Case Study

AI-powered Financial Document Intelligence

A top audit company in the US increases the time-to-value efficiency of audits with AI-powered document intelligence.

A leading US-based audit business embraces digital transformation through an AI-powered document intelligence solution to deliver better value in audits to its clients.
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Industry & Region : Audit and Advisory Services, USA
Technology Stack:
Serverless Cloud Computing Platform: AWS Lambda, Programming Language: Python, NLP Model: FINBERT, CDQA, BERT, Named Entity Recognition: Spacy/BERT, Deep Learning: Keras, ML Platform: TensorFlow, Scikit, Web UI: Django, JavaScript Library: React, Image-to-Text Conversion/OCR: PDFMiner, Topic Modelling: Latent Dirichlet Allocation (LDA)
Client Overview
Our client is a reputed audit company in the US enabling businesses to transform their audit processes by leveraging modern technology solutions that streamline processes and generate actionable insights for intelligent business decisions.
Business Problem
To grow the business and meet its revenue targets, the audit company sought to enhance its audit-related processes. However, they faced challenges with their audit deliveries that were getting delayed due to the manual effort and time-consuming nature of extracting information from the large volumes of financial documents such as 10k reports and LPAs. These financial documents would often have information represented in tables and extracting this information involved manual effort. Overall, these manual processes were not only labor-intensive but also error-prone, leaving room for reputational damage.
To deliver on its mission, the company needed a solution that would automate documentation processes, extract relevant information, and offer prompt and accurate responses to auditors’ queries to reduce the turnaround time of the overall audit process. This would help them offer better customer experiences, scale the business, and enhance revenue growth.
Solution Offered
With KANINI’s help, the audit company leveraged AI-powered document intelligence, empowering auditors to deliver high-quality audits in record time and contribute toward organizational growth.
Our teams studied the company’s existing processes to identify the key transformation opportunities and built a prototype to demonstrate how our next-gen data platform with enhanced NLP (Natural Language Processing) and ML capabilities would transform audit-related experiences for customers, auditors, and other stakeholders while establishing a data-driven culture across the enterprise.

Here’s how KANINI’s document intelligence solution combined the auditor’s expertise with AI technology prowess to turn around the client’s audit process –

  • Document Ingestion and Classification: The solution allowed fast ingestion of large volumes of text-heavy financial documents in diverse formats, either individually or in batches. It extracted text and tabular data using OCR techniques. This data was then transformed and contextualized using different NLP techniques and specific financial entities were recognized using AI algorithms. Further, a taxonomy of data was created for documents and later used for classifying the documents.
  • Interactive Q&A: In addition to automating classification, the users had been seeking a solution that would allow them to ask questions and get accurate answers. To enable this Q&A feature, we used a deep learning-based AI model to train on the extracted data. Every time a user would ask a question, this model would trigger one or more relevant answers.  The auditors could now get prompt and accurate contextual responses to their queries, removing manual dependencies and speeding up the overall audit process.
  • Human AI Feedback Loop – The application also offered the ”Human AI Feedback Loop” feature that allowed the user to give feedback on the AI recommendations. This feedback was used to train the model further and get more accurate results from the solution in the future. This feature was created to ensure that the users are always in control of the outcomes.
  • Text Exploratory Data Analysis (Text EDA): The EDA techniques enabled the extraction of actionable insights from the uploaded 10-K reports, LPAs, and other financial documents. The solution analyzed data to identify key topics and ensured the findings were easily comprehensible to detect anomalies and potential risks through interactive visual representations such as charts, tables, and infographics.
Value Delivered
  • Reduced manual efforts as a result of automating the data mining and document reviewing processes, enabling auditors to focus on other high-value tasks for improved business profitability.
  • Assured high-quality recommendations to auditors for informed and consistent decision-making around important disclosures, regulatory compliance information, and other pertinent details required for audits, leading to audit excellence and improved market reputation.
  • Enhanced privacy through the PII Data anonymized via an automated anonymization pipeline.
  • Easy extensibility to support new use cases and drive innovation across the enterprise for organizational growth.
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