AI Articles and Analysis in Finance

Robotic process automation, or RPA, is a technology used across multiple industries to automate business processes. RPA software involves what are known as "software robots" to handle repetitive tasks traditionally handled by human employees. That said, there are no actual robots involved in the way one might see in manufacturing or heavy industry.

In the past few decades, insurance companies have collected vast amounts of data relevant to their business processes, customers, claims, and so on. This data can be unstructured in the form of PDFs, text documents, images, and videos, or structured, organized and curated for big data analytics.

The International Financial Reporting Standards (IFRS) and Financial Accounting Standards Board (FASB) have introduced new financial and accounting standards which require financial institutions to compile all their lease agreements and accounting reports electronically. These new standards may require significant changes to a financial institution's lease and contract management processes.

NASDAQ estimates more than $5 trillion is traded every day in what it describes as “the most actively traded market in the word:” foreign exchange, or forex. Business leaders might expect AI to make its way into the forex world the way it has into finance and banking broadly. Most companies claim to assist foreign exchange traders by predicting when to trade or hold onto currencies. As it turns out, however, Most of the AI vendors in the forex space are in fact only claiming to use AI. There is strong evidence to suggest that their claims are illegitimate.

Some financial institutions have begun investing in departments that focus on artificial intelligence and machine learning applications that could determine their customer's sentiments towards market developments. These applications fall under the category of sentiment analysis. We have previously covered some of the top the machine learning applications in finance. In this report, we focus on AI-based sentiment analysis applications for the finance sector.

Our AI in Banking Vendor Landscape and Capability Map report details the state of various AI approaches and capabilities within specific banking functions, measuring them on their level of funding, evidence of ROI and adoption at large banks, and more. In this article, we discuss how and where banks are using natural language processing (NLP), one such AI approach—the technical description of the machine learning model behind an AI product.

Natural language processing, (NLP) is one AI technique that's finding its way into a variety of verticals, but the finance industry is among the most interested in the business applications of NLP. In fact, according to our AI Opportunity Landscape research in banking, approximately 39% of the AI vendors in the banking industry offer solutions that involve NLP. 

AutonomousNEXT released a report on the opportunity that AI might create in the banking and financial services industry. The report estimated that by 2030, the potential cost savings by applying AI in banking, investment management, and insurance were $490 billion in front office operations, $350 billion in middle office, $200 billion in the back office operations.