AI Articles and Analysis in Finance

In 2017, Equifax’s systems were compromised by hackers, and the data of over 143 million Americans was exposed. Other incidents, such as the WannaCry and Petya ransomware scams, have highlighted the vulnerabilities in financial cybersecurity globally. According to the Global Banking and Finance Review, such cyber attacks have cost nearly USD 360 billion per year in losses for each of the last three years.

The insurance industry is looking to adopt artificial intelligence applications for a variety of business functions due to its access to large volume of customer data. According to our AI Opportunity Landscape research in insurance, approximately 46% of AI vendors in insurance offer solutions for claims and 43% offer solutions for underwriting. While there are other areas of insurance that could benefit from AI, it's clear that these two core insurance tenets are where the traction is with AI in the industry.

According to the Consumer Financial Protection Bureau, Americans filed more grievances about debt collections than about any other financial incident. Of the 316,810 complaints received by the CFPB about debt collection in 2017, the most common was, “Continued attempts to collect debt not owed,” which was cited by 39 percent of grievance filers.

Today, two highly discussed disruptive technologies are artificial intelligence and blockchain. In this piece, we aim to highlight projects where AI and blockchain might be used together to support the B2B environment.

According to the Automated Imaging Association (AIA), machine vision is a combination of hardware like cameras, image sensors, and image processing software that can help automate applications like inspection and analysis by allowing machines (such as robots) to ‘see’ their surroundings.  

Stockbrokerage might be viewed by investors as a traditionally human-based service allowing them to buy and sell equities. When looking at the shift in how stock brokerage is different today compared to the early 2000s, the largest change seems to be in software-based automation. Put simply, a lot of what was being done by humans (such as executing trades, giving advice to investors, discretionary trading) can now be done through software.

Episode Summary: In this episode of the podcast, we interview AIG’s Chief Data Science Officer, Dr. Nishant Chandra, about natural language processing (NLP) for internal and team communication. Dr. Chandra talks about how NLP can help with sharing documents with specific team members whose roles warrant viewing those documents.

In a 2017 symposium in Harvard’s Institute for Applied Computational Science, R. Martin Chavez, Deputy Chief Financial Officer of  Goldman Sachs explains that the company’s US cash equities trading division used to employ over 600 human traders back in 2000. Today that number is down to just two human traders, with the rest of the jobs being taken over by automated trading platforms that are managed by around 200 computer engineers.