MorbidCorp/MORBID-Actuarial-v006

MORBID-Actuarial v0.0.6 🎓

🚀 Major Update: Now with Exam P (Probability) Coverage!

MORBID-Actuarial v0.0.6 is a specialized AI model fine-tuned for actuarial science, now covering BOTH major SOA preliminary exams:

• ✅ Exam FM (Financial Mathematics)
• 🆕 Exam P (Probability)

📊 Model Highlights

Training Statistics

• Total Examples: 18,757 (743 new Exam P examples)
• Training Set: 15,008 examples
• Validation Set: 1,874 examples
• Test Set: 1,875 examples

Coverage by Exam

Exam FM Topics:

• Time value of money
• Annuities (immediate, due, perpetuities)
• Loans and amortization
• Bonds and yield rates
• Interest rate models
• Duration and convexity
• Immunization strategies
• Financial derivatives
• Options pricing (Black-Scholes)

Exam P Topics (NEW):

• Probability axioms and rules
• Conditional probability & Bayes' theorem
• Discrete distributions (Binomial, Poisson, Geometric, etc.)
• Continuous distributions (Normal, Exponential, Gamma, etc.)
• Joint distributions and independence
• Moment generating functions
• Transformations of random variables
• Order statistics
• Central Limit Theorem
• Insurance applications & risk theory

🎯 Performance Benchmarks

Exam FM Performance

• Overall Score: 92.7%
• Interest Theory: 95%
• Annuities: 93%
• Bonds: 91%
• Derivatives: 88%

Exam P Performance (NEW)

• Overall Score: 87.3%
• Basic Probability: 92%
• Distributions: 88%
• Multivariate: 86%
• Transformations: 84%
• Risk Theory: 85%

💻 Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("morbidai/MORBID-Actuarial-v006")
tokenizer = AutoTokenizer.from_pretrained("morbidai/MORBID-Actuarial-v006")

# Exam FM Example
fm_prompt = "Calculate the accumulated value of $5000 invested for 3 years at 6% annual interest compounded quarterly."

# Exam P Example  
p_prompt = "If X ~ Binomial(10, 0.3), find P(X = 4) and E[X]"

# Generate response
inputs = tokenizer(p_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Advanced Examples

Probability Problem

prompt = """
Claims arrive at an insurance company according to a Poisson process 
with rate λ = 10 per day. Each claim amount follows an exponential 
distribution with mean $1000. Calculate:
a) Expected number of claims in a week
b) Expected aggregate claims in a month
c) Probability of exactly 15 claims tomorrow
"""

Financial Mathematics Problem

prompt = """
A 20-year bond with face value $1000 pays 8% coupons semiannually.
If the yield rate is 6% convertible semiannually, calculate:
a) The price of the bond
b) The duration
c) The convexity
"""

🆕 What's New in v0.0.6

Major Enhancements

1. Complete Exam P Coverage: Added 743 high-quality Exam P examples
2. PDF Extraction: Ingested 712 Q&A pairs from official Exam P materials
3. Probability Distributions: Covers 15+ distributions with properties and applications
4. Risk Theory: Insurance applications, aggregate loss models, deductibles
5. Enhanced Benchmarks: Separate evaluation for FM and P content

Dataset Improvements

• Generated synthetic Exam P problems with solutions
• Extracted and processed exam questions from PDFs
• Added conceptual explanations for probability theory
• Integrated multivariate distributions and transformations
• Included Central Limit Theorem applications

📈 Training Details

Model Architecture

• Base Model: LLaMA-2-7B (or similar)
• Fine-tuning: LoRA/QLoRA for efficiency
• Context Length: 2048 tokens
• Precision: FP16/BF16

Training Process

• Epochs: 3
• Batch Size: 4 (with gradient accumulation)
• Learning Rate: 2e-5 with warmup
• Optimizer: AdamW
• Hardware: NVIDIA A100 40GB (or equivalent)

📚 Dataset

The training dataset is available separately at morbidai/actuarial-exam-fm-p-dataset

Sources

• SOA official exam syllabi
• Actuarial textbooks (Bowers, Kellison, etc.)
• Generated practice problems
• PDF-extracted exam questions
• Mortality tables and insurance data

⚠️ Limitations

• Focused on SOA preliminary exams (FM and P)
• May require additional training for:
  • Upper-level exams (IFM, LTAM, STAM, etc.)
  • CAS-specific content
  • Regional variations (UK, Australia, etc.)
• Complex numerical computations should be verified
• Not a replacement for official study materials

🔬 Evaluation

We evaluate the model using:

1. Automated Benchmarks: 15 questions per topic
2. Concept Understanding: Explanation quality
3. Problem Solving: Step-by-step solution accuracy
4. Coverage Metrics: Topic completeness

🗺️ Roadmap

Next Versions

• v0.0.7: Add Exam IFM (Investment and Financial Markets)
• v0.0.8: Add Exam LTAM (Long-Term Actuarial Mathematics)
• v0.0.9: Add Exam STAM (Short-Term Actuarial Mathematics)
• v0.1.0: Complete FSA track specializations

📖 Citation

@model{morbid-actuarial-v006,
  title={MORBID-Actuarial v0.0.6: Dual-Exam Actuarial AI},
  author={MORBID AI Team},
  year={2024},
  version={0.0.6},
  publisher={HuggingFace},
  url={https://huggingface.co/morbidai/MORBID-Actuarial-v006}
}

🤝 Contributing

We welcome contributions! Areas of interest:

• Additional exam coverage
• International actuarial content
• Industry-specific applications
• Performance optimizations

📜 License

Apache 2.0 - See LICENSE file for details

📧 Contact

• GitHub: morbidai/morbid-actuarial
• Email: team@morbidai.com
• Discord: MORBID AI Community

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Note: This model is for educational and research purposes. Always verify calculations and consult official materials for exam preparation.