Build a Credit Card Default Risk & Early Warning Agent with LlamaIndex RAG
Macroeconomic pressures have driven credit card defaults to multi-year highs. Build a advanced RAG compliance agent using LlamaIndex to query complex borrower credit histories, macroeconomic indices, and loan documentation to forecast default probabilities with an AUC-ROC > 0.88.
What you are building
Construct an enterprise credit risk modeling and early warning system using LlamaIndex RAG data connectors and advanced query engines.
How work is evaluated
Evaluates default prediction AUC-ROC and retrieval context relevance for early warning triggers.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
auc_roc_pass
Default prediction AUC-ROC must exceed 0.88 baseline.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
auc_roc
Area under ROC curve for credit default identification. • target: 0.91 • range: 0.8-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement advanced RAG indexing over credit agreements using LlamaIndex
Construct hybrid vector-keyword query engines with reranking layers
Predict credit card default probability based on structured balance and macro data
Generate early warning debt restructuring recommendations for high-risk accounts
Resources
Dataset of borrower credit profiles, historical repayment patterns, and unstructured debt counseling transcripts.
Challenge setup
Evaluates default prediction AUC-ROC and retrieval context relevance for early warning triggers.
Challenge input
JSON borrower profile detailing debt ratio, payment delay days, and income
LlamaIndex
Provides state-of-the-art query engines and data connectors for complex indexing.
Evaluated output
JSON default prediction probability and primary risk factor summary
- Default prediction AUC-ROC must exceed 0.88 baseline.
- Area under ROC curve for credit default identification. • target: 0.91 • range: 0.8-1
- Benchmark: Credit Risk ML Suite
- Auc Roc target: 0.91
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Llama Index · Required
- LlamaIndex · Optional
- Llama 3.2 · Optional
Evaluation contract
- auc_roc_pass · Weight 1
- auc_roc · Weight 1
Metrics
- auc_roc · Target 0.91
Benchmarks
- Credit Risk ML Suite
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON borrower profile detailing debt ratio, payment delay days, and income
Output: JSON default prediction probability and primary risk factor summary
Execution harness: Python
Evaluation tools
- LlamaIndex · RAG Framework
Recipe freeze marker
No recipe freeze time is recorded.