
Machine Learning Optimization
Machine Learning (“ML”) is often confused with Artificial Intelligence (“AI”), Deep Learning (“DL”) and even Robotic Process Automation (“RPA”).
Although the boundaries between these four forms of computer-driven activities are sometimes blurred, ML generally refers to computer programs, or algorithms, written to make predictions based on a set of data, that can evolve with time and by being exposed to a larger and more diverse set of data. The evolution encompasses a change in the program parameters in order to optimize its success rate, such as predicting more accurately credit card defaults. ML can be an extremely powerful tool, particularly in the world of finance, where large financial and trading data is available and financial institutions’ competitive advantage can be measured sometime in fractions of a second or a few basis points.
Japan is one of the most advanced manufacturing nations with highly sophisticated robotics. International Data Corporation, an IT market research firm, indicated that “Machine learning as an effective means to achieve artificial intelligence (AI) is no longer just hype, but an emerging reality” and predicted that Japan will have the highest rate of growth in AI in the world.

This focus on AI and ML in Japan is not surprising given its aging population and need to increase its disappointing labour productivity. Naturally, Japan boasts an extensive number of research centers that focus on machine learning, mostly to be found at the leading universities. Here is a comprehensive list.
Data scientists devise ML algorithms using specific types of programming languages, such as R, Python and Lisp, specially suited for data analysis, manipulation and statistics. However, the applications that the ML algorithms feed into use a variety of end-user applications that are built on several types of programming languages such as Javascript, C++, SQL and so forth.

This disconnect often results in a “translation” process where the ML algorithms created by the data scientists are passed on to software developers who code, test and deploy them into client applications. This linear approach to software development is highly inefficient given that every change in the source ML algorithms results in a new “translation” needed to be fed into client applications, exposing the development process to errors and a great expense in time and resources.

ML software company, Knowru, specializes in ML tools for finance applications, such as credit scoring, fraud prevention and loan underwriting. Knowru’s team has created a fully-supported software platform providing a collaborative tool that can bridge the gap between data scientists and software developers. Knowru’s flagship product allows data scientists to upload their ML algorithms and run testing and diagnostics while linking the algorithms to the client applications via APIs. Knowru’s tool allows the deployment of ML models without the need for any translation and supports a leaner production lifecycle with a centralized data repository and a wealth of pre-existing ML algorithms specially designed for the consumer credit market. Knowru’s clients have been able to save hundreds of hours in development time with its streamlined processes.
For further information regarding Knowru’s suite of ML services and tools, see their website: https://www.knowru.com
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Finetiq Limited is a distribution agent for Knowru’s products and services in Japan.
