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InsightZai
Machine Learning Modeling for Powerful Insights

Challenge

One of the main activities of a Data Scientist is the use of knowledge of a given domain (eg, processing credit card transactions) for the analysis of raw datasets (e.g. credit), in order to identify features that increase the effectiveness and efficiency of computational learning algorithms.

Unfortunately, the essential tools to the performance of that activity are neither integrated nor optimized, being a time consuming and complex process involving a multitude of completely different tools and programming.

Solution

Main Objective:
To strengthen research, technological development and innovation.

Complete platform for data modeling and analysis (data science), to be applied to fraud prevention activities, but without discarding other domains such as insurance or alternative payments. The goal is to drastically increase the productivity of data scientists, allowing at the same time data science's democratization by making it accessible to less specialized roles, such as business or fraud analysts.
Development of a complete and integrated solution for data modeling and analysis, using machine learning and Big Data techniques, with an emphasis on fraud prevention, but with applicability to other domains.

Objectives, Activities and Results expected / achieved

Expected results:
• Development of an integrated platform for data modelling and analysis (data science), usable in the domain of fraud detection.

Final results:
• Integrated platform for data modelling and analysis (data science) in the domain of fraud detection.

Project Reference

CENTRO-01-0247-FEDER-017728

Funding


Intervention Region

Center (100%) of Portugal

Total Investment

1.758.244,05

IPN Investment

140.007,02

Total Eligible

1.710.416,20

IPN Eligible

140.007,02

EC Funding – Total

1.266.653,17

EC Funding – IPN

176.109,00

Duration

27 Months

Start Date

2016-07-01

End Date

2018-09-30

Approval Date

2016-09-08

Consortium

FEEDZAI - Consultadoria E Inovação Tecnológica, S.A.
Instituto Pedro Nunes

Keywords

Data Scientists;
Fraud prevention;
Machine learning.