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Artificial Intelligence – Here to Stay

Given recent updates related to Blockchain and other innovations in the financial sector, technology is close to again significantly altering the way we do and see things. Artificial Intelligence (AI) is a driving force in these advancements, and is primed to have a major impact on the financial sector.

While we are only beginning to understand what AI can do, many Financial Institutions, such as Goldman Sachs and UBS, have already begun to anticipate the potential impact of AI on their respective businesses and the industry as a whole. Many have gone a step further and started to invest in solutions that leverage the technology in the hope that it will help them improve existing processes and make them become smarter as a business. For every business, the critical question remains, “Are we doing enough?”

A New Wave of Technology

“As we look ahead to 2016, one of our major priorities will be to aggressively pursue the innovative technologies that we have been making investments in.”

Daniel Pinto (CEO of the corporate and investment bank at JP Morgan)


In recent times, technology firms have been making noise with innovative and disruptive ideas and everyone has taken notice. In fact, 61% of global executives and 78% of U.S. executives are concerned about the speed of technological change in their industry.[1] In the banking and capital markets industry, 81% of the executives have expressed concern. These concerns stem principally from a fear of missing out on significant opportunity and change – technology advances are seen as the global trend most likely to transform wider expectations of business.1 It is a combined sense of fear and opportunity that is driving businesses to invest heavily in advanced technology as it becomes increasingly clear that harnessing technology, such as adopting advanced applications and tools, is not only an advantage, but also a necessity in an increasingly competitive environment.

The reasoning for investing in the advanced technology is clear - companies with high technology intensity[2] have, on average, higher gross margins than those who do not (see exhibit 1). In banking and financial services, companies with the highest gross margins have technology intensities and margins that are roughly double those of average performers. In the insurance sector, top-performing companies enjoy gross margins that are more than three times the average performers and technology intensities that are more than 50% higher.3 Financial Companies being squeezed by ever-increasing competition and stringent regulations are looking to leverage comparative advantages they can access, and technology is an effective tool that can be leveraged.

Innovative technologies will help firms achieve increased automation as well as operational efficiency. Among the innovative technologies which are bound to profoundly impact operations across all industries and notably in the Financial Services industry, is Artificial Intelligence. The definition of Artificial Intelligence is “The theory and development of computer systems able to perform tasks that normally require human intelligence”. Many would take this definition further as today we are finding that not only can machines perform an increasing number of tasks in the workplace, but they can perform them better. With advances in technology and AI, firms are going to have no choice but to switch an increasing number of their tasks to machines in order to reduce costs and increase efficiency.

The Rise of Artificial Intelligence

According to CB Insights, 2016 should be a record year in terms of total Artificial Intelligence venture financing deals and in dollar volume (200 AI venture financing deals had been completed from January to June 2016 totaling $1.5B). 2015 saw 397 AI venture financings totaling $2.3B.[3] Most of the deals are at a series B or C stage, indicating that startups in this space are beginning to see success.

In the Financial Services industry, firms like Goldman Sachs, JPMorgan and Barclays have taken major steps towards implementing AI in different areas.

It would not be farfetched to see exponential growth in the number of AI tools and applications used in the next 5 years. In fact, Citigroup has predicted that nearly 47% of U.S. jobs will be at risk with the introduction of AI.[4] While many of these are going to be jobs that consist of “predictable physical work”, which do not significantly apply to the Financial Services industry, it is expected that many jobs involving data processing and collection will also be at risk[5]. Many financial companies are fully aware and thus have followed the efforts of tech firms like Google (DeepMind) and Facebook (Jarvis) by becoming early adopters of new generation AI technology that will automate human tasks.

