Proud to announce that Stanislav, senior data scientist from Inmeta, will
be providing our keynote at our workshop .

He has:
• 6+ years of experience in predictive modeling, analytics and machine learning
• Team lead with a track of successfully delivered high value business projects
• Experienced in architecting and building highly scalable cloud-native solutions
• Ph.D. in Applied Physics with extensive experience in programming (Python, R)
• In-depth knowledge of deep learning and time-series forecasting
• Strong presentation, leadership and teamwork skills

Machine learning (ML) has moved from the periphery to the very center of the technology boom. But which industry is best positioned - with the huge data sets and resources - to take advantage of machine learning? According to research by PwC, this industry is finance.

Machine learning can significantly contribute to your FinTech project’s success by increasing accuracy within predictive analytics which can be leveraged not only for trading algorithms but also for personalizing your customers experience.

Examples include, algorithm trading (stocks, investment, bitcoin, electricity, energy), risk analysis (anomaly prediction, car accidents, lending and insurance), and fraud protection.

According to Techfunnel, 73 percent of daily trading worldwide is carried out by machines in 2017. Almost every major financial company invests in algorithmic trading as the frequency of trades executed by machine learning technology is impossible to replicate manually.

Fraud criminals steal $80 billion a year in insurance industry according to the Coalition Against Insurance Fraud. Machine learning can detect and prevent anomaly behavior, auto report them, and advise on course of action.

We're going to go through which algorithms are currently used by the industry and how they innovate in the above issues mentioned.