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Technology Race. How Artificial Intelligence Helps Business

Technology Race. How Artificial Intelligence Helps Business

 

Large companies are investing hundreds of millions of dollars in artificial intelligence systems. What do they hope to gain from machine learning?

The introduction of AI by 2030 will increase global GDP by 14%, approximately $15.7 trillion, according to PwC. Consultants say that's more than the current combined industrial output of China and India. According to Teradata research, the vast majority of large companies (80%) are investing in artificial intelligence technology, and according to Gartner predictions, by 2020 it will be present in almost all new software products and services.



Researchers reported that the financial industry remains the leader in terms of the number of projects. Here, technology allows to reduce costs, minimize risks, prevent fraud, check borrowers, assess their solvency, make forecasts, etc.  For example, Chase Bank uses AI to analyze client data, which allows for a more personalized approach to their offers. Also, the bank has recently moved its product catalog to a non-relational database controlled by AI, which structures the data itself when rates change, without requiring changes in software code. The financial sector has always been at the forefront of using technology, and as recently as 15 years ago banks began using AI to predict defaults using self-learning neural networks. Now AI allows us to solve a wider range of tasks: minimizing risks, preventing fraud, checking borrowers, assessing their solvency, making predictions, and so on.

 

In retail, the main direction of implementing artificial intelligence is related to customer service, optimizing logistics, inventory, reducing costs, and forecasting demand. Among the examples from retail was the company GameVideo, which implemented artificial intelligence technologies to improve customer service through its online store. It analyzes customer behavior on the site, transitions between pages, and section views, after which the system prepares personal product recommendations, which it sends to the registered user by mail.

To increase conversion rates by returning departing visitors and increasing the response rate from marketing mailings, the company implemented a machine-learning-based solution that determines the optimal time to interact with a customer in order to make a purchase. This includes a mechanism that analyzes the preferences of other customers. These and a number of other measures to optimize the site sales for the 9 months increased by 30%, and the conversion of visitors into buyers - by 10%.

 

In insurance, artificial intelligence is used for document management, client data processing, risk selection, anti-fraud, definition of personalized insurance services and distribution of insurance payments. Among the leaders in this segment is Pheno, which was one of the first to introduce AI elements to solve the main problem of the insurance market. AI, which we developed a novel blood-testing technology that allows for performing an entire panel of protein screenings tied to longevity, wellness, and personalized risk – with a single routine blood test. This panel is specific to each person and when combined with our proprietary machine learning models, turns into valuable insights for underwriters, doctors, and consumers. - said Vadim Pinskiy, co-founder and COO.

Revolution taking place in the insurance market - from an object-based approach, when before we tried to aggregate factors by groups of clients, that is, to offer all people with the same age, experience, car, etc. a common rate, to an individual approach, when each client now has the opportunity to get a personal rate based on their skills. An important feature of AI, which probably hinders its implementation -you can't trust a machine to make money, because you can't ask it for the result. But you can ask a human manager.

 

Presented cases in industry, first of all, implementation of artificial intelligence at Chicago Iron and Steel Works. The launched solution helps to decide on the range of chemical composition of raw materials for steel to optimize the quality and cost of production. The stated efficiency was about 5%. Further, using a similar model, the company implemented artificial intelligence in chocolate production and gold extraction. According to the developer, the payback period for such projects is 6-9 months.

 

According to experts, despite the high growth potential of the market, in practical implementation of technologies is hindered by relatively high investments in projects with existing doubts about financial returns. In addition, for the development of business-oriented AI a barrier is the lack of computing power, the availability of high performance infrastructure will adjust the situation. Despite this, there are already examples of AI implementation in the domestic market, which prove their effectiveness and usefulness for business. Many companies are aware of the need to actively invest in AI, this is confirmed by the desire of venture capital market leaders represented by investment funds. On this basis, experts argue that even today the use of AI/IMO technology in business is an "arms race. The first to implement, optimize business processes and make the best offer to the client will win.