Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (November 2015 to December 2018) concerning non-life motor insurance portfolio. This dataset comprises 105,555 rows and 30 columns. Each row signifies a policy transaction, while each column represents a distinct variable.

Context

The company has shared its annual car insurance data. Now, you have to find out the real customer behaviors over the data.

Content

The columns are resembling practical world features. The outcome column indicates 1 if a customer has claimed his/her loan else 0. The data has 19 features from there 18 of them are corresponding logs which were taken by the company.

Acknowledgements

Mostly the data is real and some part of it is also generated by me.

Inspiration

The data is so well balanced that it will help kagglers find a better intuition of real customers and find the deepest story lien within it.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

ObjectiveThe Social Health Insurance Program (SHIP) shares a major portion of social security, and is also key to Universal Health Coverage (UHC) and health equity. The Government of Nepal launched SHIP in the Fiscal Year 2015/16 for the first phase in three districts, on the principle of financial risk protection through prepayment and risk pooling in health care. Furthermore, the adoption of the program depends on the stakeholders' behaviors, mainly, the beneficiaries and the providers. Therefore, we aimed to explore and assess their perception and experiences regarding various factors acting on SHIP enrollment and adherence.MethodsA cross-sectional, facility-based, concurrent mixed-methods study was carried out in seven health facilities in the Kailali, Baglung, and Ilam districts of Nepal. A total of 822 beneficiaries, sampled using probability proportional to size (PPS), attending health care institutions, were interviewed using a structured questionnaire for quantitative data. A total of seven focus group discussions (FGDs) and 12 in-depth interviews (IDIs), taken purposefully, were conducted with beneficiaries and service providers, using guidelines, respectively. Quantitative data were entered into Epi-data and analyzed with SPSS, MS-Excel, and Epitools, an online statistical calculator. Manual thematic analysis with predefined themes was carried out for qualitative data. Percentage, frequency, mean, and median were used to describe the variables, and the Chi-square test and binary logistic regression were used to infer the findings. We then combined the qualitative data from beneficiaries' and providers' perceptions, and experiences to explore different aspects of health insurance programs as well as to justify the quantitative findings.Results and prospectsOf a total of 822 respondents (insured-404, uninsured-418), 370 (45%) were men. Families' median income was USD $65.96 (8.30–290.43). The perception of insurance premiums did not differ between the insured and uninsured groups (p = 0.53). Similarly, service utilization (OR = 220.4; 95% CI, 123.3–393.9) and accessibility (OR = 74.4; 95% CI, 42.5–130.6) were found to have high odds among the insured as compared to the uninsured respondents. Qualitative findings showed that the coverage and service quality were poor. Enrollment was gaining momentum despite nearly a one-tenth (9.1%) dropout rate. Moreover, different aspects, including provider-beneficiary communication, benefit packages, barriers, and ways to go, are discussed. Additionally, we also argue for some alternative health insurance schemes and strategies that may have possible implications in our contexts.ConclusionAlthough enrollment is encouraging, adherence is weak, with a considerable dropout rate and poor renewal. Patient management strategies and insurance education are recommended urgently. Furthermore, some alternate schemes and strategies may be considered.

Data on Medicaid coverage among persons under age 65 by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Health Interview Survey, health insurance supplements (1984, 1989, 1994-1996). Starting with 1997, data are from the family core and the sample adult questionnaires. Data for level of difficulty are from the 2010 Quality of Life, 2011-2017 Functioning and Disability, and 2018 Sample Adult questionnaires. For more information on the National Health Interview Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Machine Learning in Insurance Market Outlook

As per our latest research, the global machine learning in insurance market size reached USD 4.2 billion in 2024, demonstrating robust momentum driven by rapid digital transformation and the increasing adoption of artificial intelligence across the insurance sector. The market is projected to grow at a CAGR of 26.8% from 2025 to 2033, reaching a forecasted value of approximately USD 39.5 billion by 2033. This extraordinary growth is primarily fueled by the need for enhanced operational efficiency, improved customer experience, and advanced risk mitigation strategies within the insurance industry.

One of the principal growth drivers for the machine learning in insurance market is the rising demand for automation in claims processing and risk management. As insurers face mounting pressure to reduce operational costs and accelerate service delivery, machine learning technologies are increasingly being deployed to automate repetitive tasks, minimize manual errors, and streamline claims adjudication. The integration of predictive analytics enables insurers to assess claims more accurately, detect anomalies, and flag potentially fraudulent activities in real-time. This not only expedites the claims settlement process but also enhances customer satisfaction by providing faster and more transparent services. Furthermore, the ability of machine learning algorithms to learn from historical data and continuously improve their accuracy makes them indispensable tools for modern insurance operations.

