Ethical AI for Lending
TIMELINE
Fall 2025
ROLE
HCI Research, UX Design, UI
INDISTRY
AI, Lending and Finance
SPECIALTY
Explainable AI, Strategy and Policy, Human Centered Design, UI
INTRODUCTION
As artificial intelligence increasingly shapes who is deemed creditworthy, financial decision-making has become faster but also more opaque due in part to “black box” machine learning models that are difficult to explain. Millions of borrowers now receive instant, algorithmic approvals or denials with little understanding of why. This project examines how human-centered design and ethical AI principles can enhance the transparency, fairness, and empowerment of automated lending systems. Grounded in the frameworks of Data Feminism (D'Ignazio and Klein, 2020) and Design Justice (Costanza-Chock, 2020), this research investigates how explainability, participatory design, and examining power can be woven into digital lending dashboards, transforming the “black box” of credit decisions into a space for user understanding, agency, and ambition.
BACKGROUND AND PROBLEM SPACE
AI-driven lending is rapidly expanding, transforming how financial institutions assess risk and make credit decisions. The global market for AI in financial services was valued at $38.36 billion in 2024, and is projected to reach $190.33 billion by 2030, reflecting a compound annual growth rate (CAGR) of about 30.6% (1). In 2025, roughly 78% of global banking organizations reported using AI in at least one business function (2). This rapid adoption is not inherently negative, as machine learning tools have already proven valuable in areas such as fraud detection, document intake, and customer service. However, as lenders increasingly rely on AI models to determine borrower eligibility and loan terms, a critical question arises: how can we ensure that borrowers maintain agency and that power does not shift entirely to favor the lender? This project will seek to answer that question by addressing three problems with AI-driven lending:
LACK OF TRANSPARENCY
Existing systems tend to provide minimal explanations limited to vague or technical “adverse action” notices that fail to clarify how data points, algorithms, or model weights influence the outcome. This “black box” effect erodes user trust and prevents applicants from learning or improving.
AMPLIFICATION OF INEQUALITY
ML models in lending rely on historical data that may encode systemic bias. Even when protected attributes like race or gender are excluded, proxy variables (like ZIP code, education, and spending patterns) can still replicate inequity, leading to certain applicants, especially those from marginalized or nontraditional backgrounds, being excluded or misjudged by algorithms.
LIMITED BORROWER AGENCY
In most AI lending/ finance interfaces, users have no way to contest, clarify, or learn from a decision. Once the algorithm outputs “approved” or “denied,” the process ends, removing the opportunity for human review, personalized feedback, or educational guidance for next steps.
This perpetuates cycles of financial exclusion as denied applicants don’t know what to fix or how to qualify in the future.
This project examines how we can enhance transparency, education, and user confidence in AI-driven lending by designing systems that foster participation and understanding, rather than passive acceptance. We will explore how we can demystify the black box of machine learning, giving borrowers insight into how decisions are made and concrete steps to improve their financial standing. Beyond initial approval, the project envisions AI not as a gatekeeper, but as a companion tool that helps borrowers maintain healthy financial habits, build credit, and achieve long-term goals.
The aim is not to “outsmart” AI systems or secure approvals for unqualified applicants, but to align human and algorithmic decision-making toward mutual benefit: empowering borrowers to make informed improvements while protecting lenders through transparency, trust, and financial literacy.
AI in Finance Market Size, Share, Industry, Overview, Growth, Latest Trends. Retrieved from https://www.marketsandmarkets.com/Market-Reports/ai-in-finance-market-90552286.html
Superagency in the workplace: Empowering people to unlock AI’s full potential. Retrieved from https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
RESEARCH PROCESS
Process overview- user and industry We developed our personas based on qualitative data gathered from the 6 developer interviews and 2 stakeholder workshops, supported by insights from a comprehensive competitive analysis. These personas helped align design and engineering teams around the needs of both enterprise developers and non-technical decision makers. We shared the initial personas with internal tech leads and refined them based on feedback to ensure they reflected actual developer workflows.
USER RESEARCH
User personas were created based on qualitative data gathered from stakeholder interviews, survey responses, and insights from a comprehensive competitive analysis. These personas helped align feature ideas with the lived experiences, motivations, and pain points of real borrowers navigating AI-driven lending systems. Each persona represents a distinct perspective on financial literacy, trust in automation, and emotional response to loan decisions, from the cautious applicant seeking to improve their financial situation, to the digitally confident user wanting actionable insights. Grounded in principles of Design Justice and Data Feminism, the personas help guide design decisions that increase agency to lift the burden from marginalized groups and shift power back to the user.
Survey result charts
INDUSTRY RESEARCH
To better understand current practices in AI-driven lending and financial management, a competitive analysis was conducted across leading platforms to identify how these products communicate creditworthiness, support financial literacy, and handle transparency around loan decisions. While many leading products provide strong budgeting tools and instant credit insights, few offer clear explanations for AI-driven approval or denial outcomes, and many lack actionable feedback that empowers users to improve.
LESSONS LEARNED
To better understand current practices in AI-driven lending and financial management, a competitive analysis was conducted across leading platforms to identify how these products communicate creditworthiness, support financial literacy, and handle transparency around loan decisions. While many leading products provide strong budgeting tools and instant credit insights, few offer clear explanations for AI-driven approval or denial outcomes, and many lack actionable feedback that empowers users to improve.
