Insurance Risk Assessment: Scraping Vehicle Data at Scale

Insurance companies in Southeast Asia face a growing challenge: accurately assessing vehicle risk in markets where reliable data is fragmented across dozens of platforms and government databases. Traditional underwriting relies on limited data points, but modern insurers are discovering that web-scraped vehicle data can dramatically improve risk models, reduce claims costs, and enable more competitive pricing.

This guide explores how insurance companies use proxy infrastructure to collect vehicle data at scale for risk assessment, covering data sources, collection strategies, and practical applications in underwriting.

Why Scraped Vehicle Data Matters for Insurance

The Data Gap in Southeast Asian Markets

Unlike mature markets such as the US or UK, Southeast Asian automotive markets lack centralized data repositories. Vehicle history reports are incomplete, standardized safety ratings are not universally available, and pricing data is scattered across numerous platforms.

This fragmentation creates opportunities for insurers willing to invest in data collection infrastructure. By scraping vehicle data from multiple sources, insurers can build proprietary datasets that provide a significant underwriting advantage.

Key Data Points for Risk Assessment

Insurance risk models benefit from several categories of scraped vehicle data:

Vehicle Specifications:

Make, model, year, and variant
Engine size, power output, and drivetrain
Weight and dimensions
Safety features and ratings

Market Pricing:

Current market value (for sum insured validation)
Depreciation rates by model
Replacement part costs
Repair labor rates by region

Vehicle History:

Previous accident records
Modification history
Recall status
Ownership history

Claims Intelligence:

Common claim types by vehicle model
Repair cost patterns
Total loss thresholds
Parts availability issues

Data Collection Architecture for Insurance

Source Mapping

class InsuranceDataSources:
    SOURCES = {
        "pricing": {
            "sgcarmart": {"country": "SG", "type": "marketplace"},
            "carousell": {"country": "SG,MY,PH", "type": "marketplace"},
            "mudah": {"country": "MY", "type": "marketplace"},
            "carro": {"country": "SG,MY,TH,ID", "type": "dealer_platform"},
            "carsome": {"country": "MY,SG,TH,ID", "type": "dealer_platform"},
        },
        "safety": {
            "asean_ncap": {"country": "ASEAN", "type": "safety_rating"},
            "euro_ncap": {"country": "EU", "type": "safety_rating"},
            "iihs": {"country": "US", "type": "safety_rating"},
        },
        "specifications": {
            "nhtsa": {"country": "US", "type": "government_api"},
            "manufacturer_sites": {"country": "varies", "type": "oem"},
        },
        "parts_pricing": {
            "lazada": {"country": "SG,MY,TH,ID,PH", "type": "ecommerce"},
            "shopee": {"country": "SG,MY,TH,ID,PH,VN", "type": "ecommerce"},
            "autodoc": {"country": "global", "type": "parts_specialist"},
        },
        "government": {
            "lta_sg": {"country": "SG", "type": "registration"},
            "jpj_my": {"country": "MY", "type": "registration"},
        }
    }

Proxy Infrastructure for Insurance Data Collection

Insurance data collection requires accessing sources across multiple countries simultaneously. DataResearchTools mobile proxies provide the geographic coverage and reliability needed for this type of multi-source, multi-country operation.

class InsuranceProxyRouter:
    def __init__(self, api_key):
        self.api_key = api_key
        self.endpoint = "proxy.dataresearchtools.com"

    def get_proxy(self, source_config):
        country = source_config["country"].split(",")[0]  # Primary country
        return {
            "http": f"http://{self.api_key}:country-{country}-type-mobile@{self.endpoint}:8080",
            "https": f"http://{self.api_key}:country-{country}-type-mobile@{self.endpoint}:8080"
        }

    def get_proxies_for_multi_country(self, countries):
        return {
            country: {
                "http": f"http://{self.api_key}:country-{country}-type-mobile@{self.endpoint}:8080",
                "https": f"http://{self.api_key}:country-{country}-type-mobile@{self.endpoint}:8080"
            }
            for country in countries
        }

