Ex-Banker and Quant Brings Machine Learning into The Banking Scene

Ex-Banker and Quant Brings Machine Learning into The Banking Scene

Two people are in the course of integrating the amazing new technology of machine learning into the world of banking and finance. Paul Bilokon who is a former quant at Citi and machine learning lecturer at Stanford University is spearheading the motive alongside Mathew Dixon a computer scientist and lecturer at the Stanford University. Mathew is also a quant at Lehman Brothers and Barclays Capital.

The two explain the benefits that the technology could bring to the field of finance and banking if utilized in the same way.

Machine Learning has been a sensation among studies worldwide and it is being applied in almost all fields in the current world. The technology has seen great breakthroughs in the creation of new technologies worldwide and it is helping computers become more and more useful by integrating computer algorithms to help computers solve problems without having to be reprogrammed.

Mathew Dixon believes that there is need for development of an algorithm that is entirely accurate and does not make the small mistakes which are majorly detrimental in the world of finance and banking. He explains that machine learning will be very helpful in solving problems that quantitative finance.

The current Artificial Intelligence used in finance and banking involves the use of computer algorithms that can arrive at answers or results already known to the human brain. It is just an easier and faster way of finding results and therefore means the machine does not get to work out answers for itself.

Machine learning, on the other hand, will bring more power to the computer as it gives the machine enough power to predict and give conclusions given current dynamics. The technology needs extensive and dynamic programming to achieve highly functioning algorithm.

Dixon further clarifies the importance of unsupervised learning so that there is complete functionality even when the human mind tires. He proposes a technique called reinforced learning whereby the computer is penalized for making a mistake and rewarded for rightly completing a task.

The two, Dixon and Bilokon have held several seminars and conventions for the last months trying to educate the world about this new venture. In their last venture, they charged each entrant $2,600 for the talk. Many people were willing to attend and get their knowledge updated on machine learning and banking.

The future is surely bright for the integration of machine learning into banking and lets us waits for the developments that come ahead.

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