Abstract
Commercial credit underwriting is often a complex and labour-intensive process, hindered by fragmented data collection, extensive information analysis, and manual procedures that that slow turnaround times and create inconsistencies in decision-making.
Although financial institutions have extensively implemented workflow automation to streamline the origination process, the adoption of AI offers a substantial opportunity to further enhance efficiencies—especially in the underwriting process—productivity, accuracy, and consistency
This whitepaper uncovers how generative AI (GenAI), AI agents, and Retrieval-Augmented Generation (RAG) can create an intelligent, automated workflow that accelerates decision-making and strengthens risk assessment.
Key Insights
Commercial credit underwriting is fundamentally inefficient due to its reliance on manual processes. Underwriters spend excessive time gathering diverse documents from disparate systems, manually extracting data for spreadsheet analysis, and sifting through third-party reports. This fragmentation leads to significant delays, inconsistencies in risk assessment, and high operational costs.
The proposed solution embeds AI directly into the loan origination system to automate the entire process. AI agents handle autonomous tasks such as verifying document completeness and authenticating files, while GenAI and a Retrieval-Augmented Generation (RAG) knowledge base serve as a central brain to extract, understand, and synthesize all applicant information.
AI fundamentally changes the nature of the underwriter's job by automating the most time-consuming tasks. The system automatically extracts financial data for ratio calculations, utilizes GenAI to produce concise summaries of qualitative information (such as business models and news coverage), and enables human oversight of exceptions, thereby freeing up underwriters to focus on high-value decision-making.
A conversational AI assistant provides a powerful new way to interact with data. Underwriters can ask complex questions in natural language (e.g., “What are the company’s ESG ratings compared to its peers?”) and receive instant, structured answers drawn from all available documents. This capability drastically reduces research time and equips decision-makers with deeper, more accessible insights.