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McKesson is a large pharmaceutical distributor that delivers health information technology, medical tools and supplies, and care management tools and software to healthcare providers in the United States. The company has been working to incorporate artificial intelligence solutions internally since 2012, but in more recent years they have adopted a few important automation tools. 

Ten years into the longest economic expansion on record, auto lenders are looking for ways to leverage new opportunities for growth and risk reduction.

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:

Chatbots have in large part dominated the AI conversation in the enterprise. This has ignited interest in the technology that makes this possible: natural language processing, or NLP. But NLP is not limited to chatbots and covers a broad range of AI capabilities that can be used in several applications.

In 2018, James Kobielus wrote an article on the AI market’s shift to workload-optimized hardware platforms, in which he proposed:
Workload-optimized hardware/software platforms will find a clear niche for on-premises deployment in enterprises’ AI development shops. Before long, no enterprise data lake will be complete without pre-optimized platforms for one or more of the core AI workloads: data ingest and preparation, data modeling and training, and data deployment and operationalization.
We are seeing Kobielus’ words come true. In the past year, nearly 100 companies have announced some sort of AI-optimized IP, chip, or system optimized, primarily for inferencing workloads but also for training. Hyperscalers like Facebook, Amazon, and Google are increasingly talking publicly about "full-stack" optimization of AI, from silicon, through algorithms, up to the application layer.

In the banking sector, supervisory organizations create and oversee the compliance rules that banks and other financial organizations need to follow. These compliance regulations are important for companies to carefully abide by, since non-compliance can potentially result in large fines or in extreme cases, even loss of banking licenses. 

AI has made some inroads in the cybersecurity sector and several AI vendors claim to have launched products that use AI to help safeguard against cyber threats. At Emerj, we’ve seen many cybersecurity vendors offering AI and machine learning-based products to help identify and deal with cyber threats. Even the Pentagon created the Joint Artificial Intelligence Center (JAIC) to upgrade to AI-enabled capabilities in their cybersecurity efforts.

We interviewed Jay Budzik, CTO at Zest AI, about the business value of machine learning for auto lending. We speak with Budzik about how underwriting, lending, and credit scoring is evolving as a result of advances in machine learning - both in terms of new data sources, and more advanced algorithms.

The financial services sector has been one of the early adopters of data science and AI technologies. That said, financial firms that have engaged in AI projects will have realized that they require a deep understanding of data management and skilled data science professionals to solve these complex problems.

Most people may associate augmented reality (AR) with smartphone apps and video games, perhaps even Pokemon Go. But augmented reality is gaining traction in a variety of industries, including manufacturing and healthcare.