Data_Science_in_the_Insurance_Market.pdf

The insurance sector is viewed as one of the most competitive and less predictable business spheres. It is related to risk; therefore, it has always depended on statistics.
Insurance companies have a wide range of information sources for the relevant risk assessment. Big Data technologies are applied to predict risks and claims and monitor and analyze them to develop compelling customer attraction and retention strategies. Undoubtedly, insurance companies benefit from data science applications within their operations.
Source: ActiveWizards

Transcript

1. How insurance companies employ data science techniques Data Science in the Insurance Market
2. Introduction to Data Science What is the Insurance Market? Use of Data Science in Insurance Markets Tools Used - Case Study - Real World Example Importance of data science in Insurance market Genaral concerns Conclusion Order of the Presentation Points for discussion
3. Data Science Data Science is a field of study that focuses on finding meaningful correlations between large datasets and uncovering the specifics of extracted insights. It confines preparing data, cleansing, aggregating, and manipulating it to perform advanced data analysis. It is also used for predictive modeling. It combines statistics, analytics, and informatics, which makes understanding real situations easy with data.
4. What is the Insurance Market? Life Insurance General Insurance Insurance is a form of risk management that means hedging against future contingent risk. Therefore, an insurance market refers to the buying and selling of insurance for coverage of specific risks. It can be broadly categorized into two types:
5. Insurance Market These challenges force insurers to generate insights from data to enhance pricing mechanisms, understand customers, safeguard fraud, and analyze risks. Data analytics collate more precise information about several transactions, product performance, customer satisfaction, etc. Fraud Risk Challenges Competition Optimum Pricing Theft Risk
6. Use of data science in Insurance Market Why is it important? The insurance sector is viewed as one of the most competitive and less predictable business spheres. It is related to risk; therefore, it has always depended on statistics. Insurance companies have a wide range of information sources for the relevant risk assessment. Big Data technologies are applied to predict risks and claims and monitor and analyze them to develop compelling customer attraction and retention strategies. Undoubtedly, insurance companies benefit from data science applications within their operations.
7. Fraud Detection Claim Development Modelling Insurance fraud brings vast financial loss to insurance companies. Data science platforms and software made it possible to detect fraudulent activity, suspicious links, and subtle behavior patterns using multiple techniques. Usually, insurance companies use statistical models for efficient fraud detection. These models rely on previous cases of fraudulent activity and apply sampling methods to analyze them. For this purpose, the algorithms should be fed with a constant data flow. Predictive modeling techniques are also used here to analyze and filter fraud instances. Identifying links between suspicious activities helps recognize fraud schemes that had previously gone undetected. The claim amount can change drastically from an insurance claim's initial filing to full payment. Hence the ability to predict the final claim amount significantly impacts financial statements. An extremely accurate and automatic predictive model can be built to understand better how much a claim will ultimately cost. It also shows the trend in the number of claims over a year. So this helps them to predict the future and also helps to give the best recommendation according to customers' needs.
8. Personalized Marketing Highly personalized and relevant insurance experiences are assured with the help of artificial intelligence and advanced analytics, extracting insights from a vast amount of demographic data, preferences, interaction, behavior, attitude, lifestyle details, interests, hobbies, etc. Consumers tend to look for personalized offers, policies, loyalty programs, recommendations, and options. The platforms collect all the possible data to define the major customers' requirements. After that, the hypothesis on what will work or won't work is made. Here comes the turn to develop the suggestion or to choose the proper one to fit the specific customer, which can be achieved with the help of the selection and matching mechanisms. The personalization of offers, policies, pricing, recommendations, and messages, along with a constant loop of communication, largely contribute to the insurance company's rates.
9. Tools Used Data Science Tools Used in the domain PATTERN RECOGNITION Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. MACHINE LEARNING Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. PREDICTIVE MODELLING Predictive modeling is a commonly used statistical technique to predict future behavior. Predictive modeling solutions are a form of data-mining technology that works by analyzing historical and current data and generating a model to help predict future outcomes.
10. Case Study Machine Learning for Fraud Detection Data Cleansing Transformation Tokenizing Business Rules ML Algorithms Dashboards Detailed Reports Case Management Wipro uses machine learning for fraud detection. It conducted an analysis on ApolloTM- Wipro's Anomaly Detection Platform, which applies a combination of pre-defined rules and predictive machine learning algorithms to identify outliers in data. The steps employed in the process were threefold. Data Handling: Detection Layer Outcomes
11. For this purpose, they divided the insurance dataset into different categories like policy details, claim details, party details, vehicle details, repair details, risk details. For better data exploration, they explored it based on the perspectives of both the insured party and the third party. After doing some Exploratory Data Analysis (EDA) on all the datasets, some key insights were noted. The study concluded that the machine learning models were found to identify most of the fraudulent cases with a low false-positive rate, i.e., with reasonable precision. This enables loss control units to focus on new fraud scenarios and ensure that the models adapt to identify them. Continued...
12. Mindtree Ltd is an Indian multinational information technology services and consulting company headquartered in Bangalore. It also approves the use of big data and data science tools for fraud mitigation in the Insurance market. One way it uses for fraud mitigation is by tracking the history of denied claims. This can also serve as an indicator as consistent claim denial can indicate potential fraud. Again, past claim denial records can be easily scrutinized by an algorithm and can provide a probability of its future denial, such as Claim Negotiation Patterns, Risk Indicators for phone, SSN, Address, etc. The company uses Data Mining techniques like clustering, where high claim frequency clusters are formed around specific addresses and phone numbers. This helps in classifying the claims into various bins. Real-World Example
13. DATA ANALYTICS CREATE NEW CAPABILITIES THAT EMPOWER INSURERS TO OPTIMIZE EVERY FUNCTION IN THE INSURANCE VALUE CHAIN WITH THE HELP OF DATA-DRIVEN DECISION-MAKING. IT CAN ALSO ANALYZE A CUSTOMER’S RISK AND DETERMINE WHICH CLIENT IS TRUSTWORTHY OR MAY GIVE GREAT LOSS. IT CAN ALSO DETECT FRAUD. CUSTOMERS CAN USE DATA ANALYTICS TO KNOW WHICH INSURANCE COMPANY GIVES A MINIMUM PRICE WITH SUITABLE OFFERS. Insurance Industry and importance of data science How data makes insurance market work easy
14. General Concerns Data collected by life insurance companies is scarce compared to the general insurance companies since most of the population own at least one car while don't necessarily have a life insurance policy. In addition, it's much more likely to claim on one's motor insurance policy than a life insurance policy. This has implicitly made machine learning models which rely on a high volume of training data less appealing. There is low penetration of data science techniques in the life insurance industry:
15. Conclusion The use of data science in the insurance industry is rising. Insurance companies invested $3.6 billion in 2021. Companies who invested in big data analytics have seen 30%more efficiency, 40% to 70% cost savings, and a 60%increase in fraud detection rates. Both the customers and companies benefit from these solutions, allowing insurance companies to target their customers more precisely.
16. Resources Xenonstack CCS Website MindTree Wipro Gartner Website