A virtual interactive chatbot that helps to track expenditure and helps manage monthly expenditure and savings according to one’s own budget and stay's up to date with one’s savings, current and credit account.

1. CHATBOT FOR PERSONAL
FINANCE TRACKING
Submitted by,
Aathira N
Anagha M
Gautham S K
Christy Aby Varghese
Guided by,
Prof. Hazeena Yoosaf
Dept. of CSE

2. CONTENTS
• Objective
• Existing scenario
• Proposed system
• System requirements
• Modules
• Software components
• Working
• Functionalities
• Working model screenshots
• References

3. A chatbot is a computer program which
conducts a conversation via auditory or
textual methods.

4. OBJECTIVE
❖ To develop a virtual interactive chatbot that helps to track
expenditure
❖ Helps manage monthly expenditure and savings according to one’s
own budget
❖ To stay up to date with one’s savings, current and credit account

5. EXISTING SCENARIO
Managing money, sticking to a budget and even
handling investment decisions are done with personal finance
apps
• Mint
• Wally
• You need a budget(YNAB)

6. PROPOSED SYSTEM
The idea is to integrate finance tracking into a virtual chat interface
The chatbot gives personalised opinions for each customer/user.
Works as a budget assistant
Enter the monthly budget and expenses often
Users can ask questions about their balance, savings, etc.

7. SYSTEM REQUIREMENTS
Software Requirements
Frontend : Python
Backend : MySQL
Platform : Messenger
GUI : Tkinter
Dataset : Corpus
Libraries : Pretty table
Playsound
Chatterbot
NLTK
Hardware Requirements
System : PC

8. MODULES

9. SOFTWARE COMPONENTS
• Python
Python is an easy to learn, powerful programming language. It has efficient high-
level data structures and a simple but effective approach to object-oriented programming.
Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an
ideal language for scripting and rapid application development in many areas on most
platforms.

10. CONT.…
• Tkinter
Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast
and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk
GUI toolkit.
• MySQL
MySQL is a freely available open source Relational Database Management System (RDBMS) that
uses Structured Query Language (SQL).SQL is the most popular language for adding, accessing and
managing content in a database. It is most noted for its quick processing, proven reliability, ease and
flexibility of use.

11. WORKING

12. FUNCTIONALITIES
User
o Requests the current balance.
o Input expenditure for various items.
o Requests budget in table mode.

13. CONT.…
Chatbot
o To calculate money spent on various factors like rent, food, etc. on a daily and
monthly basis.
o To calculate the total income per month and plan a monthly budget.
o Analyse spending behavior and identifies opportunity to save money by using a
threshold
o Can view expenses and income in table mode.

14. CONT..
Chatbot
o Responses to general queries to make the conversation humane.
o Individual user login after checking the user id and password.
o Plays notification sound for all incoming and outgoing messages.
o Gives alert in case of invalid username or password
o To give alert/notification if a certain category exceeds the threshold or if savings is
too low

15. WORKING MODEL
SCREENSHOTS

16. Individual login window Popup message

17. General queries Specific queries

18. Warning messages

19. REFERENCES
[1] Minghui Qiu,Feng-Lin Li,Siyu Wang,Xing Gao,Yan Chen, Weipeng Zhao,Haiqing Chen,Jun
Huang,Wei Chu.2017. AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics ,Vancouver,
Canada, July 30 - August 4, 2017. Association for Computational Linguistics
[2] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching word
vectors with subword information. arXiv preprint arXiv:1607.04606 .
[3] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares,
Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder–
decoder for statistical machine translation. In Proceedings of EMNLP. pages 1724–1734.