About KickStart AI
At Kickstart AI we believe the future is positively shaped by AI. That is why our mission is to accelerate the adoption of AI in the Netherlands. We are a coalition of the doing, we grow and connect the AI community and we tackle real issues with AI solutions.
UT – ING Collaboration:
Kickstart AI is a platform committed to accelerating AI adoption in the Netherlands. In this project, the collaboration between ING and UT aims to explore cutting-edge AI applications in Data & AI, Risk Management & AI, and Linking Business Applications to AI use.
First, regarding Data & AI, the joint effort currently focuses on innovative meta-labeling techniques, federated learning models, and privacy-enhancing methods for handling confidential data. Additionally, it delves into the development and application of synthetic data generation tailored for the finance sector. These initiatives are pivotal in enhancing data handling and analysis while ensuring data security and privacy in financial operations.
As for Risk Management & AI, the project explores early warning systems for credit risk and the integration of (reinforcement) machine learning in credit scoring. This includes leveraging eXplainable AI for risk management and employing large language models for efficient information retrieval from financial documents. These initiatives aim to revolutionize risk assessment and management in banking, enhancing accuracy and transparency.
Finally, we explore the impact of AI innovations in finance, analyzing client base networks and developing statistical and visualization tools for decision-making in applied models. This segment underscores the practical application of AI in enhancing business operations, decision-making processes, and customer insights in the banking sector. This collaboration, steered through Kickstart AI's platform, is poised to set new benchmarks in AI application in finance, reflecting a blend of academic insight and industry expertise.
Main research topics:
- Data & AI:
- The use of "meta labeling" techniques;
- Federated Learning; Privacy-enhancing techniques for storing and analysing confidential data;
- Applications of synthetic data generation for Finance;
- Risk Management & AI:
- Early warning systems for credit risk;
- (Reinforcement) Machine learning for credit scoring;
- eXplainable AI for risk management related topics;
- Large Language Models for information retrieval from documents and its applications;
- Linking Business Applications to AI use:
- The value of innovation projects in Finance;
- Analysis and model of networks of the client base;
- Statistics and Visualizations for decision-making of applied models;
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