Philipp Winder
Last Name
Winder
First name
Philipp
Email
philipp.winder@unisg.ch
9 results
Now showing 1 - 9 of 9
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Item type:Publication, Biased Echoes: Generative AI Models Reinforce Investment Biases and Increase Portfolio Risks of Private Investors(2024); ; Hartmann, JochenGenerative AI models are increasingly used by private investors seeking financial advice. The current paper examines the potential of these models to perpetuate investment biases and affect the economic security of individuals at scale. It provides a systematic assessment of how generative AI models used for investment advice shape the portfolio risks of private investors. We offer a comprehensive model of generative AI investment advice risk, examining five key dimensions of portfolio risks (geographical cluster risk, sector cluster risk, trend chasing risk, active investment allocation risk, and total expense risk). We demonstrate across four studies that generative AI models used for investment advice induce increased portfolio risks across all five risk dimensions, and that a range of debiasing interventions only partially mitigate these risks. Our findings show that generative AI models exhibit similar “cognitive” biases as human investors, reinforcing existing investment biases inherent in their training data.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ownership Structures And Governance Strategies For Digital Platform Ecosystems: An Empirical Study(2023); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ownership Structures and Governance Strategies for Digital Platform Ecosystems: An Empirical Study(2023); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Beyond Automation: The Transformative Power of AI to Shape Consumer-Firm Relationship(2024-09-26) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Risky crypto: The effect of cryptocurrency as payment method on trust in consumer-firm relationshipsThe rise of cryptocurrencies is transforming how consumers trade, purchase, and pay for products. The current work explores how cryptocurrencies used as a payment method shape consumer-firm interactions. Through one large-scale pilot study, four experiments, and one large-scale field experiment, we demonstrate that consumers perceive cryptocurrencies as a riskier payment method compared to traditional payment methods. We introduce a risk-transfer-model of payment technologies and show how this heightened risk perception induced by a payment option subsequently increases consumers’ perceived interaction risk to engage with the same firm. These risk dynamics negatively impact and ultimately erode consumers’ evaluation of the firm offering the payment method. We further demonstrate two important boundary conditions: When firms actively communicate the privacy benefits of cryptocurrencies as a payment method or when the firm has a higher baseline trust with consumers, the negative effects of cryptocurrencies as a payment method are reduced. Our findings highlight the unintended risks of crypto payments and the importance of effectively managing consumer risk perceptions of digital currencies when used as a means to pay for products and services. This research provides novel insights into how digital currencies and innovative payment technologies shape the psychology of consumer-firm relationships and what firms can do to effectively manage consumer expectations and risk.Type:conference contribution - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Risky crypto: The effect of cryptocurrency as payment method on trust in consumer-firm relationships(2023); Christian HildebrandThe rise of cryptocurrencies is transforming how consumers trade, purchase, and pay for products. The current work explores how cryptocurrencies used as a payment method offered by a firm shape consumer-firm interactions. Through one large-scale pilot study and four experiments, we demonstrate that consumers perceive cryptocurrencies as a payment method riskier compared to traditional payment methods. We introduce a risk-transfer-model of payment method technologies and show how this heightened risk perception induced by the payment method subsequently increases consumers’ perceived interaction risk to engage with the same firm in the future. These risk dynamics negatively impact and ultimately erode consumers’ evaluation of the firm offering the payment method. However, we demonstrate two important boundary conditions: When firms actively communicate the privacy benefits of cryptocurrencies as a payment method or when the firm has a higher trust premium, the negative effects of cryptocurrencies as a payment method are reduced. Our findings highlight the unintended risks of crypto payments and the importance of effectively managing consumer risk perceptions due to new payment technologies. This research provides novel insights into how payment technologies shape the psychology of consumer-firm relationships and what firms can do to effectively manage consumer expectations and risk.Type:conference poster - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Accessing the Untapped Potential of Large Language Models in Banking: A Capability Readiness Framework(2025-06-18)This paper presents a novel capability-based framework for assessing organizational readiness in deploying large language models (LLMs) in the banking sector. While LLMs offer significant potential across domains such as customer service, compliance, and risk assessment, banks face unique deployment challenges due to regulatory constraints, legacy systems, and data sensitivity. Building on the dynamic capability view and adapting maturity levels from the Capability Maturity Model Integration (CMMI), the framework identifies and structures the organizational, contextual, and technical capabilities necessary for effective LLM deployment. It introduces a maturity-scaled self-assessment tool that enables banks to evaluate their current LLM readiness, diagnose capability gaps, and guide strategic investment decisions. Although developed for banking, the framework offers conceptual relevance to other high-stakes, highly regulated sectors.Type:working paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Decentralized Distrust: How Cryptocurrency Payments Undermine Firm Trust(2025-06-18); The rise of cryptocurrencies is transforming how consumers trade, purchase, and pay for products. The current work explores how cryptocurrencies used as a payment method shape consumer-firm interactions. Through one large-scale pilot study, four experiments, and one field experiment, we demonstrate that consumers perceive cryptocurrencies as a riskier payment method compared to traditional payment methods. We introduce the Payment Method Technology Risk Transfer and demonstrate how this heightened risk perception, induced by a payment option, subsequently increases consumers' perceived interaction risk when engaging with the same firm. These risk dynamics negatively impact and ultimately erode consumers' evaluation of the firm offering the payment method. We further demonstrate two essential boundary conditions: When firms actively communicate the privacy benefits of cryptocurrencies as a payment method or when the firm has a higher baseline trust with consumers, the adverse effects of cryptocurrencies as a payment method are reduced. Our findings highlight the unintended risks associated with crypto payments and the importance of effectively managing consumer risk perceptions of digital currencies when used as a means of payment for products and services. This research offers novel insights into how digital currencies and innovative payment technologies influence the psychology of consumer-firm relationships, as well as strategies that firms can employ to manage consumer expectations and mitigate risk effectively.Type:working paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI-Based Claims Handling: A Systematic Performance and Bias Assessment of Large Language Models for Automated Insurance Claims Handling(2025-05-27);