Harneet Kaur, founder of Axim Intelligence and a former distressed-credit investor, examines financial reasoning in this 14-minute AI Engineer talk.

A troubled borrower has several possible futures

  • Distressed credit means debt from a company struggling to meet its obligations. Bankruptcy is one possible outcome, alongside negotiated changes to its debt.
  • Analysis combines available cash, the order in which creditors get paid, legal documents and the business’s prospects.
  • Kaur argues that a binary default prediction misses important paths through a restructuring, where lenders and borrowers change the debt’s terms.

Two models disagree on the same company

  • Kaur uses iHeartMedia as a live case, giving two frontier models the same organized context.
  • One favors negotiated restructuring; the other initially favors bankruptcy. The future outcome is still unknown at the time of the talk.
  • Her own preference for one forecast cannot establish which model is right.

Check four layers of reasoning

  1. Extraction: verify the facts taken from financial statements and credit documents.
  2. Derivation: check calculations, such as leverage from debt and a measure of operating earnings.
  3. Judgment: assess what those calculations imply about the company’s financial stress.
  4. Prediction: estimate probabilities for the paths the company could take.
  • Errors in an early layer can contaminate later judgment. Evaluate those steps before comparing final forecasts.
  • Kaur says extracting public-company facts is relatively mature; private-loan spreadsheets and PDFs remain more difficult.

Convergence is a result, not a verdict

  • After Kaur introduces the checked reasoning process, both models favor negotiated restructuring and give similar bankruptcy probabilities.
  • That shows agreement in this example. It leaves forecast accuracy, calibration and investment value unproven.
  • Testing historical cases also requires care: a model may already know what happened, creating look-ahead bias.
  • Kaur suggests consistent probabilistic assessments could help explain market pricing. The talk does not test a trading strategy.

Caveat: this is one live company case, with no resolved outcome or broad predictive benchmark. Shared assumptions can make two models agree and still be wrong.

“But is that the correct path? We don’t know.” — Harneet Kaur, 13:00