AI Articles and Analysis in Finance

Banks and investment organizations usually have large research teams that are tasked with investigating and monitoring events that might affect financial trading markets. Investment research is a business function in these firms and is a fundamental part of what is required from analysts, equity managers, investors, and traders.

The insurance industry is making use of various artificial intelligence applications to solve business problems, but perhaps the most versatile is predictive analytics. The ability to aggregate data from disparate sources for business intelligence allows business leaders in insurance to inform important decisions across departments.

Episode Summary: Close to a year ago, we had an interview here on the AI in Industry podcast with Jeremy Barnes of Element AI. We visited their headquarters in Montreal, and we'd interviewed Yoshua Bengio a couple years before that.

Decision-makers in the banking sector have a unique set of business intelligence needs, and artificial intelligence has been on the radar of banking executives for several years now. It follows that AI and machine learning would find their way into business intelligence applications for the banking sector.

Facial recognition software is making its way into the mainstream, with consumer applications such as the ability to unlock one’s smartphone with their face. The banking sector has been at the forefront of enterprise adoption of AI since machine learning became the hot topic of the business world in the early years of the decade; as such, it makes sense that facial recognition technology would start to make its way into banking.

There are several companies claiming to offer AI solutions to banks and financial institutions. We found that these solutions are intended to help banking and finance companies with at least one of the following business problems:

Episode Summary: Many machine learning applications in business can be boiled down to some form of decision support. There are big decisions like deciding whether or not to merge or acquire another company, and there might be smaller decisions like whether or not a tumor has enough traits that make it seem like it's worth a surgical procedure or if it's worth leaving alone.

According to a 2017 report by the Financial Stability Board, artificial intelligence (AI) and machine learning firms managed assets of over $10 billion in 2017, with further growth projected in the next five years. The reasons for this are clear; AI can now present wealth managers with new capabilities to enhance and further personalize their services, at scale.