Blog/Interview – I Know First, Ltd.

Selecting Investments Using Artificial Intelligence

Yaron Golgher is the Co-Founder and CEO of I Know First Ltd. I Know First is a big data FinTech company that brings science and math to the financial world by providing daily investment forecasts based on an advanced self-learning algorithm. The big data algorithm utilizes artificial intelligence and machine learning techniques through which I Know First is able to analyze, model and predict the stock market. Mr. Golgher Holds a Bachelor of Science (B.Sc.) in Industrial Engineering from Tel-Aviv University and an EMBA from Ben Gurion University. Yaron Golgher has over 18 years of experience in managing and leading consulting projects in industrial companies, services, finance, infrastructure and transportation organizations.

How is Big Data Changing Finance/FinTech?

How is big data changing your job?

As a Big Data Fintech company, operating in the field of capital markets that grow and become more and more complex every year, we identified the need and potential of artificial intelligence and machine learning technology. So, we basically made the understanding and analyzing of financial Big Data to our job, in order to help our customers and partners in making investment decisions and managing portfolios.

While the financial markets are very intricate systems, determining the best components of a successful portfolio does not have to be. Investors are familiar with the saying, “buy low, sell high”, but this does not provide enough context to make proper investment decisions. Every investors dream is prior knowledge of the direction of the market before it happens. Although this is incredibly difficult to do accurately and consistently, it is now possible to create financial market forecasts with algorithms. The quick growing trend of financial advisors utilizing advanced algorithms, is part of a much larger trend of our entire society using “Big Data” solutions for a diversified pool of needs, including predicting credit risk, demand for goods and services, querying social networks to gauge market sentiment, machine readable format company reports, discounts and advertising targeting, as well as many more applications.

By incorporating popular types of convergence averages and moving averages that have been traditionally used to forecast assets for many years with more sophisticated technology and genetic algorithms, professionals are now capable of building complex and intelligent algorithms that can make these predictions more accurate and efficient. Even when financial bubbles and market corrections lurk, a proper understanding of how the markets function plus a vigilant risk management strategy has always been necessary to survive in the financial wilderness. However, investors today have the option to take advantage of state-of-the-art algorithms in conjunction with traditional forms of analysis in order to enhance portfolio performance, verify their own analysis and respond to opportunities faster.

What are some of the opportunities and challenges of big data in finance?

Big data are bringing opportunities and challenges to the financial world.

In the stock market, for example, the first challenge for investors and traders as well as analysts lies obviously in digesting the ever-growing amounts and types of financial data. Also, to structure the data and to clean it by identifying and separating the noise. The ultimate challenge is of course to make sense of that data, to identify the investment opportunities and to translate it into action.

The opportunity on the other side is in separating yourself and gaining a competitive advantage through applying advanced technology, like AI and deep learning, that will give you the edge. And this is what we’re delivering to our clients.

Every investor has their own strategy, such as particular fundamentals they tend to be fond of and level of risk they are willing to accept. These types of analysis alone are becoming outdated and more effective investing tools have become evident to lending a helping hand in increasing portfolio performance. Hedge funds and investment houses have already recognized the benefits of these advanced mathematical models as they play a significant role in their ability to perform, regardless of the overall market environment. Computer-based algorithms, which can analyze many stocks simultaneously and determine quantifiable founded objective predictions, are becoming increasingly more popular to investors as an improved strategy for optimizing returns and mitigating prospective risk.

The challenge for us as a Big Data fintech company, is to continue working on improving our technology, and always make sure that we detect only relevant data pieces and prevent our system from being “overfitted” – but that’s what we are very experienced in.

How is big data changing the way the financial businesses interact with consumers?

In terms of delivering a financial technology product to the client, the financial services field has become more democratic. The raw financial information is more accessible, and so is the simple data analysis software. To stay competitive, businesses have to offer better and affordable services, such as robo-advisers that use big data to optimize the portfolio returns.

In terms of interaction and better understanding of the clients’ profile and thus their needs, it is important for financial businesses to utilize the data available to customize the services. It is possible to incorporate many factors in the AI algorithms for this purpose, such as the customer’s past purchases, credit-worthiness, social networking behavior and other socioeconomic indicators.