Fujitsu Limited today announced that it is providing Nomura Securities Co., Ltd. with an analytical AI (machine learning technology), which will be deployed in June 2017. This will enable further improvements to data quality in areas where conventional methods of ensuring data quality had reached their limits by taking an autonomous analytical approach, using actual data and AI, to find situations that deviate from the norm.
Using the opportunity of this deployment, Nomura Securities plans to expand the range of its systems that apply this technology, with the goal of further improving data quality. In addition, noting the general applicability of the technology that is now being adopted by Nomura Securities, Fujitsu plans to offer it in other industries as well.
In Nomura Securities’ IT department, there has been a demand for a high quality system that can swiftly deal with the fast-paced changes in the environment of the securities business. In addition, alongside the processing performed by securities systems, there has also been needs for continuous improvement in the quality of data recorded and stored day by day through such processes as a variety of manual entry tasks and internal business processes. In previous methods of ensuring data quality, however, there were limitations in the ability to grasp human input errors and the patterns of occurrences when data deviated from the norm.
About the Analytical AI Technology
Fujitsu and Nomura Securities carried out a joint trial focusing on the occurrence tendencies and frequencies of past data. Using machine learning core technology and acceleration technology developed by Fujitsu Laboratories Ltd., the two companies verified whether this system can autonomously analyze data simply run through the system, without any prior training, after separating it into ordinary patterns and patterns that deviate from the norm (anomalies).
Given the trial results, Nomura Securities plans to deploy data analysis AI based on these technologies.
Example of Use at Nomura Securities
1. Significantly improving verification task efficiency for large volumes of operational data
Previously, there had been cases in which manually checking large volumes of operational data in their entirety was impractical, meaning that ordinary system checks were not comprehensive. In this trial at Nomura Securities, by applying the analytical AI, a few dozen records that deviated from the norm were separated out from records numbering in the tens or hundreds of millions, including a few records showing patterns that even experts could not have recognized, enabling new discoveries. Because the analytical AI can quickly detect patterns that differ from the everyday norm, this system can significantly improve the efficiency of operations. Moreover, by building up a store of new discoveries from the detected patterns as expert knowledge, ongoing improvements in data quality and analysis accuracy can be expected.
2. Efficient and comprehensive extraction of test cases through pattern analysis
Previously, the creation of test cases had to rely on the knowledge of experts, but by applying analytical AI, it is possible for it to autonomously train itself on the operational data that is produced day by day, reflecting new data patterns in test cases. Because it can comprehensively extract data patterns that are highly important to operations, while at the same time omitting unnecessary data patterns, enabling efficient test validation, this system can contribute to improving test quality and productivity.
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