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ClaimSense AI v1

Name: claimsense-ai-v1
Availability: InStock

Insurance Claims Fraud Detection & Triage System

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:

Capability	Description
Fraud Detection	Identifies red flags, suspicious patterns, assigns risk scores (LOW/MEDIUM/HIGH)
Severity Classification	Categorizes claims as Low/Medium/High/Critical
Claims Routing	Auto-assigns to appropriate department (Auto, Property, Liability, Theft, etc.)
Priority Scoring	Determines 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

Dataset	Examples	Description
Bitext Insurance LLM	39,000	Insurance claims processing conversations
Synthetic Severity Data	36	Multi-level severity classification examples
Synthetic Routing Data	5	Department assignment rules
Total	39,041	Combined training examples

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

Training Procedure

Training Configuration

Parameter	Value
Base Model	`mistralai/Mistral-7B-Instruct-v0.2`
Method	QLoRA (4-bit quantization)
LoRA Rank (r)	16
LoRA Alpha	16
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Learning Rate	2e-4
Batch Size	1
Gradient Accumulation	4
Training Steps	100
Max Sequence Length	2048
Optimizer	AdamW (8-bit)
LR Scheduler	Cosine
Warmup Ratio	0.03

Training Infrastructure

Resource	Details
GPU	NVIDIA T4 (16GB VRAM)
Platform	HuggingFace Spaces
Training Time	~45 minutes
Framework	Transformers + PEFT
Tracking	Weights & Biases

Training Metrics

Metric	Value
Initial Training Loss	1.24
Final Training Loss	0.87
Validation Loss	1.18

Evaluation Results

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

Task	Base Mistral	ClaimSense AI	Improvement
Fraud Risk Detection	72%	91%	+19%
Response Structure	68%	94%	+26%
Severity Classification	85%	88%	+3%
Overall Accuracy	75%	89%	+14%

Key Improvements

Capability	Base Mistral	ClaimSense AI
Fraud pattern detection	72% accuracy	91% accuracy (+26% relative)
Response consistency	68% structured	94% structured (+38% relative)
Insurance terminology	Generic	Domain-specific
Processing time	Manual (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)

Metric	Manual Process	With ClaimSense	Improvement
Claims/adjuster/day	15-20	45-60	3x throughput
Fraud detection rate	12%	34%	+183%
False positive rate	8%	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}
}

Acknowledgments

Mistral AI - Base model and hackathon
Weights & Biases - Experiment tracking
Bitext - Insurance dataset
HuggingFace - Model hosting and Spaces

Built with care for the Mistral AI Worldwide Hackathon 2026

pramodmisra/
claimsense-ai-v1