The financial industry is undergoing a digital transformation, and the demand for clean and reliable data is increasing. However, the current state of data quality in the financial sector is often poor, leading to costly errors and inefficiencies. In the financial sector, data is often collected from various sources, including market data, transaction data, and customer data. This data is then used for various purposes, including risk management, compliance, and trading. However, legacy data integration systems often struggle to keep up with the increasing volume and complexity of data, leading to errors and inefficiencies in decision-making processes.

The DataInc project, Carried out as Innosuisse project 120.603 IP-ICT in collaboration with Andrea Nagy, Damian Tschirky and Gabriele von Planta from Swiss FinTech firm Move Digital AG, aimed to address these challenges by developing an AI-driven solution for data integration and cleaning. The project focused on automating the process of data integration and cleaning, reducing the need for manual intervention and improving the overall quality of data.

The solution developed by DataInc is based on a combination of machine learning algorithms and natural language processing techniques. The system is designed to automatically identify and correct errors in data, such as missing values, duplicates, and inconsistencies. This not only improves the quality of data but also reduces the time and effort required for data integration and cleaning.