insurance-expert

name	insurance-expert
version	1.0.0
description	Expert-level insurance systems, underwriting, claims processing, actuarial analysis, risk assessment, and insurtech solutions
category	domains
tags	["insurance","underwriting","claims","actuarial","risk","insurtech"]
allowed-tools	["Read","Write","Edit"]

Insurance Expert

Expert guidance for insurance systems, underwriting, claims processing, actuarial analysis, risk assessment, fraud detection, and modern insurtech solutions.

Core Concepts

Insurance Systems

• Policy Administration Systems (PAS)
• Claims Management Systems
• Underwriting workstations
• Actuarial modeling systems
• Reinsurance management
• Agency management systems
• Document management

Insurance Types

• Property & Casualty (P&C)
• Life insurance
• Health insurance
• Auto insurance
• Commercial insurance
• Specialty insurance
• Cyber insurance

Standards and Regulations

• ACORD standards (insurance data exchange)
• SOX compliance
• State insurance regulations
• NAIC (National Association of Insurance Commissioners)
• GDPR for customer data
• Anti-money laundering (AML)

Policy Administration System

from dataclasses import dataclass
from datetime import datetime, timedelta
from decimal import Decimal
from typing import List, Optional
from enum import Enum

class PolicyStatus(Enum):
    QUOTED = "quoted"
    BOUND = "bound"
    ACTIVE = "active"
    CANCELLED = "cancelled"
    EXPIRED = "expired"
    LAPSED = "lapsed"

class CoverageType(Enum):
    LIABILITY = "liability"
    COLLISION = "collision"
    COMPREHENSIVE = "comprehensive"
    MEDICAL = "medical"
    UNINSURED_MOTORIST = "uninsured_motorist"

@dataclass
class Insured:
    """Insured party information"""
    insured_id: str
    first_name: str
    last_name: str
    date_of_birth: datetime
    address: dict
    phone: str
    email: str
    drivers_license: str
    credit_score: int

@dataclass
class Coverage:
    """Insurance coverage details"""
    coverage_type: CoverageType
    limit: Decimal
    deductible: Decimal
    premium: Decimal

@dataclass
 :
    
    policy_number: 
    insured: Insured
    policy_type:   
    effective_date: datetime
    expiration_date: datetime
    status: PolicyStatus
    coverages: [Coverage]
    total_premium: Decimal
    payment_plan:   
    underwriter_id: 
    risk_score: 

 :
    

     ():
        .policies = {}
        .quotes = {}

     () -> :
        
        
        insured = Insured(
            insured_id=._generate_id(),
            first_name=application[],
            last_name=application[],
            date_of_birth=application[],
            address=application[],
            phone=application[],
            email=application[],
            drivers_license=application.get(, ),
            credit_score=application.get(, )
        )

        
        risk_score = ._calculate_risk_score(insured, application)

        
        coverages = ._determine_coverages(application, risk_score)

        
        total_premium = (c.premium  c  coverages)

        
        discounts = ._calculate_discounts(application)
        discount_amount = total_premium * ((discounts.values()) / )
        total_premium = total_premium - discount_amount

        quote = {
            : ._generate_id(),
            : insured,
            : application[],
            : coverages,
            : total_premium,
            : risk_score,
            : discounts,
            : datetime.now() + timedelta(days=)
        }

        .quotes[quote[]] = quote

         quote

     () -> :
        
        score =   

        
        age = (datetime.now() - insured.date_of_birth).days / 
         age < :
            score += 
         age < :
            score -= 
        :
            score += 

        
         insured.credit_score < :
            score += 
         insured.credit_score > :
            score -= 

        
         application.get(, ) > :
            score += application[] * 

         application.get(, ) > :
            score += application[] * 

        
         application.get(, ) > :
            score += application[] * 

         (, (, score))  

     () -> [Coverage]:
        
        coverages = []
        base_rate = Decimal()

        
        risk_multiplier = Decimal(( + (risk_score / )))

         application[] == :
            
            coverages.append(Coverage(
                coverage_type=CoverageType.LIABILITY,
                limit=Decimal(),
                deductible=Decimal(),
                premium=base_rate * risk_multiplier
            ))

            
             application.get(, ):
                deductible = Decimal((application.get(, )))
                premium = base_rate * Decimal() * risk_multiplier
                
                premium = premium * (Decimal() / deductible) * Decimal()

                coverages.append(Coverage(
                    coverage_type=CoverageType.COLLISION,
                    limit=Decimal((application.get(, ))),
                    deductible=deductible,
                    premium=premium
                ))

