Following the newly formed cooperation with Poalim Hi-Tech (Bank Hapoalim) during BizTEC 2016, and the general development of the Fintech world, we converged on an opportunity to raise awareness and encourage the establishment of technological ventures in the Fintech field.
This led us to initiate a Fintech Hackathon in order to engage students and alumni, from both Technion and Haifa University, who are interested in the Fintech and entrepreneurship worlds, and by that, creating Interdisciplinary teams.
The event was targeted at teams and individuals, wishing to gain experience in creating ventures in the Fintech field and receive guidance from Bank Hapoalim’s experienced professionals, and from BizTEC mentors.
The Fintech Challenge was held on March 29-31 at the Technion with 38 participants.
The event began with introduction by Professor Avishai Mandelbaum- Dean of the Faculty of Industrial Engineering and Management followed by a lecture by Dr. Yoav Intrator, CTO of Bank Hapoalim, and Yael Weisbord, Bank Hapoalim’s Fintech Program Manager. The participants delivered short presentations with their initial ideas. From that point on, the teams worked for two full days on developing their ideas with the guidance of mentors from the field , and reached great achievements.
The event ended with the final presentations of the newly created ventures in front of a judging panel that included representatives from Bank Hapoalim, David Shem Tov, Prof. Uzi De-Haan from the Technion and the BizTEC and BEC team. The top three teams were announced, and the winning team was granted an automatic qualification to the BizTEC 2016 competition, and a chance to receive a $25K future investment from Bank Hapoalim.
The winners of the Fintech Challenge are the ClearCut team, which consists of 5 members, most of them are students from the Technion’s Industrial Engineering and Management Faculty. Clear-Cut develops real time optimization tools for online retailers which find and execute the best payment clearing for every transaction, using cutting edge predictive and quantitative models thus minimizing clearing fees for it’s customers.