DC6: Collaborative learning across data silos

Objectives

Connecting several dozen different data pipeline components and integrating an excessive number of APIs to leverage siloed data is a significant barrier to the comprehensive implementation of AI-based systems in finance. Currently, very little research is devoted to addressing all of the challenges associated with training, testing, and deploying cutting-edge ML and DL methods while leveraging siloed data. We will concentrate on the data challenges that finance service providers face by proposing solutions to streamline data collection, resolve data quality issues, and structure data to support downstream processes.

Expected Results

APIs for integrating Machine Learning and Deep Learning algorithms into FinTech processes necessitate careful abstraction of the specified input and output, which is the responsibility of the researchers to simplify and aggregate the complexity. This project produced a large number of API definitions that are closely related to research papers in the fields of theory of Artificial Intelligence and Machine Learning, as well as theory of Finance applications in various sub-fields such as security and compliance. The API specification itself should not only be integrated into financial institutions' business processes, but should also provide fruitful input for new research papers.

Secondments (2)

Recruitment & Hosting Details

DC 6 WWU WU Vienna M9 36

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DC 6 WWU WWU Month 9 36 months D 1.1 - 1.3

Collaborative learning across data silos (WP 1)

Objectives: Connecting several dozen different data pipeline components and integrating an excessive number of APIs to leverage siloed

data is a significant barrier to the comprehensive implementation of AI-based systems in finance. Currently, very little research is devoted

to addressing all of the challenges associated with training, testing, and deploying cutting-edge ML and DL methods while leveraging siloed

data. We will concentrate on the data challenges that finance service providers face by proposing solutions to streamline data collection,

resolve data quality issues, and structure data to support downstream processes.

Expected Results: APIs for integrating Machine Learning and Deep Learning algorithms into FinTech processes necessitate careful

abstraction of the specified input and output, which is the responsibility of the researchers to simplify and aggregate the complexity. This

project produced a large number of API definitions that are closely related to research papers in the fields of theory of Artificial Intelligence

and Machine Learning, as well as theory of Finance applications in various sub-fields such as security and compliance. The API

specification itself should not only be integrated into financial institutions' business processes, but should also provide fruitful input for new

research papers that are of interest to readers and users of all involved fields of research.

Planned secondments: Swedbank, Prof. Dr. Tadas Gudaitis, M23, 18 months, research on prototype implementations, applied research

Fraunhofer, Prof. Dr. Ralf Korn, M41, 4 months, applied industry-research, implement various use-cases

Fellow Host institution PhD enrolment Start date Duration Deliverables

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Deliverables