UT Students who wrote their thesis at ING

In his master thesis titled 'The Creation of an Explainable Artificial Intelligence Model to Enhance Interpretability and Transparency for ING in Their Fight Against Transactional Fraud' he explores ways of enhancing transparency and interpretability of ML models for Fraud Detection Models. 

Sebastian continued to work at ING. ING utilized the techniques developed within his thesis to challenge and refine their model development methodology. Specifically, his thesis investigated and enhanced new feature design using the transactional data of retail customers to predict their probability of default. His work improved the predictive power of the model, potentially resulting in fewer defaulting clients.

Jens is an enthusiastic and hardworking student, with a strong focus on continuous learning. Located in the Netherlands, he is a double MSc student in the areas of Financial Engineering & Management, Data Science & Business, and Enterprise Architecture & IT Management. Moreover, his BSc in Electrical Engineering forms an ideal basis for technical problem solving within the financial industry.

His technical proficiency spans across a diverse array of programming languages and mathematical knowledge. Beyond technical expertise, he excels in communication and interpersonal skills, demonstrating a proven ability to work independently or collaboratively. These proficiencies and interests are demonstrated through various projects, publications, and positions at companies, aiming to support his interests.

Vitalii is master’s student in Business & Information Technology, specializing in AI research and data science. He just started the preparation for his thesis assignment at ING.

 In the master thesis titled "How to effectively implement the HRDD aspect of the CSDDD in financial institutions?", Thomas explores effective strategies for integrating HRDD processes within financial institutions to comply with the CSDDD. The 7-step action research model from Warner Burke is used as a basis. This is combined with a systematic literature review and semi-structured interviews to come to a best practice & recommendation model.

The aim is to apply her research to real-world use-cases that within ING's Wholesale Banking Advanced Analytics department. ING has a RAG setup where they need to extract answers to a set of questions from a large set of documents. The intern will be asked to study the latest research developments on confidence scores for LLMs, adjust it where needed for application to our practical use-cases, and define and build a hands-on prototype. 

The Wholesale Banking Advanced Analytics team is a large team of data scientists, data engineers, software developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients or internal users. The data scientists in WBAA furthermore have a strong desire to keep up with and be part of the latest developments in the fields of AI, tooling and statistics. Which they do by working closely together with master’s students on a variety of topics to solve academic yet practical problems.