Artificial Intelligence has already made an important impact on the investment industry, and is expected to go much further, especially with the rise of robo-advising. Robo-advising refers to the use of automation and digital techniques to build and manage investment portfolios. With the help of a financial algorithm and a digital platform, a tool can now provide sophisticated wealth management services. With advancements in Machine Learning[6], many expect machines to soon outperform even the most sophisticated human financial advisors. That, along with the simplicity that comes with robo-advising, has many believing that machines and algorithms will manage over $2 trillion dollars by 2020[7]. While this only represents one field where machines are replacing humans, it is a clear illustration of an industry transformation that is fueled by AI.

AI and Regulatory Challenges (Regtech)

“By making compliance less complex and capacity-demanding, Regtech solutions could free capital to be put to more productive uses.”  

The Institute of International Finance (IIF)


Artificial Intelligence, along with other innovative technologies, will also significantly alter the way many Financial Institutions approach compliance and regulation. Today, global banks such as HSBC, Deutsche Bank and JP Morgan are each spending over $1 billion a year on regulatory compliance and controls. The Financial Services industry is a data-driven industry, and Financial Institutions are continuously challenged to adequately manage a massive amount of data. The amount of data to which firms will have access will increase, especially as new technologies that leverage “Big Data” and platforms such as the “Internet of Things” become more available. The implementation of applications that leverage new AI could help better manage this data challenge by streamlining processes.

For many companies, the fusion between compliance and technology (via AI) has already begun. In November 2016, IBM, known for its move into cloud computing and Artificial Intelligence (notably with IBM Watson), completed its acquisition of a management and regulatory compliance consulting firm. IBM’s vision is to address the considerable operational effort and manual costs that come with escalating regulation and risk management requirements. Likewise, in an effort to bolster their compliance efforts, many major banks have also taken a step in leveraging new AI technology.

Last year, Citigroup used an artificial intelligence system from Stanford University spinout Ayasdi to help it pass the US Federal Reserve's stress test that it had failed the previous year.[1] With help from Ayasdi and their AI driven CCAR stress test software, Citigroup adopted a new approach which helped them identify, validate and select variables and models which ensured that business logic was built in the process and that the bank had accurate, defensible revenue forecast models that stood up to the Federal Reserve's scrutiny. Ayasdi has secured further funding and continues to experience significant growth as well as being the recipient of many awards such as in the RiskTech100 Artificial Intelligence Category.

Another enterprise which has found success within the Financial Services industry is Digital Reasoning. Goldman Sachs and UBS are among global banks that have invested in and are using Digital Reasoning’s technology to monitor employee and client behavior.[2] Digital Reasoning uses machine learning technology to scan millions of emails, instant messages and texts to map out ordinary behavioral patterns among the employees of their clients. Compliance staff for each institution then review the behavior and transactions that are deemed “out of the ordinary” by the application. The CEO of Digital Reasoning claims that Digital Reasoning is far more efficient than previous surveillance systems as it lowers false positive rates on alleged wrongdoing by between 95% and 99%. Today, Digital Reasoning has raised over $70 million from Goldman Sachs, Credit Suisse, NASDAQ, and the CIA’s venture arm, among others.9

Other Compliance areas of focus for Financial Institutions include conduct monitoring, customer identity, and risk data aggregation. There are new applications that help with each of these areas. Sybenetix, like Digital Reasoning, helps with conduct monitoring by leveraging AI to learn about each trader’s personality so as to be more precise when flagging suspicious trading and raising costly alarms.[3] With regard to customer identity, Onfido leverages machine-learning technology to deliver “next-generation background checks” and make it easier for banks to onboard customers.[4] Lastly, when it comes to risk-data aggregation, Suade provides a new tool that gathers and analyses a firm’s information on capital and liquidity for use in internal models and in reports to regulators.[5]

Artificial Intelligence – Issues and Challenges

The rise of Artificial Intelligence will not be smooth. While we are entering the early stages of AI, there are still many unanswered questions that businesses and governments need to consider. For companies, the challenges will more likely come with the ever-changing business landscape that will be deeply impacted by the introduction and implementation of AI Firms. Companies will need to react quickly to their respective new landscapes and begin to anticipate future changes. A new landscape means that companies will have to find new ways to differentiate and compete. Meanwhile, their AI driven systems will need to remain user-friendly, transparent and comprehensive.

