A Transition from 4G to 5G: Integrating AI into the Corporate Field

The 21st century is being characterized by a new wave of technological revolution: the 5th generation of computer, also known as 5G. With the development of self-driving cars, autonomous drones, virtual assistants, and IoT-integrated smart homes, this emerging trend in advanced technology is paving its way into the private industries. Utilizing parallel processing in which multiple tasks are performed quickly and simultaneously, the fifth generation of computers are characterized by their capabilities to learn and respond to their surroundings with various types of sensors. To put in simple terms, 5G technology (or artificial intelligence) is seeping into the corporate industries, both large and small.


Following the rapid development of AI, corporations of all sizes are beginning to turn to an entirely novel direction. With Google’s deep-learning AI program now able to predict the death of patients with a staggering 95% accuracy and Samsung Research recently finishing first in both MS MARCO (Microsoft MAchine Reading COmprehension) Competition and at University of Washington’s TriviaQA competition (which challenges AI reading comprehension skills), it is evident that a new era of technology is arriving to the realms of corporate giants. However, what is most interesting is that the seemingly gee-whiz technology is becoming increasingly accessible to SMEs, or small and medium-sized enterprises.

Take Xineoh for instance. A nascent company based in Canada and South Africa responsible for their AI-based business platforms capable of predicting customer behavior through the mathematical analysis of transaction data to improve essential aspects of business including market targeting, production and pricing strategies, and logistics and inventory management. In the outrun, as Xineoh claims, you get a strong and stable business platform with maximum efficiency and customer satisfaction. Through a system called Deep Belief Network, which according to their CEO Vian Chinner “is a combination of machine learning and AI,” the platforms are able to “train huge amounts of data in minutes.” The highly versatile program has already proven competence in various industries including real estate, mortgage lending, and e-commerce. Already, tech giants like Amazon and Netflix are employing algorithms like Xineoh’s to provide better customer recommendations. Xineoh has already paired up with VideoLlama.com, a website that recommends  movies and TV shows to its clients based on their options from who they are watching with to what genre they prefer.

SMEs are a prime target for Xineoh, and with such technology, AI may be not so much as out of grasp for these growing companies. Harnessing the 5th wave of computer and incorporating AI into their business strategies can enable such SMEs to become as successful and powerful as their corporate giant counterparts. Already, we are seeing growth in the AI field with other similar companies like Humley, a British-based organization founded in 2014 that focuses on machine-to-human communication, utilizing the new wave of technology for other businesses to use.

AI may even be somewhat of a necessary element especially for SMEs. According to a paper recently released from Tradeteq, machine learning, when integrated with broader data collection, could increase access to trade finance for SMEs. When compared to traditional models like the Altman Z-score, the AI-integrated scoring system could be a much better, if not necessary, alternative for SMEs. The widely-used “linear discriminant” analysis model like those used by the Altman Z-score creates a myriad of problems for SMEs. For instance, the model selectively focuses on only a small number of accounting entries while ignoring essential non-accounting information, making credit scoring unfeasible for companies that lack even a single entry. Also, it lacks timely information as the traditional model relies on accounting data to be filed annually. However, new models with machine learning technology and big data analysis could generate credit scoring that avoids the difficulties associated with the traditional models while possessing numerous elements for what good predictive credit models should have according to the paper’s author, Michael Boguslavsky: the abilities to “accommodate varying data availability across companies to increase the depth of datasets”, “leverage a broad set of available and emerging data sources, including geographical data,” and “utilize trade network data, including common clients, suppliers, or bank relationships, to spot irregularities and predict credit risk.” All of these, according to Boguslavsky, will enable SMEs to further their understandings of their credit risk, leading to better credit decisions and fewer loan rejections.

With the rise of AI and its increasingly growing connections in the corporate enterprises of all sizes, it is only a matter of time before we fully embrace the new wave of technology. Observing the progress with artificial intelligence and its widening accessibilities to SMEs, traditional businesses and their platforms seem ready to evolve into a completely new system. Progress is a matter of steps, and we are already taking our first step forward to welcome the new era of technological development: the 5G era.


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