            
             application.get(, ):
                deductible = Decimal((application.get(, )))
                premium = base_rate * Decimal() * risk_multiplier

                coverages.append(Coverage(
                    coverage_type=CoverageType.COMPREHENSIVE,
                    limit=Decimal((application.get(, ))),
                    deductible=deductible,
                    premium=premium
                ))

         coverages

     () -> :
        
        discounts = {}

        
         application.get(, ):
            discounts[] =   

        
         application.get(, ) ==   application.get(, ) == :
            discounts[] =   

        
         application.get(, ):
            discounts[] =   

        
         application.get(, ):
            discounts[] =   

         discounts

     () -> Policy:
        
        quote = .quotes.get(quote_id)
          quote:
             ValueError()

        
         datetime.now() > quote[]:
             ValueError()

        policy_number = ._generate_policy_number()

        policy = Policy(
            policy_number=policy_number,
            insured=quote[],
            policy_type=quote[],
            effective_date=datetime.now(),
            expiration_date=datetime.now() + timedelta(days=),
            status=PolicyStatus.ACTIVE,
            coverages=quote[],
            total_premium=quote[],
            payment_plan=,
            underwriter_id=,
            risk_score=quote[]
        )

        .policies[policy_number] = policy

         policy

     () -> :
        
        policy = .policies.get(policy_number)
          policy:
             {: }

        
        
        new_risk_score = policy.risk_score *   

        
        inflation_factor = Decimal()  
        new_premium = policy.total_premium * inflation_factor * Decimal(())  

         {
            : policy_number,
            : (policy.total_premium),
            : (new_premium),
            : policy.expiration_date,
            : policy.expiration_date + timedelta(days=)
        }

     () -> :
        
        policy = .policies.get(policy_number)
          policy:
             {: }

         effective_date  :
            effective_date = datetime.now()

        
        days_active = (effective_date - policy.effective_date).days
        total_days = (policy.expiration_date - policy.effective_date).days
        earned_premium = policy.total_premium * (Decimal(days_active) / Decimal(total_days))

        
        refund_amount = policy.total_premium - earned_premium

        policy.status = PolicyStatus.CANCELLED

         {
            : policy_number,
            : effective_date.isoformat(),
            : reason,
            : (earned_premium),
            : (refund_amount)
        }

     () -> :
        
         uuid
         

     () -> :
         uuid
         uuid.uuid4().[:].upper()

Claims Management System

from enum import Enum

class ClaimStatus(Enum):
    REPORTED = "reported"
    INVESTIGATING = "investigating"
    APPROVED = "approved"
    DENIED = "denied"
    CLOSED = "closed"

@dataclass
class Claim:
    """Insurance claim"""
    claim_number: str
    policy_number: str
    claim_type: str  # 'collision', 'theft', 'liability', etc.
    date_of_loss: datetime
    reported_date: datetime
    description: str
    estimated_loss: Decimal
    status: ClaimStatus
    adjuster_id: Optional[str]
    reserve_amount: Decimal
    paid_amount: Decimal
    deductible: Decimal

class ClaimsManagementSystem:
    """Claims processing and management"""

    def __init__(self):
        self.claims = {}
        self.fraud_detector = FraudDetectionSystem()

    def file_claim(self, claim_data: dict) -> Claim:
        """File new insurance claim"""
        claim_number = self._generate_claim_number()

        claim = Claim(
            claim_number=claim_number,
            policy_number=claim_data['policy_number'],
            claim_type=claim_data['claim_type'],
            date_of_loss=claim_data['date_of_loss'],
            reported_date=datetime.now(),
            description=claim_data['description'],
            estimated_loss=Decimal((claim_data.get(, ))),
            status=ClaimStatus.REPORTED,
            adjuster_id=,
            reserve_amount=Decimal(),
            deductible=Decimal((claim_data.get(, ))),
            paid_amount=Decimal()
        )

        
        fraud_result = .fraud_detector.screen_claim(claim)
         fraud_result[] > :
            claim.status = ClaimStatus.INVESTIGATING
            ._flag_for_siu(claim, fraud_result)  

        
        claim.adjuster_id = ._assign_adjuster(claim)

        
        claim.reserve_amount = ._calculate_reserve(claim)

        .claims[claim_number] = claim

         claim

     () -> :
        
        claim = .claims.get(claim_number)
          claim:
             {: }

        claim.status = ClaimStatus.INVESTIGATING

        
        investigation_steps = [
            ,
            ,
            ,
            ,
            ,
            ,
            
        ]

         {
            : claim_number,
            : claim.status.value,
            : investigation_steps,
            : (datetime.now() + timedelta(days=)).isoformat()
        }

     () -> :
        
        claim = .claims.get(claim_number)
          claim:
             {: }

        
          ._validate_coverage(claim):
             {: }

        
        payment_amount = approved_amount - claim.deductible
         payment_amount <= :
             {: }

        claim.status = ClaimStatus.APPROVED
        claim.paid_amount = payment_amount

        
        payment_result = ._process_payment(claim, payment_amount)

