AI Articles and Analysis in Finance

Proving the economic value of AI projects remains paramount to the success and continuation of any machine learning-related project. As more companies adopt AI technologies, measuring these projects' success is increasingly important – unfortunately, proving this value is proving anything but straightforward.

PwC (PricewaterhouseCoopers) is a professional services firm that provides a range of consulting, audit, and advisory services to clients worldwide. Per its annual report, the company's revenue was USD 50.3 billion, employing 328,000 people in 152 countries.

Bank of Montreal (BMO) is a North American bank that provides personal and commercial banking, global markets, and investment banking. The bank caters to over 12 million customers with over 1000 branches. Per the bank's 2022 Annual Report, BMO earned approximately CAD 33.71 billion in revenue.

Although not often regarded as a technological first-mover, the insurance industry has recently seen robust, even rapid, adoption and deployment of AI capabilities, particularly in those related to natural language processing (NLP). 

Traditional investors have several mechanisms for achieving their desired portfolio risk level. One of the most common methods involves the use of correlations. The more well-known 60/40 and 80/20 models distribute investments in a specific ratio of equities/bonds. These models are based on correlations between markets.  

While machine learning might be as close to a household name as any AI capability in 2023, any serious historian will tell you the technology itself is nothing new. The term 'machine learning' was coined in an IBM study on computer gaming and AI dating back to 1959.

Voice of the customer (VoC) is a term used in modern business practices to describe customers' feedback about their experience with the products and services both inside and outside any official business channels to make their opinions known. Businesses capture the voice of the customer data to provide the best possible customer experiences. The process needs to be proactive and innovative to capture the customers' changing requirements with time. 

As of 2023, AI and machine learning are widespread technologies in various applications and use cases throughout the financial services industry. Both demand factors in profitability needs, competition, and regulations – along with supply factors, such as technological advances and the availability of financial sector data – are primary drivers of adoption for these technologies.