Vendor PulseGen: Generative Vendor Risk Management Platform

Swapnil Yasasvi, Nilanjan Sinhababu, Suman Kumar Chakraborty, Shuvo Bardhan

Abstract: In today’s world, vendor risk management (VRM) requires field expertise and assessment capabilities. A general concern in VRM is ‘what to ask?’ - usually addressed in the literature using transformer-based models. We use zero-shot approaches as these don’t have specialized VRM understanding; likewise, uploading sensitive vendor information to fine-tune LLM is not possible. To solve, we utilize the non-parametric learning abilities of LLMs to generate context-based tailored questionnaires depending on the vendor metadata (i.e., publicly available). We also assign an quantitative risk score to the different risk dimensions that our model is capable of assessing which includes compliance based risks, external surface based risks; and overall risk posture (i.e., assessed by the questions asked). By integrating the structured scoring mechanism such as Question Risk Score (QRS), Compliance Risk Score (CRS), and External Surface Risk (ESR), this platform offers an efficient approach to assess the vendor risk in a quantified manner.

External IDs:dblp:conf/compsac/YasasviSCB25