Another significant factor propelling market growth is the escalating volume and complexity of data generated by insurers. With the proliferation of digital channels, IoT devices, and telematics, insurance companies are inundated with vast amounts of structured and unstructured data. Machine learning models excel at extracting actionable insights from these massive datasets, enabling insurers to personalize offerings, optimize pricing, and anticipate customer needs. Advanced risk assessment and underwriting solutions powered by machine learning allow for more granular segmentation and dynamic pricing, which in turn leads to better risk selection and improved profitability. The growing emphasis on data-driven decision-making is expected to further accelerate the adoption of machine learning technologies across all segments of the insurance value chain.

The evolving regulatory landscape and the increasing focus on fraud detection and prevention also play a pivotal role in shaping the machine learning in insurance market. Regulatory bodies worldwide are mandating stricter compliance standards and demanding greater transparency in insurance transactions. Machine learning-based fraud detection tools are becoming essential for insurers to comply with these regulations, as they can identify suspicious patterns and flag high-risk transactions with greater accuracy than traditional rule-based systems. Additionally, the rising sophistication of cyber threats and the increasing incidence of insurance fraud are compelling insurers to invest heavily in advanced analytics and machine learning solutions to safeguard their operations and maintain consumer trust.

From a regional perspective, North America currently dominates the machine learning in insurance market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced technologies, the presence of leading insurance companies, and significant investments in digital infrastructure contribute to North America's leadership position. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, driven by rapid urbanization, the expansion of the middle class, and increasing digital literacy. Emerging markets in Latin America and the Middle East & Africa are also witnessing growing interest in machine learning applications, particularly in areas such as fraud prevention and customer engagement, albeit from a lower base.

Component Analysis

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The Survey of Household Spending provides detailed information on household expenditures, dwelling characteristics, and ownership of household equipment.The Survey of Household Spending is carried out annually across Canada in the ten provinces. Data for the territories are available for 1998, 1999 and every second year thereafter.The 2011 SHS was conducted from January 2011 to December 2011 using a sample of 17,873 households in the 10 provinces (the territories were not includ ed in the 2011 survey). Detailed spending information was collected, as well as limited information on dwelling characteristics and household equipment. The method of adjusting for incomplete diaries has been refined with the 2011 SHS. As well, the age of household members is now defined to be at the time of the interview rather than as of December 31st of the survey year. To ensure comparability of the data, the 2010 data have also been revised by incorporating these changes. The revised 2010 estimate of average household spending on all types of goods and services has increased by 1.3% when compared with the previously published 2010 estimate (April 2012). Starting with the 2011 SHS, Standard Tables are available in CANSIM. Some data tables are provided from the link below, and CANSIM tables can also be found under Related Materials.

This dataset contains records of motorcycle sales across various Indian states, covering top brands like Honda, Royal Enfield, TVS, Yamaha, Hero, Bajaj, KTM, and Kawasaki. The dataset includes key attributes such as average daily distance traveled, engine capacity, fuel type, mileage, price, resale value, insurance status, and seller type. It provides insights into bike sales trends, market demand, and resale values across different city tiers.

🔹 Use Cases:

Market Analysis: Understand the sales trend of different brands and models.

Resale Price Estimation: Analyze depreciation trends.

Consumer Behavior: Study how owner type, insurance, and mileage impact pricing.

Geographical Trends: Identify demand patterns in different Indian states and city tiers.

State (Random Indian states)

Average Daily Distance (in km, between 5-80 km)

Bike Brand (One of the top 10 brands you listed)

Model Name (Random bike models per brand)

Price (INR) (Based on brand & model)

Year of Manufacture (2015-2024)

Engine Capacity (cc) (100cc - 1000cc)

Fuel Type (Petrol, Electric, Hybrid)

Mileage (km/l) (Varies by brand)

Owner Type (First, Second, Third)

Registration Year (Varies based on Year of Manufacture)

Insurance Status (Active, Expired, Not Available)

Seller Type (Dealer, Individual)

Resale Price (INR) (Based on depreciation formula)

City Tier (Metro, Tier 1, Tier 2, Tier 3)