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Natural Language Processing Start Up Eye Capital Threatens to Phase out Human Traders

Financial traders operate or work in a high-risk, complex and fast-paced environment.

Since they also deal with large sums of money in the same environment, they are susceptible to making mistakes.

Although numerous risk reduction technologies and methods have been created over the years, the most effective one ought to be getting rid of the human element.

Eye Capital, an Argentinian-based startup that focuses on natural language processing, appears to be fit for this role.

Eye Capital’s algorithms can read and interpret financial news in real time, which has caused the firm to create a buzz in the financial world.

In an interview, Alejandra Litterio, the co-founder and CRO of Eye Capital, revealed more details about the company.

READ MORE: 10 Applications of Machine Learning in Finance

READ MORE: Artificial Intelligence Revolution Disrupts Investment Banking

Origin of the Eye Capital

Eye Capital traces its background seven years back when Litterio was undertaking an academic project at Universidad Abierta Interamericana’s Centro de Altos Estudios en Tecnologia Informatica.

The purpose of the project was designing an algorithm that could create correlations in the markets as well as forecast and automatically suggest whether to sell or purchase equities.

In 2016, the project became successful, especially after Litterio’s husband, Juan Pablo Brana, met with some angel investors who shared their vision.

On May 4, 2017, Eye Capital became an official company, having begun as only an academic project.

Currently, Litterio serves as the chief research officer whereas Brana is the CDO.

The team, which is made up of six co-founders, itself on being a multidisciplinary company with the objective of being the first ever fintech aiming to develop AI algorithms.

Meaning of Natural Language Processing

According to Litterio, natural language processing entails the language that humans utilize to interact on a daily basis.

However, by delving deeper into the main aspects of linguistics, the complexity of the field becomes more obvious. Worst case scenario, human communication is dependent on context.

Multidisciplinary Organization

Litterio explained in the interview how Eye Capital uses a multidisciplinary approach. She emphasized that different experts including translators, neurologists, philosophers, historians and anthropologists are all critical.

The reason is that in a bid to train an AI algorithm, you require understanding various aspects like how human brain functions, the practicalities of building a functioning AI algorithm and how the neurons in the brain interconnect.

Risk

With the existence of inherent risk in the financial trading industry, it is imperative to limit it, particularly in automated trading.

Risk parameters come in handy when training artificial intelligence (AI) algorithm models that can eventually create portfolios with minimal risk and considerable returns.

Can the Technology Replace Traders?

If Eye Capital’s technology works successfully, it is highly unlikely to imagine it phasing out traders.

However, Litterio said that putting such people out of work is not the company’s objective.

She also pointed out the need for change and a tool that can effectively minimize risk, increase profits and manage portfolios efficiently.

Litterio believes that the general methodology, using machine learning and AI, would eventually replace financial traders.

For her, such a situation would not be entirely wrong as it would create time and space for new enterprise areas and ecosystems to emerge.

Source TheStack

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KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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