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Client
An accounting company in USA wanted to streamline accounting operations to improve the overall productivity and efficiency of the accounting company and its associated clients.
Problem Statement
As part of the tax filing process, accounting firms have to manage vast amounts of legal documents from their clients. The manual review and categorization of these documents are time-consuming and prone to errors. Additionally, accounting firms have to analyse the transactions in excel shared by their clients during filing returns and taxes and feed them manually to the accounting software which is a very cumbersome and tedious task.
Result
Biz4Solutions developed a unique AI-powered solution to reduce repetitive and time-consuming tasks for accounting firms and their clients, saving valuable time. The document mining application uses advanced AI techniques to identify types of legal documents accurately. Additionally, the AI-driven DataMine application enables clients to share financial transactions directly through third-party applications, connecting to financial institutions without the need for Excel files. AI models analyse these transactions to identify their types, streamlining the entire transaction review process.
Overview
DataMine is an application that automates many of the mundane repetitive operations of an accounting firm with the help of artificial intelligence and intelligent algorithms. It reduces the chances of human error by automating manual reviews of the documents, payroll and financial transactions. There are 3 modules namely, DataMine, DocumentMine and Payroll Mine. Datamine application is used to identify the financial transaction types coming from 3rd party applications like Yodlee and submit the corresponding general ledger entries to 3rd party accounting software. DocumentMine application identifies the legal document by analysing the text within documents to understand context and content and accordingly classify the documents and then submit the documents to 3rd party accounting software. PayrollMine application identifies the transaction type and puts it in the corresponding GL entries. These applications use advanced AI model techniques like optical character recognition, natural language processing, deep learning, named entity recognition, automated workflow integration, continuous learning and adaptation and machine learning. These techniques reduces the reliance on manual intervention, speeds up processing times, and minimizes errors, and streamlines the accounting operations effectively.
Developmental Challenges
Our team came across some developmental challenges as mentioned below:
- Getting accurate results through AI was challenging due to insufficient Training data sets and poor quality dataset. AI model worked well with initial training data sets but failed with both further validation and real-world data.
- Range of AI algorithms were available as starting points, and each had its own strengths and weaknesses. For example, logistic regression algorithms can move projects forward quickly but provide only binary results.
- Processing a document which has multiple pages in it, for example, a contract that has several pages in it, processing such document was a challenge specially when many such documents are included in one single pdf file and with possibility of them shuffled, that is not in the desired sequence. During the operations, User could also add multiple documents to process through the system.
How did we resolve these development challenges?
- We aimed to expand the dataset by incorporating real-world data from a variety of sources, thereby ensuring greater diversity and increasing its volume. Additionally, we utilized regularization techniques to adjust the models, mitigating overfitting through various optimization methods to produce simpler and more accurate results. Common regularization methods include ridge regression, lasso regression, and elastic net.
- The team conducted extensive research to determine the most suitable AI algorithm for document identification and transaction analysis. We opted for docAnalyzer.ai, an advanced AI embedding tool designed for document analysis. This cutting-edge technology enables a thorough and contextual comprehension of the documents, providing accurate and insightful findings.
- Implemented a worker environment using Celery which would create jobs for processing each page and could process multiple pages at the same time.
