A Semantic Search Framework for Similar Audit Issue Recommendation in Financial Industry

Abstract

Audit issues summarize the findings during audit reviews and provide valuable insights of risks and control gaps in a financial institute. Despite the wide use of data analytics and NLP in financial services, due to the diverse coverage and lack of annotations, there are very few use cases that analyze audit issue writing and derive insights from it. In this paper, we propose a deep learning based semantic search framework to search, rank and recommend similar past issues based on new findings. We adopt a two-step approach. First, a TF-IDF based search algorithm and a Bi-Encoder are used to shortlist a set of issue candidates based on the input query. Then a Cross-Encoder will re-rank the candidates and provide the final recommendation. We will also demonstrate how the models are deployed and integrated with the existing workbench to benefit auditors in their daily work.