Forbes Article Rating

Student Loans Set For Big Credit Reporting And Collections Changes As Soon As This Week

Dec 30, 2024 View Original Article
  • Bias Rating

    -8% Center

  • Reliability

    50% ReliableFair

  • Policy Leaning

    2% Center

  • Politician Portrayal

    -11% Negative

Bias Score Analysis

The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.

Sentiments

Overall Sentiment

24% Positive

  •   Liberal
  •   Conservative
SentenceSentimentBias
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Bias Meter

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Bias Meter

Contributing sentiments towards policy:

60% : Administration officials now anticipate collections efforts resuming against defaulted student loan borrowers in early 2025 -- potentially as soon as January.
56% : This can allow borrowers to pursue programs like income-driven repayment, which can provide affordable payments and eventual student loan forgiveness; Public Service Loan Forgiveness, Teacher Loan Forgiveness, and other profession-based student loan forgiveness programs; and start the process of repairing their credit.
53% : Administrative collections include wage garnishment, which can siphon off 15% or more of your paycheck, and Treasury Offset, which allows the government to intercept federal tax refunds and other federal income streams and offset federal benefits such as a portion of Social Security.
44% : This typically occurs when administrative collections efforts are limited or not fruitful, and officials believe the borrower may have additional income or assets that go beyond what the government can collect through administrative wage garnishment or Treasury Offset.
33% : But President Trump, and subsequently President Biden, issued multiple short-term extension.

*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.

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