Generative AI and large language models (LLMs) integrated into our iRAA platform can significantly enhance the auditing functions of an Organization

Generative AI and large language models (LLMs) integrated into our iRAA platform (Integrated Robotics Automation & Auditing) can significantly enhance the auditing functions of both internal auditing teams and external auditors. By automating and enhancing various processes, these technologies offer numerous benefits.

Here are some potential use cases that are being built into the iRAA platform to help businesses unlock the full potential of Robotics and Automation, along with Generative AI & LLMs:

???Anomaly Detection: The Generative AI models we are building have been extensively trained on vast volumes of financial transactional data, enabling them to identify patterns and anomalies that may indicate fraud, errors, or compliance violations. Auditors can readily leverage these models to detect and investigate unusual transactions or patterns.

???Risk Assessment: The LLMs, which we are now integrating with iRAA, are actively assisting auditors in assessing risks by analyzing a company's financial statements, disclosures, and industry-specific data. These models, backed by ongoing development, provide auditors with valuable insights into potential risks and highlight areas that require immediate attention.

???Compliance Monitoring: Our generative AI capabilities is enabling auditors to actively monitor compliance with regulatory requirements and internal policies. By analyzing large datasets, including legal and regulatory documents, our models proactively identify non-compliant practices and potential compliance gaps.

???Automated Report Generation: Our LLMs, which are being integrated into iRAA, have been extensively developed to automate the process of generating audit reports. By inputting audit findings, key data, and analysis, the models produce comprehensive and accurate reports that effectively summarize the audit results, including identified issues, recommendations, and supporting evidence.

???Document Analysis: Through ongoing investments in generative AI models, we are ensuring auditors can efficiently analyze and categorize large volumes of financial documents, such as invoices, receipts, contracts, and bank statements. By automating the extraction of relevant information and identification of discrepancies, auditors can focus their efforts on areas that necessitate further investigation.

???Fraud Detection: We are building LLMs that can be actively deployed to help auditors identify patterns and indicators of fraud by analyzing financial data, transaction records, and employee behavior. By proactively flagging suspicious activities, these models enable auditors to effectively concentrate on high-risk areas and potentially fraudulent transactions.

???Continuous Monitoring: Our generative AI models, backed by ongoing development efforts, provide auditors with continuous monitoring capabilities. By analyzing real-time financial data, these models proactively alert auditors to potential issues or anomalies as they occur, empowering auditors to intervene promptly and mitigate risks in real-time.

???Predictive Analytics: Through ongoing advancements, our LLMs leverage historical financial data and industry trends to generate accurate predictive analytics for auditors. These models assist in forecasting financial performance, identifying potential risks, and providing crucial insights to support decision-making processes during audits.

???Generation of Supporting Documents: Our generative AI capabilities, being developed with a focus on industry best practices and regulatory guidelines, allow auditors to efficiently generate supporting documents such as checklists for industry-wise auditing processes, best practices, and customized submissions. This streamlines the documentation process and ensures comprehensive and accurate deliverables.

It is important to note that while generative AI and LLMs offer valuable capabilities, human oversight and judgment remain crucial in the auditing process. These technologies augment auditors' work and should be used in conjunction with human expertise to ensure accurate interpretations and appropriate actions. Additionally, it is essential to acknowledge that while LLM models offer significant improvements, 100% accuracy cannot be guaranteed. Manual intervention and human involvement are still necessary, as LLMs typically achieve accuracy ranging from 70% to 80%.

玛泽咨询 , Forvis Mazars, Canada , Mazars in the UK , Mazars in India , Forvis Mazars in Germany, Karriere , Mazars in Thailand , BDO in India , 立信 , BDO , Grant Thornton Bharat LLP , Grant Thornton (US) , Grant Thornton UK LLP , Crowe , Crowe UK , Crowe Indonesia , RSM US LLP RSM UK nasscom INDIAai Aptus Data Labs Abhishek Das Srinidhi Acharya Ravindra S. V Adiseshu Naik Srinivasa Raghavan Parvathinathan Veerabahu Pillai Sourav Dutta #genai #artificialintelligence #llm #llmops #machinelearning #audit #auditors #internalaudit #externalaudit #riskmanagement #auditing

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