Every single industry is currently expanding by the hand of artificial intelligence and machine learning, becoming Techs.
Technology is developing at an accelerated pace in every industry, in all segments and sectors within each company. But there are also countless companies that are digital natives or whose core business is based on technology.
Many of those companies are startups whereas others are large companies that have tackled the technological reconversion in an integral way. They constitute the “tech” world and are classified by industry. The earliest were the e-commerce companies that achieved exponential growth worldwide and created real emporiums such as Amazon, Ebay or Mercado Libre.
However, over the last few years there has been a strong expansion through all sectors. Among them we can highlight the well-known Fintechs, but also Insurtech, Legaltech, Foodtech, Agrotech, Proptech, Edtech, Healthtech and many others, including some vertically integrated segments such as Femtech (dedicated to women) or Pettech (pet tech).
Apart from certain characteristic features of their operation, such as the use of fully digital platforms with tools such as apps to facilitate access to all audiences, the rapid expansion of this ecosystem and competition have imposed the need for constant updating in technology use.
The reality in the third decade of the 21st century is that any tech business strategy must be supported by AI and ML, both of which are tools designed to work on one of the central inputs: data.
A profitable alliance: AI and Fintechs’
According to Mediantinc, artificial intelligence (AI) applications in FinTech are expected to be worth up to u$d 7305 million by 2022.
The portal cites The Financial Stability Board (FSB) report to highlight the five most prominent uses of machine learning in finance.
Credit scoring: enabling more loan approvals with less risk. ML processes even more layers of data and is not limited to FICO (risk) scores and income data. They also address social profile data, utility payment data, rent data and even medical checkup records.
Curbing cyber-piracy: Fraud in this sector is one of the most complex problems. ML’s ability to detect fraudulent patterns makes it possible to recognize suspicious activity and alert users.
Rapid adaptation to regulations: ML can leverage another tech field, RegTechs, which use algorithms to read and learn regulatory documents and allow them to automatically track and monitor regulatory changes as they appear.
Improve customer experience: ML generates patterns for each individual and makes it possible to create personalized offers based on a user’s needs and preferences.
An ally for the Stock Market: Historical data processing, real-time data monitoring, news analysis and trading results with the grounds for predicting future market behavior, a central input for any financial investment.
The consumer at the core: AI and FoodTech
The global food industry is increasingly focused on identifying trends and high value-added business niches to develop its products based on consumer data.
An article on TheFoodTech.com states that the growth of the sector is based, in parallel to variables such as marketing automation, production optimization, energy savings and predictive maintenance, on the accelerated development of value-added proposals.
Among the advantages of the application of AI, he points out the understanding of customers, since it allows tracking, considering their tastes and preferences, as well as their emotions associated with products according to interactions in social networks and other media. This also makes it possible to generate more effective marketing campaigns and design products that respond to consumer needs.
On the other hand, it also optimizes quality control: the classification of products according to quality standards is automated using an optical sorting technique.
Prevention as a goal: HealthTech
This type of company demonstrated in pandemics that the health-technology-data science alliance was a necessity that took hold in a resounding way.
The builting.com portal lists 16 of the advantages of ML for health technology companies. They are grouped into disease prediction and treatment, medical imaging and diagnostics, drug discovery and development, and medical records organization.
The examples are numerous, but, by way of highlighting, we cite some of them:
-Accurate and personalized medical diagnostics: processing of individual data and comparison with historical clinical experience improves diagnostic capability.
-Virtuality: systems are developed to enable diagnosis, information capture and information delivery through telemedicine.
-Logistics: The movement of human and material resources to and within health centers is key to optimize them, reduce risks and improve delivery. ML makes it possible to organize the flow of people, the delivery of supplies and the circulation of ambulances, among other advantages.
The dream of personalized learning: EdTech
AI offers a tool for the transition from whiteboards to smart screens. This sector had a great development in pandemic, and today there are thousands of online and interactive teaching sites. According to builting.com, through ML, companies offering these services can personalize the learning experience and help teachers grade accurately and more quickly.
Among many examples of EdTech are online learning platforms with interactive study guides, lecture notes, step-by-step solutions to problems and support with online assistants such as chatbots or personal assistants, to name a few.
AgroTech: these are agricultural companies that could efficiently use resources, optimize production, improve profitability, all with strategies that respect the environment.
With AI it is possible to implement automated irrigation, hydroponics, scanning and surveillance through drones, improve weather forecasting and optimize logistics.
RealTech: the world of real estate is increasingly dependent on technology. Not only for the design of intelligent buildings and construction planning, but it has become essential in the management of condominiums, in the process of selling and renting properties and even in the search for contractors.
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