Collecting Vehicle Pricing Data for Sum Insured

Market Value Estimation

The most fundamental use of scraped data in insurance is validating the sum insured. Policyholders often over-insure or under-insure their vehicles. By scraping real-time market pricing, insurers can:

Verify that the declared value matches current market conditions
Detect potential fraud where vehicles are insured for significantly more than market value
Offer accurate guaranteed value products
Automate renewal sum insured calculations

class MarketValueEstimator:
    def __init__(self, proxy_manager):
        self.proxy_manager = proxy_manager
        self.scrapers = {
            "SG": [SGCarMartScraper, CarousellScraper],
            "MY": [MudahScraper, CarlistScraper],
        }

    def estimate_value(self, make, model, year, country, mileage_km=None):
        listings = self.collect_comparable_listings(make, model, year, country)

        if not listings:
            return None

        prices = [l["price"] for l in listings if l.get("price")]

        if mileage_km:
            # Weight listings closer in mileage more heavily
            weighted_prices = self.mileage_weighted_prices(listings, mileage_km)
        else:
            weighted_prices = prices

        return {
            "estimated_value": statistics.median(weighted_prices),
            "market_low": np.percentile(weighted_prices, 10),
            "market_high": np.percentile(weighted_prices, 90),
            "sample_size": len(weighted_prices),
            "data_sources": list(set(l["source"] for l in listings)),
            "as_of_date": datetime.now().isoformat(),
        }

    def collect_comparable_listings(self, make, model, year, country):
        all_listings = []
        scraper_classes = self.scrapers.get(country, [])

        for scraper_class in scraper_classes:
            proxy = self.proxy_manager.get_proxy({"country": country})
            scraper = scraper_class(proxy)
            listings = scraper.search(make=make, model=model, year_from=year-1, year_to=year+1)
            all_listings.extend(listings)

        return all_listings

Collecting Safety Rating Data

ASEAN NCAP Scraping

ASEAN NCAP provides crash test ratings for vehicles sold in Southeast Asia:

class ASEANNCAPScraper:
    def __init__(self, proxy_manager):
        self.proxy_manager = proxy_manager
        self.base_url = "https://aseancap.org"

    def scrape_ratings(self):
        proxy = self.proxy_manager.get_proxy({"country": "MY"})

        session = requests.Session()
        session.proxies.update(proxy)
        session.headers.update({"User-Agent": get_random_ua()})

        response = session.get(f"{self.base_url}/results", timeout=30)
        soup = BeautifulSoup(response.text, 'html.parser')

        ratings = []
        for vehicle_card in soup.select('.vehicle-result'):
            rating = {
                "make": safe_text(vehicle_card, '.make'),
                "model": safe_text(vehicle_card, '.model'),
                "year_tested": safe_text(vehicle_card, '.year'),
                "overall_stars": self.extract_stars(vehicle_card),
                "adult_occupant_score": safe_text(vehicle_card, '.adult-score'),
                "child_occupant_score": safe_text(vehicle_card, '.child-score'),
                "safety_assist_score": safe_text(vehicle_card, '.safety-assist'),
                "detail_url": vehicle_card.select_one('a')['href'] if vehicle_card.select_one('a') else None
            }
            ratings.append(rating)

        return ratings

    def get_detailed_report(self, detail_url):
        proxy = self.proxy_manager.get_proxy({"country": "MY"})

        session = requests.Session()
        session.proxies.update(proxy)
        response = session.get(f"{self.base_url}{detail_url}", timeout=30)
        soup = BeautifulSoup(response.text, 'html.parser')

        return {
            "frontal_impact": self.extract_test_result(soup, 'frontal'),
            "side_impact": self.extract_test_result(soup, 'side'),
            "pedestrian_protection": self.extract_test_result(soup, 'pedestrian'),
            "safety_features": self.extract_safety_features(soup),
        }

Collecting Parts and Repair Cost Data

Parts Pricing from E-Commerce Platforms

class PartsCosScraper:
    def __init__(self, proxy_manager):
        self.proxy_manager = proxy_manager

    def scrape_parts_prices(self, make, model, year, part_categories):
        results = {}

        for category in part_categories:
            search_query = f"{make} {model} {year} {category}"

