Intelligent Data Intergration and Cleaning
Clean, reliable data is crucial to an increasingly digitized financial industry. We currently observe a lack of consistent, high-quality data across asset classes. We propose an AI-driven solution to address this issue.
Factsheet
- Lead school Business School
- Institute(s) Institute for Applied Data Science & Finance
- Research unit(s) Finance, Accounting and Tax
- Funding organisation Innosuisse
- Duration (planned) 01.03.2023 - 29.02.2024
- Project management Prof. Dr. Branka Hadji Misheva
- Head of project Prof. Dr. Branka Hadji Misheva
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Project staff
Prof. Dr. Branka Hadji Misheva
Prof. Dr. Jörg Robert Osterrieder - Keywords Data Cleaning, Compliant Decisions, Big Data, Wealth Management
Situation
With the extraordinary technological advancements over the last few decades, data has become one of the central elements of today's information society. Our implementation partner Move Digital AG (Move) is a Swiss FinTech company focused on 1) digitalisation of the wealth management value chain; 2) integration of wealth managers with suppliers of products, data and services into a shared ecosystem; and 3) development of differentiating digital services for wealth managers. Move recognises that financial market data fuels most wealth management processes and is the basis for taking appropriate, compliant decisions. As processes become increasingly automated and the amount of ingested data grows exponentially, the availability of complete and accurate data is more important than ever. Yet, most industry participants struggle to obtain such data which meets their requirements. The structure of the data vendor landscape is one fundamental reason: No single vendor offers the entire spectrum of data across asset classes and geographies. As a result, wealth managers who serve their clients across a broad spectrum of financial instruments are forced to laboriously aggregate data sets from different vendors.
Course of action
Our mission is to free wealth managers from this strenuous task by applying and extending state of the art, AI-driven entity matching algorithms to financial datasets and integrating them into a unique data ecosystem, achieving increased data coverage, improved data quality and flexible data exploration.
Result
Through this project, asset managers could benefit from improved, AI-driven data. Enhanced data integrity allows for more informed decisions, and expanded data coverage offers a more comprehensive view of the financial markets. The optimized processes for data aggregation increase operational efficiency and enable quicker adaptation to market conditions. The increased transparency strengthens the trust between asset managers and clients, and improved communication tools solidify customer relationships. These developments could set new standards in the financial sector.
Looking ahead
This project has the potential to bring about transformative changes in the financial sector by setting new standards in data processing and analysis. The further outlook of the project includes the continuous improvement and adaptation of the implemented AI algorithms to enable even more precise and comprehensive data analyses. Moreover, ongoing research and development in this area could produce innovative solutions that can revolutionize the way asset managers and financial experts use and interpret data. Thus, this initiative could make a significant contribution to optimizing financial services and promoting transparency and trustworthiness in the financial sector.
