AI-Driven Mortgage Prepayment Shock Forecasting System using LangChain & Claude 4 Opus
Mortgage bond investors suffer unexpected yield decay when AI loan-refinancing applications cause spikes in pool prepayment rates. Construct a time-series evaluation agent using LangChain and Claude 4 Opus to project prepayment speeds (CPR) under changing yield curves. Achieve mean absolute error under 1.5% CPR across synthetic MBS pools.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a LangChain financial forecasting chain leveraging Claude 4 Opus to predict residential mortgage bond prepayment shocks under dynamic interest rate shifts.
How work is evaluated
Measures prediction precision of calculated CPR rates under specific interest rate drop scenarios.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
prepayment_flag_match
Checks correct identification of prepayment shock status
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
cpr_mae
Mean Absolute Error of predicted CPR rate against baseline market standard • target: 0.015 • range: 0-0.05
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct custom LangChain `Runnable` chains to orchestrate interest rate scenario prompts and financial forecasting tools
Leverage Anthropic Claude 4 Opus (`ChatAnthropic`) for reasoning over complex macroeconomic signals and borrower AI-refi behavior
Calculate Conditional Prepayment Rate (CPR) and Single Monthly Mortality (SMM) metrics dynamically
Evaluate scenario sensitivity under parallel yield curve shifts (+/- 100 bps)
Reference links and supporting material
Mortgage backed security pool characteristics including coupon rates, vintage years, and historical prepayment speeds.
Challenge setup
Measures prediction precision of calculated CPR rates under specific interest rate drop scenarios.
Challenge input
JSON containing wac, weighted_average_maturity, current_mortgage_rate, rate_drop_bps
LangChain
Framework for constructing structured prompt-chaining pipelines.
Claude 4 Opus
Advanced frontier LLM for complex macro-financial reasoning.
Evaluated output
JSON containing predicted_cpr, smm, prepayment_shock_flag
- Checks correct identification of prepayment shock status
- Mean Absolute Error of predicted CPR rate against baseline market standard • target: 0.015 • range: 0-0.05
- Benchmark: MBS Prepayment Analytics Benchmark
- Cpr Mae target: 0.015
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- LangChain · Required
- Langchain · Optional
- Claude 4 Opus · Optional
- LangChain · Required
- Langchain · Optional
Evaluation contract
- prepayment_flag_match · Weight 1
- cpr_mae · Weight 1
Metrics
- cpr_mae · Target 0.015
Benchmarks
- MBS Prepayment Analytics Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing wac, weighted_average_maturity, current_mortgage_rate, rate_drop_bps
Output: JSON containing predicted_cpr, smm, prepayment_shock_flag
Execution harness: Python
Evaluation tools
- LangChain · agent_framework
- Claude 4 Opus · llm_model
Recipe freeze marker
No recipe freeze time is recorded.