent base, enable foreign AI talent to immigrate, and increase incentives for research and development (R&D). But is anyone listening?
10 Most Important Moments in AI
— JAY PALTER (@jaypalter) September 20, 2019
I love to see my favorite science fiction author taking the top spot as the number one most important moment in AI for when he wrote the three laws of robotics as part of a short story in 1942. But was that really the most important moment in AI? It may have sparked a lot of imaginations, but no one actually leveraged these rules to build actual AI technology, let alone robots.
My vote for #1 would be the development of the first step towards what we know of as neural networks, which is called a perceptron. A perceptron is a binary identifier that can be used to decide if an image is a cat or a dog, for example.
A psychologist named Frank Rosenblatt built this electromechanical model of a perceptron in 1957, which today sits in the Smithsonian. It was an analog device that consisted of a grid of light-sensitive photoelectric cells connected by wires to banks of electrical motors with rotary resistors. Rosenblatt developed a “Perceptron Algorithm” that directed the network to gradually tune its input strengths until they consistently correctly identified objects, effectively allowing it to learn.
This was the first example of a working neural network and the perceptron concept is still in use in AI systems today.
In 2019 alone, hedge funds are estimated to spend in excess of $1 billion on alternative data and close to double the amount in 2020, according to web intelligence company YipitData – Alterntivedata.org. This could turn wealth and asset management on its head since 71% of asset managers believe that they get an edge over competitors using non-traditional data.
Some of this alternative data includes geospatial data that shows the proximity of competitors, credit card transactions, supply chain & logistics data, all of which can be used to evaluate new and existing investment opportunities. Around half of investment managers are currently using alternative data with another quarter planning to do so in the next 12 months, according to the research.
A digital arms race is under way. The weapons are ever more sophisticated algorithms and mountains of data. Who will be left standing when the dust settles?