AI Articles and Analysis in Finance

Implementing responsible AI in the financial sector is crucial for ethical practices, fairness, and transparency. Financial institutions must prioritize data privacy, address biases, ensure explainability, and practice ongoing monitoring. By doing so, they build trust, mitigate risks, and foster sustainable growth. 

While accounting as a discipline has roots going back to the 13th century, current-day accounting software is still based on manual record-keeping just as it was in the 1990s, only with digital checks in a digital checkbook register, allowing users to keep track of their finances on a computer.

Market surveillance refers to activity authorities conduct to ensure that products available to consumers adhere to applicable laws and regulations. Additionally, market surveillance in banking and finance takes a somewhat specific form.

Customer experience (CX) teams often face a complicated puzzle when it comes to budgetary decisions and resource allocation within their organizations.

MetLife is a leading global insurance company headquartered in New York City. It provides its customers various insurance and financial services, including life insurance, health insurance, retirement plans, and investment management.

AI is revolutionizing how companies conduct operations, serve customers, and manage risks – driving a USD 20 Billion investment in AI capabilities among BFSI firms last year. As AI continues to transform the industry, financial services organizations will seek to build effective AI teams to stay ahead of the competition. 

Goldman Sachs was founded in 1869 and joined the New York Stock Exchange in 1896. Since 2016, Goldman Sachs has begun moving into consumer financial products. The company is headquartered in Lower Manhattan with regional offices in England, Poland, Hong Kong, Tokyo, Bangalore, and on US soil in Dallas and Salt Lake City.

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.