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Nomura Securities uses AI analytics to improve data quality

Anuradha Shukla | June 5, 2017
Fujitsu's solution takes an autonomous analytical approach, using actual data and AI, to find situations that deviate from the norm for the securities company.

AI head with brain

Fujitsu Limited is enabling further improvements to data quality at Nomura Securities Co., Ltd.

The analytical AI (machine learning technology) will be deployed this month to enable further improvements to data quality in areas where conventional methods of ensuring data quality had reached their limits.

Taking an autonomous analytical approach, the solution will use actual data and AI to find situations that deviate from the norm.

This deployment is spurred by the positive results of an earlier joint trial.

Nomura Securities' IT department was searching for a high quality system that can deal with the fast-paced changes in the environment of the securities business.

Since the company had to attend to a variety of manual entry tasks and internal business processes, it was searching for a system that could improve the quality of data recorded and stored each day.

Before turning to Fujitsu, Nomura Securities used a method of ensuring data quality, which had limitations in grasping human input errors. The previous method was also not able to detect the patterns of occurrences when data deviated from the norm.

Fujitsu and Nomura Securities thus teamed up to conduct a joint trial focusing on the occurrence tendencies and frequencies of past data.

Fujitsu's analytical AI solution delivered several benefits including significantly improving verification task efficiency for large volumes of operational data. 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. 

Since the analytical AI can quickly detect patterns that differ from the everyday norm, the solution 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.

The solution also delivered efficient and comprehensive extraction of test cases through pattern analysis. Machine learning allows the analytics AI solution to autonomously train itself on the operational data that is produced day by day, reflecting new data patterns in test cases. Since it can comprehensively extract data patterns that are highly important to operations while omitting unnecessary data patterns,  the solution helps improve test quality and productivity.

Spurred by the benefits, Nomura Securities will expand the range of its systems that apply the analytical AI solution in future to further improve data quality.

 

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