FINANCE-09 · Advanced · ENGLISH · PLANNED
Trace cash flows, calculate a transparent model, stress-test assumptions and explain risk.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Finance | Banking, Credit, Insurance and Financial Risk
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Sign in to save this learning pathPractical outcome: Write a credit memo for a fictional borrower using supplied records.
Explain borrower capacity using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Explain collateral using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Explain covenants using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Explain default probability using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Use the first concepts from this section. Write a credit memo for a fictional borrower using supplied records. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain loss given default using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Explain exposure at default using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Explain credit scoring using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Explain credit monitoring using a visual and one worked real-world example. Context: Write a credit memo for a fictional borrower using supplied records. End with a prediction or check-for-understanding.
Write a credit memo for a fictional borrower using supplied records. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Borrower capacity, Collateral, Covenants and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare hypothetical balance-sheet risks and controls.
Explain asset-liability management using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Explain liquidity risk using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Explain interest-rate risk using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Explain capital adequacy concepts using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare hypothetical balance-sheet risks and controls. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain underwriting using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Explain risk pooling using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Explain claims reserves using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Explain reinsurance using a visual and one worked real-world example. Context: Compare hypothetical balance-sheet risks and controls. End with a prediction or check-for-understanding.
Compare hypothetical balance-sheet risks and controls. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Asset-liability management, Liquidity risk, Interest-rate risk and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Build a risk dashboard from synthetic loss scenarios.
Explain market risk using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Explain value at risk using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Explain expected shortfall using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Explain stress testing using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Use the first concepts from this section. Build a risk dashboard from synthetic loss scenarios. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain operational risk using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Explain model risk using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Explain risk appetite using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Explain independent risk oversight using a visual and one worked real-world example. Context: Build a risk dashboard from synthetic loss scenarios. End with a prediction or check-for-understanding.
Build a risk dashboard from synthetic loss scenarios. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Market risk, Value at risk, Expected shortfall and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Build a risk dashboard from synthetic loss scenarios. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Banking, Credit, Insurance and Financial Risk, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Fictional USD budgets, statement analysis, valuation models and risk scenarios. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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