Furthermore, companies and governments will have new concerns and challenges related to security and privacy. Artificial intelligence is a powerful tool that could have an adverse effect if it is used incorrectly, if it is compromised, or subject to hacking and/or tampering. Some of the risks include users relying on inaccurate recommendations or malicious advice or having some of a user’s personal information shared illegally. These risks could lead to significant financial and reputational damage to the firm involved.

From a firm perspective, new policies and procedures will need to be developed in order to manage the new risks that come with AI Governments, and regulating bodies meanwhile, will need to consider new regulations to ensure that firms are adequately mitigating risks. Before doing so however, regulators will need to answer important questions, such as; does a robot advisor need a certification; or how should Financial Institutions and third parties that develop and run AI applications be chartered, audited and assessed?

We believe that the conversion of Big Data and Artificial Intelligence is going to magnify the privacy and security challenges and risks. Leveraging new platforms such as the Internet of Things, firms will have access to an increasing amount of data. With AI, this data could be used to provide advice and recommendations but there could also be a risk for it being used for purposes that could be considered an invasion of a person’s privacy. With the emergence of these new technologies, a broad range of new developing conflicts, trends and ethical challenges will be introduced, and solutions will need to be debated, recognized and implemented.

Next Steps

Businesses and regulatory bodies should continue to embrace this new wave of technology innovation. Businesses should ensure they leverage their existing technology investments and embrace new technologies in order to overcome operational challenges and make the most of the data that is available to them. Businesses will need to actively research new technologies and the solutions that they bring. Regarding regulation, businesses should clearly identify and understand upcoming data and reporting requirements as well as the next set of regulations and look at new innovative tools and applications that may help tackle the expected areas of regulatory challenge. Regulators should amend and create regulations that account for the current and potential implementation of new AI technology.


“We're really, I think, at the very beginnings in the world of recognizing and seeing how you can take AI, artificial intelligence, and really provide business applications around it”

                                 Don Duet (Co-head of the Goldman Sachs’ technology division)



[1] PricewaterhouseCoopers. “19th Annual Global CEO Survey / January 2016 – Redefining Business Success in a Changing World CEO Survey.” http://www.pwc.com/. N.p., Jan. 2016. Web. Dec. 2016

[2] Tech Intensity is a phrase Rubin Worldwide has coined that refers to the level of technology spending as it relates to business results, both as a percentage of revenues and as a percent of operating expenses

[3] "Artificial Intelligence Explodes: New Deal Activity Record For AI Startups." CB Insights - Blog. CB Insights, 15 Aug. 2016. Web. 01 Dec. 2016

[4] Williams-Grut, Oscar. "Robots Will Steal Your Job: How AI Could Increase Unemployment and Inequality." Business Insider. Business Insider, 15 Feb. 2016. Web. 01 Dec. 2016

[6] A type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data

[7] Regan, Michael P. "Robo Advisers to Run $2 Trillion by 2020 If This Model Is Right." Bloomberg.com. Bloomberg, 18 June 2015. Web. 01 Dec. 2016

[8] Ayasdi. "After Yesterday, CCAR Less Stressful for Citigroup | Ayasdi." Ayasdi. N.p., 16 Oct. 2015. Web. 01 Dec. 2016

[9] Gara, Antoine. "Wall Street's Big Brother: The AI Software Goldman Sachs And Steve Cohen Are Using To Track Traders." Forbes. Forbes Magazine, n.d.

[10] "Sybenetix for Market Surveillance and Compliance." Sybenetix for Market Surveillance and Compliance. N.p., n.d.

[11] "Onfido | Identity Verification and Background Checks." Onfido | Identity Verification and Background Checks. N.p., n.d.

[12] Labs, Suade. "Suade - Financial Regulation for the Future." Suade - An Open Platform for Financial Regulation. N.p., n.d

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