Comparing Firm Performance across Expert-led and AI-Based Technology Valuation Methods

This study empirically investigates whether AI-based technology valuation produces different outcomes compared to conventional expert-led evaluation methods. The Korea Technology Finance Corporation (KOTEC), a public institution that supports SME financing through credit guarantees, has implemented an AI-driven valuation system known as KPAS to assess firms’ intellectual property. To evaluate the effectiveness of this AI-based approach relative to traditional expert appraisals, we examine changes in two key performance indicators: firm sales, which reflect current business performance, and the Tech Index, which serves as a proxy for innovation capacity. Using a difference-in-differences (DID) framework combined with propensity score matching (PSM), we compare the post-guarantee performance of firms evaluated by each method. The results show that guarantees based on technology valuation improve firm performance overall, and that KPAS substantially reduces the time and cost of evaluation compared to expert appraisal. Despite its lower resource requirements, the AI-based method delivers performance outcomes that are comparable to, or even better than, those of traditional evaluations. These findings remain largely robust across alternative model specifications and offer empirical support for the broader adoption of AI-assisted valuation systems in technology finance.