DIY Credit Repair: AI Credit Dispute Letter Tool

Powered by OpenAI’s language models, this portfolio project offers a unique approach to repairing credit profiles. It uses personalized dispute letters to challenge negative marks on credit reports. The system was developed after seven months of research, design, and development, with a holistic and data-driven approach.

Behind The Scenes: My Process

When credit gets damaged, most don’t know how to correctly repair it. Myself included. They seek individuals or companies to help – but in reality, that is usually very cumbersome. DIY Credit Repair, powered by OpenAI’s GPT 3.5, 4, and Divinci language models takes a brand new and fresh approach to how credit profiles are repaired through the dispute process. I was tasked with building a system which analyzes the intricacies of credit profiles and identifies areas of improvement. It generates personalized dispute letters, designed to challenge inaccurate or unfair negative marks on credit reports. These dispute letters are crafted with precision and backed by comprehensive research, ensuring the highest chances of success. I spent seven months doing user experience research, user interface design, and full stack development. My approach was a holistic, yet data-driven one. This is a behind the scenes look at what it took.

Steps Within My Process:

UX Research + UX Design
UI Design + Prototyping
Branding + Full Stack Development

My process first began by doing extensive user research into the credit repair industry – particularly apps and software that repair credit with the assistance of AI. I then analyzed the findings to identify common pain points and challenges faced by users. This allowed me to gain a deeper understanding of their needs and requirements. Next, I conducted a competitive analysis to examine the strategies and approaches employed by other credit repair companies and systems such as SmartCredit and Credit Versio. This helped me identify gaps in the market and potential opportunities for differentiation. Based on the insights gathered from user research and competitive analysis, I designed and developed a comprehensive user journey map. This map highlighted the various touchpoints and interactions users have with credit repair services, from initial research to signing up for a program and monitoring progress. The biggest thing I noticed was that the process was absolutely difficult for the user to handle. There had to be a better way to go about this. So I designed an interface where the user could chat with the AI itself, utilizing data directly from the consumer credit reporting agencies.

I began with the wireframes first, utilizing the data insights I had gathered in the beginning of my process. I then progressed into high fidelity mockups and a prototype. The interface I designed allowed users to input their personal information and credit history by connecting their credit profiles, which the AI would then analyze to provide personalized recommendations and strategies within credit disputes for improving their credit score. Users could also ask questions and receive real-time answers from the AI, making the process more interactive and user-friendly. To ensure the accuracy and reliability of the AI’s recommendations, I implemented a rigorous data validation process. This involved cross-referencing information from multiple credit reporting agencies and conducting regular updates to ensure the AI was up-to-date with the latest credit scoring algorithms and industry regulations.

Next up was the development phase. In this phase I worked closely with the stakeholders and team to bring the interface to life. We used agile development methodologies to ensure a smooth and efficient process. Regular meetings and communication were key to keeping everyone on the same page and addressing any issues or challenges that arose along the way. The development phase involved building the necessary backend infrastructure to support the AI’s analysis and recommendations, as well as integrating with the credit reporting agencies’ systems to retrieve and update user information. We also implemented robust security measures to protect user data and ensure compliance with privacy regulations. Once the development phase was complete, we conducted thorough testing and quality assurance to identify and fix any bugs or issues. User feedback was invaluable during this phase, as it allowed us to fine-tune the interface and make any necessary improvements. We also conducted usability testing to ensure that the interface was intuitive and easy to navigate for users of all levels of technical expertise.

This brought us to the current progress within the process, which is branding, planning, and marketing it for launch. DIY Credit is slated to be launched in mid-2024.

The Results