AI Articles and Analysis about Business intelligence and analytics

Our research indicates that AI applications for risk-related banking functions are more numerous than applications for other business areas. Fraud and Cybersecurity, Compliance, Loans and Lending, and Risk Management collectively made up 56% of the AI vendor products in the banking industry, as shown in the graph below:

Digitally-native eCommerce businesses are used to working with their customer data in order to write copy for marketing campaigns, run PPC ads, calculate customer lifetime value, and make decisions based on core metrics within CRM dashboards.

AI and machine learning have had successful applications in the financial sector even before the entry of the mobile banking ecosystem. AI is being used to leverage insights from data for financial investing and trading, wealth management, asset management, and risk management.

Artificial intelligence is changing the way healthcare networks do business and physicians perform their routine activities from medical transcription to robot-assisted surgery. Although the more mature use-cases for AI in healthcare are those built on algorithms that have applications in various other industries (namely white-collar automation), we believe that in the coming three to five years, AI solutions for healthcare will become increasingly specialized to individual use-cases.

The International Energy Agency’s latest annual gas market report, Gas 2018, estimated that global gas demand could reach more than 4,100 billion cubic meters (bcm) in 2023. This is an increase from 3,740 bcm in 2017. Greater gas demands mean more oil rigs, and the machines on these rigs break down.

Customer data is essential for insurance firms to stay competitive in the coming decade. Insurance companies at present have backlogs of data on past and existing customers in the form of policy agreements, applications, and claims forms. They’ve also collected millions of images showing car damage, property damage, and personal injuries.

Government contracts are notoriously hard to acquire. It takes many months of painstaking work to get through the gamut of regulations that weigh about 8.5 lb when printed out. Startups and small companies, including those developing artificial intelligence, often do not have the resources to compete for a share of this market. The US Air Force is changing that by streamlining the process in what it calls Pitch Days, the first of which was on March 6 and 7, 2019 in New York.

Generic pharmaceuticals require less research and development than their brand name counterparts. As a result, AI applications for research and development don’t seem to be the most prominent solutions for generic drug companies. That said, despite the lack of precedence, there may be many areas in which AI could help generic drug companies.