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    The Credit Sentiment Score solution compiles adverse credit signals from news articles, backed by extensive research and state-of-the-art natural language processing, text analytics, and machine learning techniques. This score helps firms assess credit in the loan origination and portfolio risk monitoring process and track unfavorable media. The higher the score, the stronger the credit distress signal.

    Use alternative data to improve the credit decisioning process

    • Identify credit-relevant news: Adverse scores capture a host of negative news including default, bankruptcy, covenant default, debt restructuring, rating downgrades, lawsuits, downsizing, and fraud events. Additionally, they acquire signals around financial loss, liquidity concerns, industry- and sector-wide strains, trade tensions, weak demand, competitive implications, and even criminal investigations. If the credit sentiment score is high, you will likely find the content to be relevant.
    • Improve research efficiency: Credit analysts can significantly reduce time spent on company research and monitoring activities. Our experience using credit sentiment scores shows you can filter out up to 99% of articles from your initial search, reducing the time to find the specific content you want.
    • Create early warning indicators: Credit sentiment scores rise before major credit events. Our research indicates that, on average, this happens between six to eight months before such events.
    • Increase transparency: Analysts can easily review news content used as input to understand how scores were derived and form their own judgment.
    • Develop a well-rounded view: Using these scores in conjunction with other quantitative metrics, such as expected default frequency (EDFs), helps provide context and identifies why changes occur.

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