Spend/Wealth Management/BharatpeVerse

1. Introduction
The purpose of the Spend Analysis Feature in the PostPe App is to promote
financial awareness and increase user engagement by facilitating budgeting
and expense tracking, identify spending patterns, optimize spending, support
wealth management and financial planning, and enhance the overall user
experience.
It serves as a valuable tool for users to manage their finances effectively and
achieve their financial goals. Also it will help to increase the visibility of
merchant as we will show BharatPe merchants nearby of category similar to
user spending history.

2. Understanding Spend Analysis
● Overview of Spend Analysis: Spend analysis is a process of
examining and categorizing expenditures to gain insights into
spending patterns and trends. It helps individuals understand
where their money is going and identify opportunities for
optimization.
● Mechanism of Spend Analysis in PostPe: Within the PostPe
App, the Spend Analysis Feature operates by categorizing
transactions based on user spending data like SMS. It
aggregates transaction data and applies algorithms to analyze
spending behavior, providing users with a comprehensive
view of their financial activities.

3. Analyzing Spending Patterns
● Category-wise Spend Analysis: The Spend Analysis Feature
categorizes transactions into different spending categories,
such as groceries, entertainment, transportation, etc. Visual
representations, such as charts or graphs, illustrate the
distribution of spending across these categories, helping users
identify where their money is being spent the most.
● Trends and Patterns: By analyzing historical transaction data,
the feature identifies monthly spending trends and highlights
any seasonal variations in spending behavior. This information
enables users to anticipate fluctuations in expenses and plan
accordingly.

4. Insights Generation
● Key Insights from Spend Analysis: Users gain valuable
insights into their financial behavior, such as identifying top
spending categories, recognizing areas of excessive spending,
and understanding changes in spending patterns over time.
These insights empower users to make informed decisions
about their finances.
● Comparative Analysis: The Spend Analysis Feature allows
users to compare current spending patterns with previous
months or years. Additionally, it benchmarks users' spending
habits against similar demographics, providing context for
their financial behavior and highlighting areas for
improvement.

5. Wealth Management Advice
The Spend Analysis Feature offers tailored advice based on
users' spending habits and financial goals. Recommendations
include practical budgeting strategies, tips for cutting costs in
specific categories, and guidance on achieving financial
objectives. These insights empower users to make informed
decisions and optimize their spending for better financial
outcomes.
Additionally, the feature provides targeted tips for reducing
expenses in specific categories, such as dining out or
entertainment, through actionable steps like exploring
discounts or opting for cost-effective alternatives.

6. Implementation and User Experience
● Implementation of Spend Analysis Feature: The Spend
Analysis Feature is seamlessly integrated within the PostPe
App, accessible to users with a few taps. The user interface is
designed to be intuitive and user-friendly, allowing users to
navigate through spending analysis and insights effortlessly.
● User Experience Enhancements: To enhance the user
experience, the feature offers customization options, allowing
users to tailor spending analysis according to their
preferences. Interactive visualizations, such as charts and
graphs, provide users with a clear understanding of their
financial data, making it easier to interpret and act upon.

7. Implementation and User Experience
● User SMS data for a month, containing both debited and credited amounts along
with location information, is collected.
● Data cleaning techniques, utilizing Python libraries such as NumPy, are applied to
preprocess the collected data.
● The cleaned data undergoes categorization using the OpenAI API to classify
transactions into different spending categories.
● Due to the large size of the input data, it is divided into tokens and processed in
chunks to fit within the token limit of the OpenAI API. Each chunk is individually
processed, and the results are aggregated to obtain the final output.
● The final output includes total expenses incurred for each spending category and is
further filtered based on location.
● The aggregated expenditure data is then fed into the OpenAI API to generate four
insights regarding the user's spending habits and wealth management.

8. BharatPe & Postpe Multiverse
We will leverage BharatPe to curate a list of nearby merchants
tailored to the user's spending habits. By analyzing their
expenditure patterns, we'll identify merchants frequented by the
user, ensuring relevance and convenience. These merchants,
selected based on the user's transaction history and preferences,
will be seamlessly integrated into the PostPe app interface. Users
will benefit from this personalized approach, as it enhances their
overall experience by providing convenient access to merchants
they commonly engage with. Through this collaboration between
BharatPe and PostPe, users will discover nearby merchants that
align with their spending behavior, fostering a more intuitive and
user-centric platform.