Hackathon Semi Final 2 (S1) – UC4 | NLP with Tally using SpaCy in Python | CA Ramajayam Jayachandran

Summary

This presentation showcases the integration of natural language processing (NLP) with Tally using Python’s Spacey model. It enables users to enter financial transactions in plain English, which are then automatically processed and posted into Tally. The model handles missing data, flagging exceptions, and making the process accessible even for those with no technical background. Additionally, the presenter demonstrates how to export Tally data to Excel for further analysis with AI models, streamlining financial reporting and auditing. The session also discusses the potential for building reusable code libraries and the importance of keeping the system updated as tools evolve.

Takeaways

• 😀 NLP-based system allows non-accountants to easily enter financial transactions in plain English into Tally accounting software.
• 😀 Spacey, a Python-based NLP model, is used to process the transaction text and convert it into proper accounting entries.
• 😀 The system recognizes and handles missing information by flagging it as an exception, which can later be reviewed and corrected by the user.
• 😀 Unlike previous versions, the new model does not require specific keywords like 'create ledger' or 'make payment' for processing transactions.
• 😀 The system is designed to be accessible to people without technical knowledge, allowing them to interact with Tally using simple language.
• 😀 Tally's version 4.0 handles exceptions automatically if required parameters (e.g., debits or credits) are missing in a transaction.
• 😀 Python can be used to export large volumes of data from Tally (e.g., ledgers, daybook, GST data) into Excel in just a few seconds.
• 😀 Exported data can be used with external tools like generative AI models for financial analysis, audit reports, and reconciliations.
• 😀 The process can help automate tasks such as GST reconciliation, TDS items, and other financial reporting tasks.
• 😀 There is an emphasis on building code libraries to handle software upgrades, ensuring long-term flexibility and integration with Tally and other systems.
• 😀 The use of open-source libraries, like Spacey, and the integration of Python in accounting tools can streamline workflows and increase efficiency for accountants.

Q & A

What is the main focus of the presentation?

The main focus of the presentation is the use of Natural Language Processing (NLP) to automate financial transaction entries in accounting software like Tally, using the Spacey Python model.

How does the new NLP model improve transaction entry in Tally compared to the older version?

The new model allows users to input transactions in plain, natural English without needing to use specific keywords like 'Create Ledger' or 'Make Payment.' It automatically processes the text and generates the corresponding accounting entries, whereas the older model required predefined keywords and was less flexible.

What are some examples of transactions that the NLP model can process?

The NLP model can process basic financial transactions such as 'I spent ₹5000 on office books from vendor ABC Corp on 29th April' or 'I sold goods to RAM Ltd.' The system interprets these transactions and generates the appropriate entries in Tally.

How does the system handle missing information in a transaction entry?

If the system detects missing information, such as the name of a party or vendor, it flags the entry as an exception. The accounting application can capture the record and allow the user to modify and save it later.

Can users without programming knowledge use the NLP model?

Yes, the system is designed to be user-friendly. Users can simply click a bat file to initiate the process and input transactions in natural language without needing to know programming or Python.

What additional feature does the system offer regarding data export?

The system allows for a one-click export of Tally data, including ledger data, GST information, stock movement, and other financial details. This data can be used for further analysis, such as financial reporting or tax reconciliation.

How quickly can the data be exported from Tally?

The system can export up to 95% of the data from Tally to an Excel file in just a few seconds, with the example in the presentation showing a file export in 3.96 seconds.

How can the exported data be used in AI models?

The exported data can be uploaded to generative AI models like ChatGPT for various financial tasks, such as reconciliation, analyzing notices, or preparing audit reports.

What is one of the challenges in using this Python-based model for accounting?

One challenge is that accounting software and tools like Tally are frequently updated, so continuous code maintenance and adjustments are necessary to ensure the system remains compatible with newer versions.

What suggestion was made to address the challenge of continuously updating the system?

The suggestion is to create a code library that can handle different versions of Tally and other tools, allowing for easier updates and integration of new features without having to rewrite the entire codebase.