Problem statement

Market & user research

Competition analysis

User persona

Rahul

Age: 24

Occupation: Marketing Analyst

Financial goals: Learn how to file taxes, Save up for a solo trip, Save for an emergency fund

Pain-points:

Recently started earning, lacks discipline, frequently stressed about overspending and where to draw a line

Afraid of asking questions about the complicated jargon of investing, needs help and direction to get started

Finds it challenging to actually save up, since he is not sure where to cut down

Gen Z’s unmet needs

Neha

Age: 19

Occupation: College student

Financial goals: Save for a new laptop, Start investing from internship income

Pain-points:

Limited income, crucial to track where money goes and where she can save better

Impulse expenses, ends up spending on fashion and lifestyle

Lacks financial literacy and confused from too many content creators “influencing”

Solution space

1. Intelligent expense categorization

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Feature brief: Uses AI to automatically categorize user transactions into predefined spending categories and merchants, making it easier for users to track their expenses without manual input.

Pain-point addressed:

Users often struggle to keep track of where their money goes.

Manual categorization of expenses is time-consuming and error-prone.

Difficulty in identifying spending patterns due to uncategorized transactions.

Value to the user:

Saves time by automating the categorization process.

Provides a clear view of spending habits and patterns.

Helps in identifying areas where users can cut down expenses.

Enhances budgeting accuracy and financial planning.

How it works:

The app takes access to reading SMS messages in order to acquire credit and debit data from bank’s transaction messages.

An ML model trained on merchant data and their category is to be leveraged to identify merchants in transactions and classify.

Users can still make manual corrections in category (for example - A Swiggy transaction may actually belong to Dining instead of Grocery).

2. AI powered peer benchmarking

Feature brief: Compares user’s spending habits with anonymized data from users in the same income bracket & age to provide benchmarking insights, helping users understand their financial behavior in a broader context.

Pain-point addressed:

Lack of context on whether one's spending habits are normal or excessive.

Difficulty in identifying areas of overspending without comparative data.

Users often feel isolated in their financial struggles without knowing how others manage similar incomes.

Value to the user:

Provides context and perspective on personal spending habits.

Helps identify areas where users are overspending compared to peers.

Encourages healthier financial behavior by showing best practices from similar users.

Increases motivation to improve financial habits through social comparison.

How it works:

During app onboarding, collect user’s income and age. Also collect income cycle info to accurately track spending on income.

Make anonymized data available to ML models to train and create peer groups for each user based on income and age data.

Compare and provide insights on spending across categories again the user’s own spending in that time period.

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3. AI assisted financial goal management

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Feature brief: Allows users to define financial goals, such as saving a certain amount by a specific date, and provides personalized advice through AI, using spend analytics and peer benchmarking to create a plan.

Pain-point addressed:

Difficulty in setting realistic financial goals and tracking progress.

Lack of personalized advice on how to achieve financial goals.

Uncertainty about how current spending habits impact future financial success.

Value to the user:

Helps in setting achievable financial goals with clear timelines.

Provides a tailored plan to achieve these goals, taking into account current spending habits and benchmarks.

Continuously tracks progress and adjusts recommendations to stay on track.

Encourages disciplined saving and spending behavior.

How it works:

In Goals section of the app, users can create financial goals.

When creating goals, users can be given templates such as - Save for your next trip, Your next gadget, Save for higher education, etc.

To create a financial goal, user needs to provide - goal name & description, total amount to be saved, duration to achieve.

Once created, the app will leverage peer benchmarking data to suggest which categories the user can save money and give actionable suggestion on how to do it.

Users can add a manual entry of money saved for that financial goal.

4. AI professor for financial learning

Feature brief: An AI-driven chatbot that provides personalized advice on important financial topics and answers user queries, serving as a virtual financial advisor.

Pain-point addressed:

Users often don’t know where to find reliable and financial advice.

Lack of education that is relevant to their situation - income, age and peer group

Value to the user:

Simplifies complex financial topics into understandable insights.

Encourages positive behavior of asking questions and getting answers.

How it works:

Users can ask Prof. FinLearn any question to get GPT powered answers via OpenAI’s public APIs.

Along with the question asked by the user, user’s age, income and expense data, peer benchmarking data will be forwarded to OpenAI in the prompt.

The answers will be customized and personalized to the user’s needs.

Users can also start with readymade templates on financial topics to educate themselves.

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Thank you!

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