USER PERSONAS
UX GOALS & DESIGN OBJECTIVES
The research phase uncovered key user behaviors, pain points, and opportunities, which we synthesized into a focused set of findings. These insights were then translated into clear design goals and experience principles that shaped our direction as we moved into concept development. From there, we began ideating through sketching, user flows, and low-fidelity wireframes, testing early concepts against the user needs identified during research. These artifacts laid the foundation for iterative design and usability testing in the next phase.
DESIGN GOALS
Streamline Developer Onboarding
Reduce time and cognitive load for new users to understand and start using the platform.
Enable Seamless Navigation
Create a clear central hub with intuitive pathways, accessible documentation, and responsive feedback to support confident, self-guided use.
Accelerate Exploration & Adoption
Empower developers to independently discover, test, and integrate platform features without unnecessary friction or sales dependencies.
Create a Cohesive, Scalable Design System
Ensure UI consistency, reusability, and accessibility across both the marketing site and the Developer Portal.
ETHICAL PRINCIPLES
Clarity Over Complexity
Prioritize directness, plain language, and intuitive structure, especially for complex technical content. Users should never be surprised by platform behavior; consistency builds credibility.
Empower Through Feedback
Give real-time, contextual feedback to help users understand the outcome of their actions (e.g., error states, confirmations).
Trustworthy and Predictable
Build user trust through transparent authentication flows, clear permission scopes, and reassuring feedback that communicates protection without overwhelming the experience.
IDEATION
To support both business and technical adoption, we intentionally split the MVP launch into two distinct phases, each tailored to a different user group.
PHASE 1: MARKETING WEBSITE MVP
Phase 1 focused on the marketing side of the platform, designed to generate early interest and secure buy-in from business decision makers. This initial launch prioritized storytelling, visual polish, and clear articulation of the platform’s value, key factors in aligning stakeholders and attracting early partnerships. Bryte IQ was presented on stage at SCTE TechExpo 2024 by Danny Bowman EVP of product at Charter Communications, who highlighted its innovative capabilities and how it will facilitate collaboration with other companies on behalf of the customer. (Press Release) Once Phase 1 achieved its goal of market visibility and executive support, we rapidly transitioned to Phase 2: the Developer Portal.
PHASE 2: DEVELOPER PORTAL MVP
Phase 2 prioritized functionality, clarity, and a streamlined user experience tailored to the technical audience. This phased approach allowed us to build early momentum while keeping the long-term developer experience at the center of our roadmap. The production goal of mid-July was met successfully.
Due to confidentiality restrictions, select visuals and content have been redacted or recreated for illustrative purposes. The UX process, design rationale, and results presented are representative of the actual work.
TESTING
USER FLOWS AND WIREFRAMES
To drive early engagement and ensure alignment with business stakeholders, I crafted a user flow for the marketing site that clearly communicated Bryte IQ’s value. Our goal was to guide visitors, particularly non-technical audiences, through a compelling narrative that showcased the platform’s strategic benefits.
While the initial navigation plan included a broader set of features, we collaborated closely with stakeholders in working sessions to distill it down to only the most essential elements for the MVP. This intentional simplification enabled us to launch quickly with a polished experience that fostered executive buy-in and increased market visibility.
I created Homepage wireframes incorporating storytelling and visual clarity into the core user flow. We emphasized concise messaging, visual hierarchy, and entry points that guide our non-technical user group toward deeper exploration. Additionally, the case study page wireframe showcases how we planned to build credibility by highlighting real-world applications and strategic partnerships. Together, these wireframes represent early structural thinking that informed visual design and content development in later stages.
HOME PAGE
CASE STUDY
HIGH FIDELITY PROTOTYPE
With the foundation grounded in research, strategic prioritization, and a scalable design system, I brought the Bryte IQ experience to life through a high-fidelity prototype. This interactive prototype offered a realistic preview of the end-to-end experience and served as a critical alignment tool for stakeholders, enabling early usability feedback, design validation, and seamless transition into development.
RESULTS AND NEXT STEPS
RESULTS
Website traffic climbed from (xx) visits in the first week to over (xxxx), a 700% increase that reflected heightened interest and engagement following our announcement.
Similarly, the number of account registration requests surged by 675% following the launch at SCTE TechExpo, signaling strong interest among developers.
Onboard, an early partner, integrated Bryte IQ to streamline bulk internet services in multifamily housing, demonstrating the platform’s potential to drive operational efficiencies and enhanced customer experiences.
Secured additional strategic partnerships to expand platform adoption (details under NDA).
WHATS NEXT?
Our team has a clear roadmap to evolve the platform in ways that deliver greater value to users and align with business objectives. In the near term, we’ll roll out Dark Mode as an accessibility-driven quick win and introduce status monitoring to give users real-time visibility into integration health.
Building on feedback uncovered during user research, we will also implement flexible user management and team-based collaboration, enabling organizations to structure role and permission-based workflows that support seamless collaboration across teams.
Looking back at our design goal to Accelerate Exploration & Adoption, advancing automation will play a central role in our strategy. To empower self-service and reduce friction, we’ll launch a Help Center with an integrated Knowledge Base and provide a streamlined process for support ticket submission.
From a business standpoint, upcoming releases will introduce a monetization strategy with transparent billing and a usage dashboard, giving organizations greater insight and control over API consumption. Together, these enhancements are designed to strengthen user trust, drive adoption, and ensure the platform scales effectively to meet enterprise-level demands.
