1. Automated sales practice identification for increased compliance efficiency and regulatory coverage
A bank received more than 500,000 customer complaints annually. Its existing process for identifying sales practice issues relied heavily on key-word filtering in customer call transcripts with a manually crafted lexicon — and then a team of hundreds of employees performed manual monitoring, review and validation. How could the bank apply NLP and ML for more effectiveness and efficiency?
The EY approach
Recorded customer calls were converted into transcripts. Extensive pre-processing of the transcribed calls decoded misspellings, acronyms and jargon in the text. An Ernst & Young LLP (EY) team layered multiple NLP techniques — including semantic pattern matching and sentiment analysis — to identify and extract customer signals. Those signals informed the development of linear and nonlinear ML models to predict the risk of sales practice issues.
By using the EY approach, the bank achieved a more than 90% capture rate of sales practice issues, reducing the risk of regulatory matters requiring (immediate) attention. False positives declined by more than 30%, and manual reviews dropped 80%, so this application of NLP also drove increased operational efficiency.
2. NLP/ML assisted workflow for marketing material review
A financial institution needed to review its marketing materials to determine whether they were in breach of fiduciary limitations set by a recent Department of Labor (DoL) ruling. The client’s existing setup relied completely on manual review by the compliance team. How could the process be automated, considering that the marketing materials were in multiple formats and document types and included a broad range of terms?
The EY approach
The EY team developed a proprietary tool to parse and review the materials by deploying ML and NLP algorithms, which were packaged into a virtual assistant to aid in the overall review. EY also developed a module that extracted information based on the document layouts. The tool was designed to fit naturally into the institution’s existing workflow. The approach identified the presence of disclosures and glossary terms, flagged promissory and misleading language, and calculated the degree to which documents adhered to DoL requirements laid out in templates
The EY approach reduced the review time per document by 95%, and it did more than just minimize the resource requirements. It also accelerated the process of reviewing outliers and consistently captured standard issues with improved consistency across document types, which relieved regulatory pressure and reduced time to market.