A recent analysis of European startups found that the vast majority of those who thought they were using AI services were not. This seems to be a good metaphor for the hype surrounding AI in recent years, and expectations have exceeded reality.
The latest report from the Alan Turing Institute explores whether there is such a gap between expectation and reality in the financial services sector. High-frequency trading firms (HFTs) are particularly interested in fully exploring the potential of algorithmic trading — after the technology sector — to report that financial services are the 2nd biggest cost in AI.
“The major users of AI in finance are HFT companies, but now applications are spread to other areas, including banks, regulators, fintech and insurance companies,” the author explained. “In the financial services industry, AI applications include algorithmic trading, portfolio composition and optimization, model validation, backtesting, robot-advising, virtual customer assistants, market impact analysis, regulatory compliance, and stress testing.”
AI in Financial Services
The report identifies three main applications of AI in finance today:
Fraud detection and compliance
Detecting fraud is interesting because banks and other financial services companies spend billions each year to deal with financial crimes, although cases of payment-related fraud have increased over the past few years. By massively increasing the data used to predict fraud, AI makes the process more efficient, but much faster. The report highlights examples from HSBC and NatWest, where AI is being used to improve their fraud detection rates.
Banking Chatbots and Robo-Advisory Services
Chatbots are the most prevalent AI technology in financial services today, designed to help customers manage their money more efficiently. Some of these applications are hosted on the financial institution’s platforms, while others are integrated with Facebook Messenger or Slack.
Algorithmic Trading
Algorithmic Trading (AT) is fast gaining dominance in global financial markets. It is estimated that the machines make up 70 percent of equity market trades, 60 percent futures trades, and 50 percent treasuries. The logic is clear: Computers can trade faster, more accurately, and in a way that ensures that both parties get the best price. While eliminating the potential for human error.
This paper provides an overview of the different types of artificial intelligence being used today, as you might expect from the Turing Institute, which is an excellent summary. The paper then explores some regulatory and policy barriers that need to be overcome, including the least of which is to ensure that policies and regulations can accelerate the pace of change in technology.
This is a field where the authors believe that AI also plays an interesting role, with the emerging tech market aiming to bring new technologies to the market.
To Know More: AI in Accounting & Finance — How AI Will Impact The Accounting & Finance Industry?
“Regtech employs Big Data and ML and is a growing field to reduce costs and increase effectiveness,” they say. “Researchers argue that ML methods can provide fast, accurate, and consistent judgments and streamline operations with minimal error.”
This belief in the ability to control AI probably explains why governments are investing so much in their AI capabilities, with Germany investing € 3 billion, but in a stable dance of both the technology landscape and the regulatory landscape, it is not a treadmill.
“Artificial intelligence in the financial services industry is still in its early days,” the authors concluded. “AI is ubiquitous in finance, with more challenges, including legal, ethical, economic, and social barriers. AI also continues to bring new complexities to the global financial ecosystem. As more and more data becomes available and computing power increases, AI programs become more complex.”
As a starting point in understanding some of these issues, the paper provides excellent grinding. It is worth reading for anyone interested in the subject.
Role of AI in Financial Services
A recent analysis of European startups found that the vast majority of those who thought they were using AI services were not. This seems to be a good metaphor for the hype surrounding AI in recent years, and expectations have exceeded reality.
The latest report from the Alan Turing Institute explores whether there is such a gap between expectation and reality in the financial services sector. High-frequency trading firms (HFTs) are particularly interested in fully exploring the potential of algorithmic trading — after the technology sector — to report that financial services are the 2nd biggest cost in AI.
“The major users of AI in finance are HFT companies, but now applications are spread to other areas, including banks, regulators, fintech and insurance companies,” the author explained. “In the financial services industry, AI applications include algorithmic trading, portfolio composition and optimization, model validation, backtesting, robot-advising, virtual customer assistants, market impact analysis, regulatory compliance, and stress testing.”
AI in Financial Services
The report identifies three main applications of AI in finance today:
Fraud detection and compliance
Detecting fraud is interesting because banks and other financial services companies spend billions each year to deal with financial crimes, although cases of payment-related fraud have increased over the past few years. By massively increasing the data used to predict fraud, AI makes the process more efficient, but much faster. The report highlights examples from HSBC and NatWest, where AI is being used to improve their fraud detection rates.
To know More: AI In Insurance — How the Coronavirus epidemic will effect AI innovation in the insurance sector
Banking Chatbots and Robo-Advisory Services
Chatbots are the most prevalent AI technology in financial services today, designed to help customers manage their money more efficiently. Some of these applications are hosted on the financial institution’s platforms, while others are integrated with Facebook Messenger or Slack.
Algorithmic Trading
Algorithmic Trading (AT) is fast gaining dominance in global financial markets. It is estimated that the machines make up 70 percent of equity market trades, 60 percent futures trades, And 50 percent treasuries. The logic is clear: Computers can trade faster, more accurately, and in a way that ensures that both parties get the best price. While eliminating the potential for human error.
This paper provides an overview of the different types of artificial intelligence being used today, as you might expect from the Turing Institute, which is an excellent summary. The paper then explores some regulatory and policy barriers that need to be overcome, including the least of which is to ensure that policies and regulations can accelerate the pace of change in technology.
This is a field where the authors believe that AI also plays an interesting role, with the emerging regtech market aiming to bring new technologies to the market.
“Regtech employs Big Data and ML and is a growing field to reduce costs and increase effectiveness,” they say. “Researchers argue that ML methods can provide fast, accurate, and consistent judgments and streamline operations with minimal error.”
This belief in the ability to control AI probably explains why governments are investing so much in their AI capabilities, with Germany investing € 3 billion, but in a stable dance of both the technology landscape and the regulatory landscape, it is not a treadmill.
“Artificial intelligence in the financial services industry is still in its early days,” the authors concluded. “AI is ubiquitous in finance, with more challenges, including legal, ethical, economic, and social barriers. AI also continues to bring new complexities to the global financial ecosystem. As more and more data becomes available and computing power increases, AI programs become more complex.”
As a starting point in understanding some of these issues, the paper provides excellent grinding. It is worth reading for anyone interested in the subject.
