AI Sector Overviews Articles and Reports

The retail industry could be losing nearly $1 trillion in sales annually due to business process errors that could be automated by AI, such as restocking in eCommerce. In this article, we discuss the top 3 most well-funded AI startups selling to the retail industry and how their solutions could help retailers and eCommerce sites save money lost to fraud and increase revenue through customer analytics.
Signifyd - Retail and eCommerce Fraud Detection
Signifyd is the most well-funded AI startup in the fraud detection industry for retail and eCommerce, having raised $180 million. They were founded in August 2011 and specialize in fraud detection for retail and eCommerce companies. Their most prominent offering is called “guaranteed fraud detection,” and it likely uses anomaly detection technology to recognize fraudulent transactions and prevent chargebacks. The offering was originally announced exclusively for the Magento eCommerce platform in 2017.

Many of the largest US banks, including Bank of America, are starting to automate many of their business processes with AI. These include ACH payments and more specific processes such as order to cash. Bank of America has been investing seriously in AI and machine learning since at least 2017 and continues to research ways they can take advantage of AI in the future.

Much of the discussion surrounding AI in banking is focused on retail banks, with applications such as chatbots and payment fraud detection. But there exist AI capabilities that stand to benefit investment banking clients specifically as wealth management departments begin to adopt them.

Many AI vendor companies offer AI-enabled products and services for pushing more and more products in front of customers. That said, it is not always clear how these solutions determine which products to advertise to which customers. Retailers and other businesses should consider what they need to do to prepare their enterprise for one of these solutions and familiarize themselves with how AI recommendations are built and trained.

Many large banks and financial institutions are beginning to digitize parts of their business processes to prepare for future initiatives in automation and machine learning. This is particularly true with loan processing. These functions could become faster and more accurate if they use digitized data that is more easily accessible than paper documents.

In this brief overview, we run through several use-cases for voice recognition software in the healthcare industry. Voice recognition software, built on natural language processing (NLP) algorithms, primarily finds a home in the doctor's office. Physicians use it to dictate their notes into their healthcare network's system or update patient electronic medical records (EMR).

Wells Fargo has begun a number of AI initiatives, some they've created in-house and some they've created with help from vendors. In this article, we detail the following AI initiatives at Wells Fargo:

It can be difficult for financial institutions to keep up with the rapid changes in the digital marketing and advertising landscape. There are numerous factors which are susceptible to change, and they all have an effect on how useful certain marketing strategies are. As the internet and advertising evolve, some companies may find it important to consider an automated solution to driving efficiency in marketing.