Financial Services Third Wave of Innovation: AI & Machine Learning (2024)

“The financial services industry will see more change in the next 10 years than it has in the last 100. And that transformation is being driven by a group of smart insurgent startup companies. The Fintech 250 are the most promising of these insurgents.” — CB Insights CEO Anand Sanwal. According to CBInsights, there are now more than 250 tech startups redefining the financial services industry.

Financial Services Third Wave of Innovation: AI & Machine Learning (1)

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To learn more about the innovation in the financial services industry, retail investing, and analysis of the largest technology companies in the world, Ray Wang and I invited Ophir Gottlieb to our weekly show DisrupTV, where we cover the latest and most impactful leadership, business and technology disruptive trends.

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Ophir Gottlieb is the CEO and Co-founder of Capital Market Laboratories. Ophir Gottlieb is the CEO & Co-founder of Capital Market Laboratories. Mr Gottlieb’s mathematics, measure theory and machine learning background stems from his graduate work at Stanford University. He is a former option market maker on the NYSE and CBOE exchange floors and has been cited by dozens of various financial media including Reuters, Bloomberg, The NY Times and the Wall St. Journal. Gottlieb is the inventor of the Forensic Alpha Model (FAM) and a co-inventor of Accounting and Governance Risk Model (AGR), both now owned commercially by MSCI. FAM and AGR are used by asset managers worldwide with over $1 trillion of assets under management. The FAM model has made Gottlieb one of the most recognized names in all of quantitative finance. Gottlieb is very active and accessible on Twitter (@OphirGotlieb) and a great follow.

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Gottlieb founded Capital market Laboraties (CML) in order to break the information asymmetry that has benefited the few at the expense of the many for far too long. CML research sits next to Goldman Sachs, JP Morgan, Barclays, Morgan Stanley and every other multi billion dollar institution as a member of the famed Thomson Reuters First Call. The difference according to Gottlieb is that while those investors pay upwards of $2,000 a month for their live terminals, CML is created to be the anti-institution, breaking the information asymmetry by providing unprecedented access to information and insights for pennies on the dollar. CML is democratizing information for investors.

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The 3 Waves of Innovation in the Financial Services Tech Industry

According to Gottlieb, there have been two waves of innovation in financial services technology, and we are positioned for the third wave.

  • Wave 1 - The Internet: The dawn of the Internet was the first wave of innovation in fin tech. In the early 1990's many people were still get stock prices from Newspapers a day later than the price. With the Internet came access to stock quotes and stock trading -- both of which were tightly held by the finance industry as barriers to entry for a retail investor.
  • Wave 2 - Democratization of Data: This started in 2009 after the great recession and really took hold about two-years ago: the democratization of all financial data -- that is financial statements, events, insider trading, and more. As small fin techs scraped the SEC filings, the data sets became available to smaller firms to re-publish in their own portals. People were then freed from depending on the e-broker for data and the birth of fintech came about.
  • Wave 3 - Turning Big Data to Knowledge using Machine Learning/AI: With all the data accessible from many sources has come a wave of poor journalism, sophom*oric analysis and generally faulty conclusions. This is a necessary step in order to move forward yet again. The third wave will be the consumption of this data and the transformation of it into knowledge. This echoes the same transformation in technology turning big data into knowledge using machine learning broadly and AI specifically.

There are now 1907 artificial intelligence (AI) startups that have raised $21.2 billion in venture capital funds across 13 AI categories, according to Venture Scanner.

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Venture Scanner

Four False Narratives About Apple

Gottlieb researches top brands and most valuable technology companies like Amazon, Google, Facebook and Apple. We asked Gottlieb to share his point of view on Apple. “Broadly speaking mega tech has never had more control over the technology landscape than it doe today. Apple, as the top "click catcher" for news organizations has been suffocated with sophom*oric analysis from well meaning journalists that lack the knowledge in both technology and finance,” said Gottlieb.

CML’s view of Apple is that there have been 4 false narratives that have been broadly accepted due to misinformation and it has directly cost retail investors.

