AI Articles and Analysis in Finance

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. 

The concept of customer experience is reaching new heights with technological advancements. Mobile technology and applications are increasingly becoming part of consumers' lives. The impact of technology on customer experience is growing tremendously. Fintech companies are coming up with innovations to serve customers with the best. One such initiative to bring increased value to customers is open banking. 

With so much competition in the market, it is easy for customers to switch their banking partners. Data and analysis of customers' financial data help banks reduce churn by channeling insights behind personalized solutions and offerings for their customers. Yet the demand for more personalization in financial services hardly stops there.

AI and machine learning are increasingly used across use cases throughout the insurance industry. Examples include automating underwriting, streamlining claims processing, improving customer service, and predicting customer risk.

A cyber attack on a large-scale government IT services provider, SolarWinds, in 2019 allowed the hackers behind it to gain access to various high-profile entities, including email access to the US Departments of Treasury and Justice. The case highlighted that a cyber attack on one company could have drastic ripple effects across vital network systems.