In recent years, Artificial Intelligence (AI) has transformed industries with automation, data-driven insights, and enhanced operational efficiency. Auditing, traditionally a meticulous and manual process, is no exception. AI tools, including Machine Learning (ML) models and Large Language Models (LLMs), are enabling auditors and developers to tackle complex challenges in innovative ways. Let's delve into how these technologies are shaping audit functions and what it means for developers working in this field.

The advent of Large Language Models (LLMs) has elevated AI's role in auditing. LLMs, with their ability to understand and generate human-like language, offer new ways to handle unstructured data, create advanced insights, and automate complex audit tasks.

LLMs excel at reading and interpreting vast quantities of text. In auditing, this capability is used to parse complex legal and compliance documents, extract key entities, and tag relevant information, making audits faster and more accurate.

Auditors frequently deal with lengthy reports and financial statements. LLMs can generate summaries, highlighting significant insights, trends, or red flags, saving valuable time.

LLMs can perform sentiment analysis and provide context around datasets, enabling auditors to understand not just the 'what' but also the 'why.' For instance, analyzing client feedback or employee communications can offer insights into organizational culture or potential compliance issues.

"The power of AI lies in its ability to augment human intelligence, not replace it. Tools like LLMs ensure we focus on what truly matters." � Sundar Pichai, CEO of Alphabet Inc.

In my experience developing vKYC, one of the most cutting-edge applications of AI and LLMs in auditing is vKYC (Video Know Your Customer) a technology that automates customer verification by processing video and audio data. This innovation enables real-time identity verification, ensuring that customers are who they claim to be through multiple modalities. The vKYC has been developed for our product, "eTHIC vKYC"

Person Detection and Counting:
Using computer vision models like YOLO or OpenCV-based frameworks, vKYC systems can identify and count the number of people in the video. This ensures that only the verified individual is present during the KYC process, enhancing compliance with identity verification standards.

Speech-to-Text Conversion and Verification:
Audio from the video is converted to text using Automatic Speech Recognition (ASR) models. LLMs then analyze the transcribed text to verify responses against expected answers, flag unusual language patterns, and ensure compliance with predefined scripts.

Prompt Verification Using LLMs:
vKYC systems can prompt customers with questions or instructions during the video call. LLMs analyze the responses for authenticity and contextual accuracy, checking for cues in both content and tone.

Integration with Existing Audit Systems:
Developers must integrate multimedia features with existing audit platforms, necessitating robust APIs and real-time processing capabilities.

Interpreting Model Results:
AI models, especially LLMs, often function as "black boxes" Developers must ensure interpretable and explainable AI results, allowing auditors to understand how conclusions were reached.

Scaling and Performance:
AI-driven auditing tools must handle substantial data volumes in real-time. Developers face challenges like optimizing performance using hardware acceleration (e.g., CUDA for deep learning) and managing large datasets effectively.

Maintaining and Updating Models:
ML models require continuous retraining to remain accurate. Developers must establish retraining pipelines to keep models current without causing significant downtime, vital for continuously monitoring systems.

"AI is complex to develop, but even harder to operationalize. Scaling AI systems requires not only technical innovation but also constant iteration and adaptation to the real world"
� Jensen Huang, CEO of NVIDIA

The convergence of AI and auditing isn't just a trend-it's redefining the profession. As LLMs and other AI technologies evolve, they will introduce more intuitive and autonomous tools, enabling auditors to focus on high-value, strategic tasks. Technologies like vKYC, which leverage video, audio, and language processing, will become essential for customer-facing audits, ensuring secure, compliant, and efficient identity verification processes.

Developers will play a pivotal role in this transformation, not only building and maintaining these tools but also ensuring they meet the ethical, technical, and regulatory demands of the industry. The integration of AI and LLMs in audit holds the potential to create a transparent, efficient, and intelligent audit environment-one where auditors and AI tools work seamlessly together to provide insights with unprecedented depth and accuracy.