1. The smartphone market is not shrinking - The smartphone market is growing, and it is not the new desktop. The PC market has shrunk for seven years in a row. The smartphone market is expected to grow 44% from 2014 to 2019. If you measure success by profits, the Apple iPhone, introduced in 2007, is the most successful technology in the world.

Financial Services Third Wave of Innovation: AI & Machine Learning (4)

IDC / Statistica

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Financial Services Third Wave of Innovation: AI & Machine Learning (5)

Statista

Financial Services Third Wave of Innovation: AI & Machine Learning (6)

IDC / Statista

2. Apple is not a “phone company” - 55% of revenue in the latest Q came from iPhone, down from 63% in the same period 2-year ago. Apple Services is 16% of revenue, growing at 22% CAGR and is now the size of Facebook. Facebook has 98% from one product, ads. Google has 80% of revenues from ads. Apple Services - Apply Pay, iTunes, App Store - is now 1/6 the Apple total revenues; the Apple Services revenues is the size of Facebook! Apple Services is also growing 22% per year, per Gottlieb.

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Mac Rumors

Financial Services Third Wave of Innovation: AI & Machine Learning (8)

BI / Statista

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3. The Apple Watch is not a failure - The first version of the Apple Watch sold more units than the first iPhone, and year two as very similar. With the wearable turning to LTE (no longer need the watch to be tethered to the iPhone), combined with AirPods (still on a one-month back-order) Apple has by far the best selling wearable technology in the world. Versions 1 and 2 of Apple products are not the large successes people see today – it’s rounds 3 and 4 that create the disruption.

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BI Intelligence

4. Apple does have a “next big thing” - Tim Cook has said out loud that they are health care tech and augmented reality (AR). These two markets are measured in trillions of dollars and the scale of Apple.

Rapid Innovation Velocity Means The First Mover Advantage is Now a Disadvantage

First to market has rarely been and continues not to be a measure of success nor it necessarily an advantage . Technology evolves fast enough that second, third and fourth comers to market often times capture the investment that the first mover made to make a market. Gottlieb references cloud computing and media companies as examples of how first mover advantage no longer exists. First movers now get punished. The top five largest companies in the world (Apple, Alphabet, Microsoft, Amazon and Facebook) are sty-filling innovation according to Gottlieb by developing platforms for imitation. With the exception of Telsa, a first mover on electric and autonomous technologies, is an exception to this trend.

More Than Half of Americans Do Now Own Stock Investments

According to a Bankrate Money Pulse survey, more than half of Americans don’t own any stock investments at all, potentially missing out on a big investing opportunity to build retirement savings and overall wealth.

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Gottlieb believes that this is rationale behavior. Retail investors are acting normally by not investing based on their limited knowledge of the market. Investing hard earned money in the stock market based on limited knowledge is irrational. Retail investors must find trusted resources before investing. The third wave of innovation will enable conversion of data to knowledge, unlocking trillions of dollars of potential for retail investors. “Find the places that turn data into information and then knowledge that empower you to make your own decisions while standing firmly in a position of expertise, not hobby,” said Gottlieb.

According to the same survey, only 26% of people under 30 invest. “This is not a new trend, it’s the same trend that has persisted. The first two waves of fintech disruption were necessary to get to wave 3. But waves one and two were not the disrupters that wave 3 will be – it’s the creation of knowledge that will change this dynamic,” said Gottlieb.

Please watch our video conversation with Ophir Gottlieb to learn more about retail investing and the impact of machine learning and AI on the future of fin tech. Gottlieb is also a brilliant follow on Twitter (@OphirGottlieb) and his blog.

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Financial Services Third Wave of Innovation: AI & Machine Learning (2024)

FAQs

How is AI being used in financial services? ›

What is artificial intelligence (AI) in finance? Artificial intelligence (AI) in finance helps drive insights for data analytics, performance measurement, predictions and forecasting, real-time calculations, customer servicing, intelligent data retrieval, and more.

What is the third wave of FinTech? ›

The third wave of FinTech (FinTech 3.0), built upon the first two waves of digitization, smart automation, “platformization” and disintermediation is nurturing a critical new surge of innovation.

