AI Articles and Analysis about Business intelligence and analytics

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.

According to the International Health, Racquet & Sportsclub Association, the global fitness industry earned revenues estimated at $83.1 billion in 2016, up from $81 billion in 2015, and growing by 2.6%. If it continues at this rate, it should reach $87.5 billion in 2018. As of now, numerous AI vendors claim to assist gyms with signing up new gym customers, developing nutrition and fitness programs, and maintaining good customer relationships. Some offer chatbots that they claim can help gym members maintain personalized fitness regimens.

According to Deloitte, global healthcare spending is expected to grow annually by 4.1% from 2017-2021, up from just 1.3% in 2012-2016. The report suggests this growth will be fuelled by aging, rising populations, the growth of developing markets, advances in medical treatments, and rising labor costs.

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.

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.

McKinsey reported that most oil and gas operators have not maximized the production potential of their assets. A typical offshore platform, according to the 2017 report, runs at about 77% of its maximum production potential. Industry-wide, the shortfall comes to about 10 million barrels per day, or $200 billion in annual revenue.

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.

Episode Summary: Prominent technology companies like Google and Amazon lead the way in the B2C world, having access to streams of searches, clicks, and online purchases. They have access to large volumes of consumer data points numbering in the billions that can be used to train machine learning algorithms.