Algora - Personalised Personal Finance Management Platform

A platform enhancing financial literacy and personal finance management for women with no prior knowledge through personalized learning modules in regional languages and AI chatbot support .

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Inspiration

In India, particularly in tier 2/3 cities and rural areas, many women have low financial literacy levels. This is primarily due to the fact that 97% of them have only completed secondary education, with no formal education in personal finance provided in schools. Additionally, many rural women are not familiar with English and prefer regional languages. Most available content in the personal finance niche is in English, which hinders accessibility. Consequently, most women rely on traditional investment options like buying gold, fixed deposits, or post office schemes, which may not keep up with inflation, ultimately defeating the purpose of saving.

What it does

We propose "Algora," an online platform designed to enhance financial literacy and personal finance management. Algora begins by assessing users' financial literacy levels through a comprehensive questionnaire. Based on these assessments, personalized learning modules are provided in multiple regional languages, tailored to individual needs. To support users on their learning journey, Algora features an AI-powered chatbot. This chatbot answers queries and addresses doubts related to personal finance, offering support in various languages to accommodate diverse user preferences and needs. Moreover, Algora goes beyond education to practical application. It creates personalized investment strategies based on users' risk profiles and preferences, providing an integrated investing experience directly within the platform.

How we built it

Vidit, a full-stack developer, used the MERN stack to build the web app. For the chatbot, Aayush, who is skilled in Machine Learning and GenAI, created a custom Flask API using Google's Gemini API and fine-tuned it to answer only personal finance-related queries. Salique developed the YouTube video recommendation system for the website by scraping data using the YouTube API and building an ML model using logistic regression. He also developed the UI and UX of the website. For authentication, we used Clerk to provide OTP-based logins, ensuring top-notch security for the end user.

Challenges we ran into

We faced challenges in fine-tuning the Gemini API and figuring out how to deploy the model and use it locally. Additionally, preparing the quiz was difficult as it required handling both numeric and multiple-choice inputs.

Accomplishments that we're proud of

After building the web app, we tested it with a woman who was a security guard by profession and belonged to a rural area. She found the app extremely useful, especially the investment recommendation and AI chatbot features, making her our first user. This positive feedback validated our efforts and gave us confidence in the app's potential impact.

Check out our user review Video: Watch Video

What we learned

Throughout this journey, we learned the importance of collaboration and teamwork, which improved our problem-solving skills. We also learned how to use GitHub for collaborating and working together from different locations. Additionally, we gained extensive knowledge about GenAI and fine-tuning.

What's next for Algora - Personalised Personal Finance Management Platform

Next, we plan to introduce features like speech-to-text in the AI chatbot so that users who cannot type in English can get answers in their regional language. We are also considering increasing the number of regional languages supported in the learning modules.

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