VeriZeka is a technology company which primarily caters to the financial sector with its self-learning, fully autonomous and – most certainly – human-free products and solutions.
VeriZeka came into existence back in October 2015 as a single incubated project, rather than a legal entity, through the joint efforts of Aykut Sarıbıyık (over 15 years of experience in banking, finance, investments, equity research and fund management) and Refik Çağlayan (over 15 years of experience in digital services, mobile, content, market research and data science).
The project, which had until recently been incubated by 4P Digital İşler A.Ş. – a widely renowned and well respected mobile solution company – was later transformed into a legal entity under the title of VeriZekalı Teknoloji Araştırma ve Geliştirme A.Ş. in November 2017.
We exist in this business to help our clients reach more rational, more precise, and more deterministic decisions. With that purpose, we offer products which, by design, are good self-learners, good self-adapters (better than people), and robust decision makers.
First of all, we believe in robots… particularly for the tasks that require a larger knowledge base (as machines are capable of storing more information than humans) or faster computations (as machines are able to calculate faster than we are, in most cases). That said, we believe it is crucial that all sorts of human error is eliminated (completely) from the decision making process to achieve the more rational, precise, and more deterministic decision environment that we strive for.
And when it comes to our start-up culture that we take a great deal of pride in… We like working as small, independent teams. From the most junior to the most senior, we adopt a highly entrepreneurial approach while conducting research and developing new ideas. We encourage self-direction, independence and autonomy in the company. At all times, we seek to maintain an inquisitive spirit in the company and follow a positive, “can-do” attitude.
Finally, we have a willingness to learn; we do our best to maintain an open and innovative mindset in the company at all times, with an awareness that the area we conduct research and develop solutions is a never-ending marathon where only those who can adapt well, work hard and think flexibly can lead.
At VeriZeka, we ultimately target complete automation of the decision cycle – as we believe that human decision makers, in most decision moments, would inevitably bring a degree of human error to the decision process.
Our prime focus is the financial sector, simply because the financial sector – among many other target sectors – stands out more clearly as a profession dogged by human error, human bias and human only barriers in the decision making process. That is why we have prioritized the financial sector (investment management to be more precise) as our target sector for the first years of our company.
To elaborate further, we aim at eliminating the cognitive biases of human beings, which would not be exhibited by machines, from the decision making processes of our clients. To give some hint:
- Confirmation Bias (i.e. putting more weight into the investment options that confirm the portfolio manager)
- Gamblers’ Fallacy (i.e. making contrarian investment decisions with no statistical background)
- Status-Quo Bias (i.e. being stuck into old, known investment options instead of researching for fresh ideas that would bring in an obvious alpha)
- Negativity Bias (i.e. placing more weight on bad news – with no rational ground- than on good news while the market is rallying and visa versa)
- Bandwagon Effect (i.e. the inability of the human traders to be greedy – as Warren Buffet defines – when others are fearful, and fearful when others are greedy)
And the emotional biases. We believe machines would simply do better than human beings, as the emotional barriers – which stand between the (human) decision maker and the (rational) best decision in most cases – don’t exist for the machines in most cases. Some phenomena which are well documented in the literature are:
- Loss-Aversion Bias (i.e. being unable to stop-loss a wrong investment decision due to sticking irrationally to the false hopes)
- Overconfidence Bias (i.e. the overconfidence that seasoned traders often carry into their investment decisions. A person with overconfidence bias believes (with no rational or quantifiable ground) that his/her skill as an investor is superior to the skills of others)
- Endowment Bias.
Finally, the psychological bias that only human decision makers are open to but not the machines; we are after the decision processes that are not clouded by psychological bias.
So, our company’s philosophy should now become apparent – we believe machine-learning based decision systems (applied in asset pricing, valuation, and investment management) should lie at the very core of every decision process in this practice. That is what we keep in mind as the team in our company while catering for our clients.