Literature Review - The Impact of AI on Fintech Design and Human-Computer Interaction

The concept of Artificial Intelligence (AI) has fascinated researchers since Alan Turing’s question in 1950 about the potential of machines to “think.” The question of AI’s capabilities and limitations has spurred technological advancements and has led to significant transformations in work and life (Waardenburg et al., 2021). AI’s rapid adoption follows a wave of large-scale digitalization, which has accelerated AI’s integration into various industries, including the workplace, where it has sparked discussions on its impact on jobs, team dynamics, and productivity (Waardenburg et al., 2021). This blog post reviews literature on AI’s impact on fintech design, focusing on Human-Computer Interaction (HCI) in AI, user experience (UX) and user interface (UI) design, and AI’s role in enhancing collaborative design workflows.

Human-Computer Interaction in the AI Dimension

In the early days of computing, applications followed a technology-centered design approach, which often neglected user needs. This stance gradually shifted as the significance of user experience became more apparent, leading to a more human-centered design philosophy, foundational to the field of HCI (Xu et al., 2022). Today, HCI promotes an interaction design that prioritises user needs, offering a personalised, user-driven experience with technological products.

With AI’s emergence, Xu and Shneiderman (2022) propose the concept of “Human-Centered AI” (HCAI), advocating that AI tools should cater to human needs by being user-friendly, ethical, and supportive rather than disruptive. HCAI stresses the need for AI to function as a collaborator rather than an autonomous tool. Unlike traditional automation, AI-powered systems possess self-adaptive capabilities, allowing them to perform tasks in unforeseen situations autonomously. This unpredictability creates unique challenges in controlling AI’s actions, raising questions about its ethical implications and the risks of relinquishing decision-making power to AI (den Broek et al., 2017; Kaber, 2018; Rahwan et al., 2019; Xu & Ge, 2020).

As HCI frameworks help evaluate interface effectiveness, the integration of AI into fintech UI/UX is transforming users' digital experiences. Modern AI capabilities like predictive analytics, intelligent automation, and personalised recommendations are enhancing user engagement in fintech applications. However, balancing the benefits of AI with ethical considerations and user-centered design remains a delicate challenge for HCI practitioners (Paneru et al., 2024).

AI in UX Design

In designing digital products, UX is critical in aligning with end-user needs and expectations (Stige et al., 2023). End-users’ demands influence designers’ choices in functionality, aesthetics, and usability (Silva-Rodríguez et al., 2020). UX design, by nature, is an iterative, problem-solving process that involves creativity, empathy, and an understanding of user expectations (Oulasvirta et al., 2020). Traditionally, designers relied on data to inform their choices, but with AI, they have new tools to streamline these processes, reducing both workload and time.

AI’s impact on UX design has spurred debate, with some researchers seeing AI as a tool to enhance design processes, while others are concerned it might replace traditional methods (Oh et al., 2018). A prominent example is the use of AI to automate design analyses, such as generating adaptive interfaces that modify based on user behaviour (Johnson et al., 2019). These AI-driven innovations in UX support designers by processing large datasets to identify patterns and preferences, allowing for a more user-centered approach to design (Stige et al., 2023).

However, AI’s role in UX remains under-researched, particularly in terms of its limitations and the necessary skills required for effective collaboration with machine learning (ML). Abbas et al. (2022) reviewed challenges UX designers face when integrating AI, particularly ML, in design tasks. One challenge highlighted is a communication gap between UX designers and data scientists, which can hinder effective collaboration. Another is the lack of tools for designers to experiment with ML in prototyping. Current UX practices and education often lack resources to equip designers with ML integration skills, prompting some designers to abandon ML-powered solutions due to technical challenges (Abbas et al., 2022).

Dove et al. (2017) further investigated the obstacles UX designers face with ML and found three major issues: lack of competency to apply ML in design contexts, insufficient ethical considerations, and a limited understanding of ML’s capabilities. These findings suggest that the UX field requires better training and resources to enable designers to work confidently with ML, bridging gaps between design requirements and technical capabilities.

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AI in UI Design

Like UX, UI design is also undergoing transformation with AI integration. UI design is an iterative process that involves structure design, interaction design, and visual aesthetics (Yang, 2019). Traditionally, UI design has been labor-intensive, requiring multiple cycles of prototyping and adjustments. AI offers a way to simplify this process, enabling designers to work faster while maintaining high-quality standards (Pandian et al., 2020).

A prominent example of AI’s utility in UI design is found in Airbnb’s use of AI to convert sketches into code, enabling rapid prototyping (Ganapathy, 2018). AI can also automate tasks like image resizing and colour adjustments, reducing repetitive work and allowing designers to focus on more creative aspects. The advantages of AI in UI design extend to data-driven personalization, which can provide more tailored experiences for end-users based on their interactions (Yang, 2019). AI-driven personalization can help UI designers create engaging and user-centric applications that meet diverse user needs more effectively.

Ganapathy (2018) noted that AI integration could eventually democratise design, allowing designers to leverage automated tools to create hundreds of design variations and select those that best meet user needs. However, there are also drawbacks, such as high maintenance costs, potential job displacement, and challenges in incorporating emotional intelligence in AI-powered design. AI’s inability to generate original creative insights or understand cultural nuances may limit its usefulness in complex design tasks that require empathy and human intuition (Ganapathy, 2018).

Research Gaps

Despite significant advancements, the literature reveals several gaps in understanding AI’s full impact on design processes. While much of the research explores AI’s potential to automate repetitive tasks and assist with data analysis, less attention has been paid to how AI can be seamlessly integrated into the iterative cycles of UX and UI design. Research on how AI could enhance the creative aspects of design by supporting ideation, rapid prototyping, and adaptation during iterative cycles remains scarce.

The lack of a practical framework for AI integration into iterative design workflows suggests a need for further research. A framework could provide designers with the tools and methodologies necessary for using AI effectively throughout the design cycle, from ideation to prototyping and final product. Additionally, a better understanding of how AI can foster collaboration between human designers and machine tools is necessary to maximise AI’s potential in UI and UX design. Furthermore, more in-depth studies on the ethical implications of AI in design could help establish guidelines for AI’s responsible use, ensuring that human values and creativity are maintained in the design process (Paneru et al., 2024).

Conclusion

AI is transforming UI/UX design, enabling designers to automate tasks, process large datasets, and create personalised user experiences. However, challenges in integrating AI into design workflows, communication gaps between designers and data scientists, and ethical considerations remain significant obstacles. As AI continues to evolve, there is a growing need for frameworks and tools that support iterative design processes, as well as training and resources for designers to use AI effectively. Addressing these research gaps will allow AI to enhance creativity and productivity in fintech design while preserving human-centered values and ethical standards.

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