         {
            : claim_number,
            : (approved_amount),
            : (claim.deductible),
            : (payment_amount),
            : payment_result[],
            : datetime.now().isoformat()
        }

     () -> :
        
        claim = .claims.get(claim_number)
          claim:
             {: }

        claim.status = ClaimStatus.DENIED

        
        ._send_denial_letter(claim, reason)

         {
            : claim_number,
            : ,
            : reason,
            : (datetime.now() + timedelta(days=)).isoformat()
        }

     () -> Decimal:
        
        
        
        reserve_multipliers = {
            : Decimal(),
            : Decimal(),
            : Decimal(),
            : Decimal()
        }

        multiplier = reserve_multipliers.get(claim.claim_type, Decimal())
        reserve = claim.estimated_loss * multiplier

         reserve

     () -> :
        
        
         

     () -> :
        
        
         

     () -> :
        
        
         {: , : }

     ():
        
        
        

     ():
        
        
        

     () -> :
         uuid
         

 :
    

     () -> :
        
        fraud_score = 
        indicators = []

        
        
        days_to_report = (claim.reported_date - claim.date_of_loss).days
         days_to_report > :
            fraud_score += 
            indicators.append()

        
         claim.estimated_loss > Decimal():
            fraud_score += 
            indicators.append()

        
        

         {
            : fraud_score,
            : indicators,
            :   fraud_score >   
        }

Actuarial Analysis

import numpy as np
from scipy import stats

class ActuarialAnalysis:
    """Actuarial modeling and analysis"""

    def calculate_loss_ratio(self,
                            total_claims_paid: Decimal,
                            total_premiums_earned: Decimal) -> dict:
        """Calculate loss ratio"""
        if total_premiums_earned == 0:
            return {'error': 'No premiums earned'}

        loss_ratio = (total_claims_paid / total_premiums_earned) * 100

        # Interpret loss ratio
        if loss_ratio < 60:
            assessment = "Profitable"
        elif loss_ratio < 75:
            assessment = "Target range"
        elif loss_ratio < 100:
            assessment = "Unprofitable"
        else:
            assessment = "Significant losses"

        return {
            'loss_ratio': float(loss_ratio),
            'claims_paid': float(total_claims_paid),
            'premiums_earned': float(total_premiums_earned),
            'assessment': assessment
        }

    def calculate_combined_ratio(self,
                                 loss_ratio: float,
                                 expense_ratio: ) -> :
        
        combined_ratio = loss_ratio + expense_ratio

        profitable = combined_ratio < 

         {
            : combined_ratio,
            : loss_ratio,
            : expense_ratio,
            : profitable,
            :  - combined_ratio
        }

     () -> :
        
        
        

        open_claims = [c  c  claim_data  c[] != ]
        total_incurred = (c[] + c[]  c  open_claims)

         {
            : total_incurred,
            : (open_claims),
            : 
        }

     () -> Decimal:
        
        
        pure_premium = expected_claims

        
        expense_load = pure_premium * Decimal((expense_ratio / ))

        
        profit_load = pure_premium * Decimal((profit_margin / ))

        
        total_premium = pure_premium + expense_load + profit_load

         total_premium.quantize(Decimal())

Best Practices

Underwriting

• Use consistent risk assessment criteria
• Implement automated underwriting for simple cases
• Maintain underwriting guidelines documentation
• Use predictive analytics for risk scoring
• Conduct regular portfolio reviews
• Segment risks appropriately
• Monitor loss ratios by segment

Claims Processing

• Provide 24/7 claim reporting
• Assign adjusters quickly
• Set appropriate reserves
• Communicate regularly with claimants
• Implement fraud detection
• Track claim cycle time
• Use photos and video for inspections

Fraud Prevention

• Screen all claims for fraud indicators
• Use predictive analytics
• Maintain Special Investigation Unit (SIU)
• Share fraud data industry-wide
• Train staff on fraud detection
• Implement identity verification
• Monitor for organized fraud rings

Compliance

• Maintain state licensing
• Follow NAIC model laws
• Implement proper data privacy controls
• Conduct regular compliance audits
• Maintain required reserves
• File timely regulatory reports
• Follow fair claims practices

Anti-Patterns

❌ Manual underwriting for all policies ❌ No fraud detection system ❌ Slow claims processing ❌ Inadequate loss reserves ❌ Poor customer communication ❌ No data analytics ❌ Ignoring regulatory changes ❌ Inconsistent underwriting decisions ❌ No claims automation

Resources

• ACORD Standards: https://www.acord.org/
• NAIC (National Association of Insurance Commissioners): https://www.naic.org/
• ISO (Insurance Services Office): https://www.verisk.com/iso/
• Society of Actuaries: https://www.soa.org/
• Casualty Actuarial Society: https://www.casact.org/
• Insurance Information Institute: https://www.iii.org/
• A.M. Best (ratings): https://www.ambest.com/