How AI is Being Used at Investment Banks – Market Data Collection


As part of our coverage on the use of AI in finance, CFTE takes a look and examines the impact of implementing such disruptive technology on various industry verticals. 
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For all the hype surrounding the potential of artificial intelligence in investment banking, we should not forget that at its core is the ability to automate highly tedious and mundane tasks. Not only does this free up human resources to focus on more strategic or important tasks, on a fundamental level it can also reduce costs and overheads for the bank. The example of Sigmoidal is a good one – the New York based firm was founded in 2016, offering consulting services for businesses that want to implement and harness the power of AI.

One such use of Sigmoidal is to optimise market data collection procedures without the need for humans to oversee this. For example, an investment firm may want to keep abreast of trends and changes in market developments accurately and quickly – this is where Sigmoidal comes in to automate the process of collecting information. Web scrapers can rapidly aggregate data from several sources, with the platform then making use of its algorithms and AI text classification to isolate the most relevant pieces of data for its clients.

The platform also claims it can use AI to identify specific names or text during extraction, which helps to isolate key details such as company names, employee positions and more. This can be particularly useful for investors, with such sensitive information often affecting the stock market positively or negatively depending on the sentiment of the news.

Such technology is crucial to introducing banks and other financial institutions to the transformational power of AI and machine learning. In fact, it could even act as an educational motivator where companies are first introduced to the power and potential of AI, and may be open to more implementations of such technology in the future.

This is one of the challenges highlighted by Ayesha Khanna of ADDO.AI in the CFTE AI in Finance course, saying that “people don’t understand how to use AI” or “how to apply it to their businesses”. In banks where many senior executives are yet to be convinced, automation could be the first step needed to open minds to the inevitable implementation of AI and machine learning industry-wide.

About the AI in Finance programme

The CFTE AI in Finance course has been developed in partnership with Ngee Ann Polytechnic, a leading institute of higher learning in Singapore and features high-quality content taught by five senior lecturers and 18 industry experts.

With an easy to follow format, the course is perfect for busy professionals to understand the technologies behind AI and machine learning that are disrupting finance.

Click here to learn more about the CFTE AI in Finance course.


About CFTE

We are a platform supported by senior leaders from the largest institutions, startups and universities. We address the needs of professionals in finance to upskill in a rapidly changing industry being transformed by emerging technologies. More than 50,000 participants learn from our online courses, such as AI in Finance, Fintech Foundation or Extrapreneurship, a mini-MBA with fast growing startups such as Revolut or Shift Technology.

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Cover image from sloanreview.mit.edu

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