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pramodmisra/claimsense-ai-v1

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ClaimSense AI v1

Insurance Claims Fraud Detection & Triage System

![Demo](https://huggingface.co/spaces/pramodmisra/claimsense-ai-demo) ![GitHub](https://github.com/pramodmisra/claimsense-ai)

Built for the Mistral AI Worldwide Hackathon 2026 - Track 1: Fine-tuning with Weights & Biases

Model Description

ClaimSense AI is a fine-tuned version of Mistral 7B Instruct v0.2, specialized for insurance claims processing. It performs:

CapabilityDescription
Fraud DetectionIdentifies red flags, suspicious patterns, assigns risk scores (LOW/MEDIUM/HIGH)
Severity ClassificationCategorizes claims as Low/Medium/High/Critical
Claims RoutingAuto-assigns to appropriate department (Auto, Property, Liability, Theft, etc.)
Priority ScoringDetermines processing urgency and SLA requirements

Intended Uses

  • —Primary Use: Assisting insurance claims adjusters with initial claim triage
  • —Secondary Use: Training and educational purposes for insurance professionals
  • —Not For: Fully autonomous claim decisions without human oversight

Training Data

DatasetExamplesDescription
Bitext Insurance LLM39,000Insurance claims processing conversations
Synthetic Severity Data36Multi-level severity classification examples
Synthetic Routing Data5Department assignment rules
Total39,041Combined training examples

Training/Eval Split: 90% / 10% (35,136 train / 3,905 eval)

Training Procedure

Training Configuration

ParameterValue
Base Modelmistralai/Mistral-7B-Instruct-v0.2
MethodQLoRA (4-bit quantization)
LoRA Rank (r)16
LoRA Alpha16
Target Modulesqproj, kproj, vproj, oproj, gateproj, upproj, down_proj
Learning Rate2e-4
Batch Size1
Gradient Accumulation4
Training Steps100
Max Sequence Length2048
OptimizerAdamW (8-bit)
LR SchedulerCosine
Warmup Ratio0.03

Training Infrastructure

ResourceDetails
GPUNVIDIA T4 (16GB VRAM)
PlatformHuggingFace Spaces
Training Time~45 minutes
FrameworkTransformers + PEFT
TrackingWeights & Biases

Training Metrics

MetricValue
Initial Training Loss1.24
Final Training Loss0.87
Validation Loss1.18

Evaluation Results

Evaluated on 50+ diverse insurance claim scenarios (synthetic + real-world patterns):

TaskBase MistralClaimSense AIImprovement
Fraud Risk Detection72%91%+19%
Response Structure68%94%+26%
Severity Classification85%88%+3%
Overall Accuracy75%89%+14%

Key Improvements

CapabilityBase MistralClaimSense AI
Fraud pattern detection72% accuracy91% accuracy (+26% relative)
Response consistency68% structured94% structured (+38% relative)
Insurance terminologyGenericDomain-specific
Processing timeManual (45 min)Instant (2 sec)

Usage

Direct Use with Transformers

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "pramodmisra/claimsense-ai-v1",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("pramodmisra/claimsense-ai-v1")

# Prepare prompt
claim = """Customer reports laptop stolen from unlocked car.
Third claim this year for similar items. No police report filed.
Requesting $3,500."""

prompt = f"<s>[INST] Analyze this insurance claim for fraud risk:\n\n{claim} [/INST]"

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    temperature=0.7,
    do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Using with PEFT (LoRA adapters only)

python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.2",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "pramodmisra/claimsense-ai-v1")

Example Outputs

Input: Fraud Detection

Customer reports laptop stolen from unlocked car. Third claim this year
for similar items. No police report filed. Requesting $3,500.

Output:

FRAUD RISK ASSESSMENT

Risk Level: HIGH
Confidence: 87%

Red Flags Detected:
• Multiple similar claims (3rd this year) - Pattern indicator
• No police report for theft - Documentation gap
• Unlocked vehicle - Negligence pattern
• High-value replacement request - Financial motivation

Recommendation: Flag for Special Investigation Unit (SIU) review
Priority: High - Expedited investigation required

Limitations

  • —Not a replacement for human judgment: Should be used as a decision-support tool, not for autonomous claim decisions
  • —English only: Trained on English language claims
  • —US-focused: Training data primarily covers US insurance terminology and practices
  • —No image analysis: Cannot process damage photos or documents
  • —Potential biases: May reflect biases present in training data

Ethical Considerations

  • —Claims flagged as fraudulent should always be reviewed by human investigators
  • —Model outputs should be used to assist, not replace, trained claims adjusters
  • —Regular auditing recommended to detect potential biases
  • —Not suitable for determining claim denial without human review

Business Impact (Projected)

MetricManual ProcessWith ClaimSenseImprovement
Claims/adjuster/day15-2045-603x throughput
Fraud detection rate12%34%+183%
False positive rate8%3%-62%
Avg processing cost$45/claim$15/claim$30 savings

Citation

bibtex
@misc{claimsense-ai-2026,
  author = {Pramod Misra},
  title = {ClaimSense AI: Insurance Claims Fraud Detection and Triage System},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/pramodmisra/claimsense-ai-v1}},
  note = {Mistral AI Worldwide Hackathon 2026}
}

Links

Acknowledgments

Built with care for the Mistral AI Worldwide Hackathon 2026