            # Search across multiple platforms
            lazada_prices = self.search_lazada(search_query)
            shopee_prices = self.search_shopee(search_query)

            all_prices = lazada_prices + shopee_prices

            if all_prices:
                results[category] = {
                    "avg_price": statistics.mean(all_prices),
                    "min_price": min(all_prices),
                    "max_price": max(all_prices),
                    "sample_size": len(all_prices),
                }

        return results

    def search_lazada(self, query):
        proxy = self.proxy_manager.get_proxy({"country": "SG"})
        # Lazada search implementation
        # Returns list of prices for matching parts
        pass

    def search_shopee(self, query):
        proxy = self.proxy_manager.get_proxy({"country": "SG"})
        # Shopee search implementation
        pass

Building Risk Models with Scraped Data

Feature Engineering

Transform scraped data into features for risk models:

class RiskFeatureBuilder:
    def build_features(self, vehicle_data, market_data, safety_data, parts_data):
        features = {}

        # Vehicle age and depreciation features
        current_year = datetime.now().year
        features["vehicle_age"] = current_year - vehicle_data["year"]
        features["depreciation_rate"] = self.calculate_depreciation_rate(vehicle_data, market_data)

        # Safety features
        if safety_data:
            features["ncap_stars"] = safety_data.get("overall_stars", 0)
            features["has_abs"] = 1 if "ABS" in safety_data.get("safety_features", []) else 0
            features["has_esc"] = 1 if "ESC" in safety_data.get("safety_features", []) else 0
            features["has_airbags"] = safety_data.get("airbag_count", 0)

        # Parts cost features
        if parts_data:
            features["bumper_cost"] = parts_data.get("front_bumper", {}).get("avg_price", 0)
            features["headlight_cost"] = parts_data.get("headlight", {}).get("avg_price", 0)
            features["windscreen_cost"] = parts_data.get("windscreen", {}).get("avg_price", 0)
            features["parts_availability"] = self.score_parts_availability(parts_data)

        # Market features
        features["market_value"] = market_data.get("estimated_value", 0)
        features["market_liquidity"] = market_data.get("sample_size", 0)
        features["price_volatility"] = self.calculate_volatility(market_data)

        # Engine and performance features
        features["engine_cc"] = vehicle_data.get("engine_cc", 0)
        features["power_hp"] = vehicle_data.get("power_hp", 0)
        features["power_to_weight"] = self.calculate_power_to_weight(vehicle_data)

        return features

    def calculate_depreciation_rate(self, vehicle_data, market_data):
        current_value = market_data.get("estimated_value", 0)
        original_price = vehicle_data.get("original_price", 0)
        age = datetime.now().year - vehicle_data["year"]

        if original_price and age > 0:
            return ((original_price - current_value) / original_price) / age
        return None

Risk Scoring

class VehicleRiskScorer:
    def __init__(self, model):
        self.model = model  # Trained risk model

    def score_vehicle(self, features):
        risk_score = self.model.predict(features)

        return {
            "risk_score": risk_score,
            "risk_category": self.categorize_risk(risk_score),
            "contributing_factors": self.explain_score(features, risk_score),
            "recommended_premium_adjustment": self.calculate_adjustment(risk_score),
        }

    def categorize_risk(self, score):
        if score < 0.2:
            return "very_low"
        elif score < 0.4:
            return "low"
        elif score < 0.6:
            return "medium"
        elif score < 0.8:
            return "high"
        else:
            return "very_high"

    def explain_score(self, features, score):
        factors = []
        if features.get("ncap_stars", 0) >= 4:
            factors.append({"factor": "High safety rating", "impact": "reduces_risk"})
        if features.get("vehicle_age", 0) > 8:
            factors.append({"factor": "Older vehicle", "impact": "increases_risk"})
        if features.get("parts_availability", 0) < 0.5:
            factors.append({"factor": "Limited parts availability", "impact": "increases_risk"})
        if features.get("power_to_weight", 0) > 100:
            factors.append({"factor": "High performance vehicle", "impact": "increases_risk"})
        return factors

