Automating Workflows like Underwriting
Investment and Trading
Machine learning can leverage fuzzy matching to encode baseline underwriting logic in addition to an evolving algorithm that can optimize the engine’s performance over time. Additionally, natural language processing (NLP) can limit the amount of material that requires analyst review, streamlining all but the trickiest applications.
Benefits for the company
Companies can take advantage of reducing the waiting time and freeing agents to automate the underwriting process with precision. Unlike traditional methods of human underwriting each and every case with predefined rules, with AI the process can be faster and error free.
Feasability
High
Type of expertise/ AI domain
Fuzzy Matching, Natural Language Process
Internal data required
Historical underwritten cases