What is the future of AI in finance? ›

Future of AI in Finance

Many experts predict that AI will continue to revolutionize the finance industry in the coming years. We'll likely see AI used in many complex ways to analyze data, identify patterns and insights, automate processes, and make many recommendations.

What is AI and ML in FinTech? ›

Machine learning (ML) and artificial intelligence (AI) are rapidly changing the fintech sector. These technologies are being utilized to streamline processes, enhance decision-making, and customize the experiences of customers.

How does JP Morgan use AI? ›

J.P. Morgan has been using the underlying AI-powered large language models for payment validation screening for more than two years. It also speeds up processing in other ways by reducing false positives and enabling better queue management.

What are the problems with AI in financial services? ›

The complexity of AI models often shrouds decision-making processes in obscurity, leading to a lack of transparency. For example, as a customer, I may not understand how or why AI made that financial decision for me: Should I trust that this is the right decision?

What is the biggest fintech company in the world? ›

Visa Paytech

Is fintech the next big thing? ›

McKinsey's research shows that revenues in the fintech industry are expected to grow almost three times faster than those in the traditional banking sector between 2023 and 2028. These trends are also coinciding with—and in many ways catalyzing—the maturation of the fintech industry.

Which is the 3rd largest fintech ecosystem? ›

The Union Finance Minister also noted that India's Fintech ecosystem is the 3rd largest in the world and growing at 14% CAGR and that RBI recently floated a draft Framework for recognition of Self-Regulatory Organisation (SRO) for the Fintech sector for stakeholder's consultation.

Will finance be replaced by AI? ›

The future of finance roles

This means that finance professionals must adapt to these changes and embrace the complementary nature of humans and technology. While some tasks may become automated or delegated to AI systems, this does not mean human jobs will be replaced entirely.

Will AI take over the finance industry? ›

According to American Banker's Predictions 2024 survey published this month, 75% of finance industry professionals think AI will change the nature of some jobs, but won't replace human workers.

How is AI changing financial services? ›

AI is already making important financial decisions, such as handling credit card applications, and it's making rapid inroads in the public and private sectors. The technology can help ensure that banks don't misbehave by, for example, taking advantage of clients or allowing fraud or money laundering, he said.

How do banks use AI? ›

How is Ai used in Banking? AI is used in banking to enhance efficiency, security, and customer experiences. It automates routine tasks like data entry and fraud detection, reducing operational costs. AI-driven chatbots provide 24/7 customer support.

What is the best use of AI in fintech? ›

Importance of the Integrating AI in Fintech
  • Efficiency and Cost Savings through Automation. ...
  • Enhanced Decision-Making Through Analytics. ...
  • Superior Customer Service. ...
  • Improved Risk Management. ...
  • Fraud Detection. ...
  • Risk Assessment. ...
  • Chatbots and Virtual Assistants. ...
  • Algorithmic Trading.

How is AI and ML used in finance? ›

Artificial intelligence (AI) in finance is the use of technology like machine learning (ML) that mimics human intelligence and decision-making to enhance how financial institutions analyze, manage, invest, and protect money.

How will AI impact the financial services industry? ›

"By leveraging AI, financial institutions are better equipped to really transform the decision-making process to be more accurate, efficient, and successful.” Many financial institutions “make risk, capital allocation and underwriting decisions, based on as little as 10% of the data available to them,” says Liu.

How AI is changing the financial services industry? ›

In recent years, AI has started to revolutionize the financial industry in ways we could only have dreamed of in the past. Machine learning algorithms can now analyze vast data sets in real time, providing deeper insights into market trends, risk assessments and customer behaviour.

How is AI used in banking and finance? ›

AI algorithms can analyze vast amounts of data in real time, enabling banks and financial institutions to detect suspicious activity and prevent losses. The enhanced understanding of fraud patterns empowers machine learning models to detect suspicious activities more accurately and effectively.

How artificial intelligence is reshaping the financial industry? ›

Enhanced Credit Risk Assessment

AI-powered credit scoring algorithms discover patterns and predict default risk, allowing for more informed decision-making and real-time risk management. This innovation of AI in financial services contributes to competitive rates and effective risk management.

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