Fraud Detection with Scraped Data

Over-Insurance Detection

class OverInsuranceDetector:
    def check_sum_insured(self, policy, market_estimator):
        declared_value = policy["sum_insured"]
        vehicle = policy["vehicle"]

        market_estimate = market_estimator.estimate_value(
            make=vehicle["make"],
            model=vehicle["model"],
            year=vehicle["year"],
            country=policy["country"],
            mileage_km=vehicle.get("mileage_km")
        )

        if not market_estimate:
            return {"status": "unable_to_verify"}

        ratio = declared_value / market_estimate["estimated_value"]

        if ratio > 1.3:
            return {
                "status": "over_insured",
                "declared_value": declared_value,
                "market_value": market_estimate["estimated_value"],
                "over_insurance_pct": (ratio - 1) * 100,
                "recommendation": "Review sum insured with policyholder",
                "market_data": market_estimate,
            }
        elif ratio < 0.7:
            return {
                "status": "under_insured",
                "declared_value": declared_value,
                "market_value": market_estimate["estimated_value"],
                "under_insurance_pct": (1 - ratio) * 100,
                "recommendation": "Advise policyholder of under-insurance risk",
            }

        return {"status": "within_range", "ratio": ratio}

Staged Accident Detection

Cross-reference claims data with vehicle listing data to detect suspicious patterns:

def check_for_suspicious_listings(vin, claim_date, proxy_manager):
    """Check if a vehicle involved in a claim was listed for sale before the incident"""
    scrapers = get_marketplace_scrapers(proxy_manager)

    for scraper in scrapers:
        listings = scraper.search_by_vin(vin)
        for listing in listings:
            listing_date = parse_date(listing.get("listed_date"))
            if listing_date and listing_date < claim_date:
                days_before_claim = (claim_date - listing_date).days
                if days_before_claim < 30:
                    return {
                        "flag": "VEHICLE_LISTED_BEFORE_CLAIM",
                        "severity": "high",
                        "listing_date": listing_date,
                        "claim_date": claim_date,
                        "days_before": days_before_claim,
                        "platform": listing.get("platform"),
                    }

    return None

Continuous Data Pipeline

Scheduling Data Collection

class InsuranceDataScheduler:
    def __init__(self, proxy_manager):
        self.proxy_manager = proxy_manager

    def run_daily_collection(self):
        # Update market pricing data
        self.collect_pricing_data()

        # Refresh safety ratings (monthly is sufficient)
        if datetime.now().day == 1:
            self.collect_safety_data()

        # Update parts pricing (weekly)
        if datetime.now().weekday() == 0:
            self.collect_parts_data()

    def collect_pricing_data(self):
        countries = ["SG", "MY", "TH", "ID"]
        for country in countries:
            self.collect_country_pricing(country)

    def collect_country_pricing(self, country):
        proxy = self.proxy_manager.get_proxy({"country": country})
        # Run pricing scrapers for this country
        pass

Compliance and Data Privacy

Insurance companies must handle scraped vehicle data carefully:

Personal data: Avoid collecting seller personal information that is not needed for risk assessment
Data retention: Implement retention policies that comply with local regulations (PDPA in Singapore, PDPA in Malaysia/Thailand)
Purpose limitation: Use collected data only for stated insurance purposes
Data security: Encrypt stored vehicle data and limit access to authorized personnel

Conclusion

Scraped vehicle data transforms insurance risk assessment from an art into a science. By systematically collecting pricing, safety, specification, and parts cost data from across Southeast Asian markets, insurers can build more accurate risk models, detect fraud more effectively, and price policies more competitively.

DataResearchTools provides the mobile proxy infrastructure that makes this data collection reliable and scalable. With carrier-grade IPs across Singapore, Malaysia, Thailand, Indonesia, and the Philippines, DataResearchTools ensures insurance companies can access the automotive data sources they need for comprehensive risk assessment. The combination of geographic coverage, high trust scores, and scalable bandwidth makes DataResearchTools an ideal foundation for insurance data operations in Southeast Asia.