Insurance Fraud, Early Detection is More Important than Post-Detection
I found out it was a fraud after claiming insurance benefits, but it was already paid.
Insurance fraud is increasing every year. Especially, organized fraud involving brokers, hospitals, and internal employees causes significant damages, and even if detected after the fact, recovery is difficult.
Insurance companies face the following concerns:
- It is difficult to identify which hospitals have a high occurrence of abnormal claims.
- There is a lack of personnel to monitor illegal brokers' activities on blogs and social media.
- Data-based systems to detect fraud patterns are lacking.
- Even if detected, it is difficult to recover losses after insurance benefits have been paid.
Relying solely on post-detection is already too late. A system to detect risk signals in advance is necessary.
Early Detection of Fraud Risks through Crawling + AI Analysis
By utilizing Hashscraper, data related to insurance fraud can be automatically collected and analyzed from blogs, social media, map services, etc.
Actual Crawling Data Example - Blog Post
{
"Channel": "네이버 블로그",
"Title": "실비보험 가입하고 OO병원에서 치료받으면 보험금 잘 나와요",
"Body": "제가 소개해드리는 병원에서 치료받으시면 보험금 청구까지 도와드려요. 관심 있으신 분은 카톡 문의주세요.",
"Author": "user_****",
"Post Date": "2025-01-02",
"Tags": ["실비보험", "보험금청구", "병원추천"]
}
Actual Crawling Data Example - Hospital Information
{
"Channel": "네이버 지도",
"Hospital Name": "OO의원",
"Category": "정형외과",
"Address": "서울시 강남구 OO동",
"Rating": "4.8",
"Review Count": "2,847",
"Collected Date": "2025-01-06"
}
Analysis of illegal broker activity patterns from blogs and hospital information from map services can be collected for cross-analysis.
Use Case: Establishment of an Insurance Fraud Prevention System for a Life Insurance Company
A domestic life insurance company aimed to strengthen its fraud prevention system. The increase in fraud involving brokers, hospitals, and internal employees lacked a system for early detection.
Crawling Settings
- Blog: Company's insurance product names + keywords related to insurance claims
- Social Media: Posts related to insurance fraud and brokers
- Map Services: Comprehensive collection of domestic hospital information
- Crawling Frequency: Once a day
Items Analyzed from Crawling Data
| 분석 항목 | 활용 방법 |
|-----------|-----------|
| 브로커 활동 패턴 | 보험금 청구 대행, 병원 연결 등 불법 영업 게시글 탐지 |
| 병원별 리스크 지수 | 비정상적 청구 패턴이 있는 병원 식별 |
| 키워드 트렌드 | 새로운 사기 수법이나 타겟 상품 조기 파악 |
| 네트워크 분석 | 브로커-병원-직원 간 연결 고리 추적 |
Example of AI Analysis Data
{
"Channel": "네이버 블로그",
"Post Body": "실비보험 가입하고 OO병원에서 치료받으면 보험금 잘 나와요. 카톡 문의주세요.",
"Risk Level": "High",
"Risk Factors": ["보험금 청구 대행 암시", "특정 병원 유도", "개인 연락처 노출"],
"Categories": [
{"category": "Fraud Indicator", "subcategory": "Broker Activity", "type": "Primary"},
{"category": "Fraud Indicator", "subcategory": "Hospital Referral", "type": "Secondary"}
]
}
Achievements
- Early identification of high-risk channels and hospitals for insurance fraud
- Enhanced risk management efficiency
- Advanced internal control system
The collected data is integrated with the internal review system to classify claims from high-risk hospitals for additional review.
Other Use Cases
Monitoring of New Fraud Methods
New fraud methods are sometimes shared on blogs and communities. By monitoring relevant keywords, new methods can be identified early.
Detection of Internal Employee Involvement
Cross-analysis of crawling data and internal data can identify if the same hospital or broker appears repeatedly in cases handled by specific employees.
Expansion to Property Insurance Sector
Similar methods can be used to monitor fraud risks in property insurance, such as auto insurance and fire insurance.
Data Integration Methods
Collected data is provided in raw data format, and integration methods can be selected according to the situation.
- Excel Download
- Email Sending
- API Integration — Integration with internal databases
- DB Integration — Direct loading into internal analysis systems
Conclusion
Early detection is more important than post-detection in insurance fraud. By monitoring broker activities on blogs and social media, and analyzing hospital information, risk signals can be captured early.
Hashscraper handles crawler operation, maintenance, and monitoring. Customers do not need to directly respond to platform policy changes or collection errors.
By establishing a data-based fraud detection system, risks can be filtered out before paying insurance benefits.
Start Now
With Hashscraper, you can automatically collect blog and map service data and analyze it with AI.
If you need customized crawling or AI analysis, please contact us.
