A new member expands and enriches our community, and we are pleased to welcome Bausparkasse Schwäbisch Hall to our ranks today.
Schwäbisch Hall is the largest German Bausparkasse (Building society) and one of the leading providers of housing financing. A team of around 6,800 people works closely with the German Cooperative Banks to create and preserve homes for the customers with more than 7 million contracts.
As part of the membership, we conducted an interview with Kristin Seyboth, executive board member of Bausparkasse Schwäbisch Hall and member of the management of Schwäbisch Hall Kreditservice GmbH. In addition to IT and process management, purchasing and supplier management, she is responsible for building society processing and for the successful migration of IT to an SAP-based core banking system.
IPAI: Kristin, how do you see the role of AI in enhancing the work environment at Bausparkasse Schwäbisch Hall?
Kristin: AI is key to increasing productivity, creativity, and joy in our work. However, it’s important to remember that our business is fundamentally about people—it’s a business for and between individuals.
That’s a great point! Given this human-centric approach, what are you looking to achieve through your involvement in the IPAI community?
We are eager to exchange experiences on the use of AI and data-driven solutions in businesses. Our goal is to identify best practices together. By collaborating with start-ups and universities, we hope to learn about the latest approaches and develop actionable ideas.
Our commitment to advancing AI across the organization reflects our belief that collaboration is essential for progress. By exchanging ideas with diverse industry experts, we can gain new insights and improve our approaches.
Kristin Seyboth
Executive Board Member at Bausparkasse Schwäbisch Hall
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To me, Human AI refers to using AI methods to support human activities and enhance workflows. It’s all about improving the human experience in our work.
With that in mind, what specific expertise does your team bring to the community?
We have experience in applying machine learning models to automate internal processes and optimize customer interfaces. Our specialized knowledge in the financial sector is particularly valuable, as it supports our data science specialists in their daily tasks.
You mentioned the importance of exchanging experiences with other companies. How does that fit into your strategy for AI?
Engaging with other organizations and connecting with start-ups and universities is crucial for us. It helps us challenge our own AI approaches and potentially improve them, bringing us closer to industry standards in AI applications.
What challenges do you foresee in implementing AI, especially in terms of data management?
One of the biggest challenges is ensuring data quality and availability, as well as integrating AI with existing systems. We need to clarify what meaningful AI use cases are, particularly regarding what is feasible and what is not. Working with customer data often involves high setup costs related to legal compliance and data protection.
Given those challenges, what specific projects are you currently working on to leverage AI effectively?
We are developing machine learning solutions for process automation and optimizing customer interfaces. Additionally, we are exploring the use of large language models for voice-, chatbots, knowledge access, and background process automation. We’re also steadily improving our tech stack with more and more experience in deploying Python-based machine learning solutions and utilizing cloud-hosted LLMs. Another AI-technology is our voicebot, which we are currently implementing in our customer service to further enhance it.