(Redirected from Data Science Application)
- See: Machine Learning Platform.
- "Top 10 Data Science Use Cases in Insurance."
- QUOTE: ...
- Fraud detection: Insurance fraud brings vast financial loss to insurance companies every year. Data science platforms and software made it possible to detect fraudulent activity, suspicious links, and subtle behavior patterns using multiple techniques.
- Price optimization: ... price optimization is closely related to the customers’ price sensitivity. ... price optimization helps to increase the customers’ loyalty in long perspective. Along with this, comes the maximization of profit and income.
- Personalized marketing: ... The personalization of offers, policies, pricing, recommendations, and messages along with a constant loop of communication largely contribute to the rates of the insurance company.
- Customer segmentation: ... target cross-selling policies may be developed and personal services may be tailored for each particular segment. ...
- Lifetime value prediction: ... In addition, the CLV prediction may be useful for the marketing strategy development, as it renders the customers’ insights at your disposal.
- Recommendation engines: ... Recommendation engines are the algorithms applied to provide proper offers for each particular customer. They help to influence the customers’ day to day decisions, choices, and preferences. ... the engines generate more targeted insurance propositions tailored for specific customers. Thus, for example, the insurance company can avoid the ambiguity of the offering car insurance to a customer who is searching for a health insurance proposition.
- Healthcare insurance: ... A wide range of data including insurance claims data, membership and provider data, benefits and medical records, customer and case data, internet data, etc. are gathered, structured, processed and turned into valuable insights for the healthcare insurance business. As a result, the aspects such as costs reduction, quality of care, fraud detection and prevention, and consumers engagement increase may be significantly improved. ...
- Risk assessment: ... prediction of risk and limits it to the minimum in order to cut losses. There are two major types of risk: pure and speculative. ... Risk assessment lies in identifying the risk quantification and the risk reasons.
- Claims prediction: ... Forecasting the upcoming claims helps to charge competitive premiums that are not too high and not too low. It also contributes to the improvement of the pricing models. This helps the insurance company to be one step ahead of its competitors.
- Automating life-event marketing: ... As the main goal of digital marketing is to reach a right person at a right time with a right message, life-event marketing is more about the special occasion in the customers’ lives. ...
- Laura D. Hamilton. (2014). “Six Novel Machine Learning Applications.” In: Forbes.
- Laura D. Hamilton. (2013). “10 Surprising Machine Learning Applications.” In: www.lauradhamilton.com July 30, 2013
- Seal Mobile ID is trying to recognize the user of a mobile device based on accelerometer data (how he holds and moves the phone).
- The Online Privacy Foundation sponsored a competition to see if it's possible to predict whether someone is a psychopath based on his twitter usage. (According to the leaderboard, you kind of can.)
- Fast Iron wants to predict the auction sale price of a piece of heavy equipment — essentially create a Blue Book for bulldozers.
- Similarly, Carvana is building a model to determine if a car bought at auction is a lemon.
- Marinexplore and Cornell University are trying to identify whales in the ocean based on audio recordings so that ships can avoid hitting them.
- Dunnhumby and hack/reduce are trying to predict in advance whether a product launch will be successful or not.
- Oregon State University is looking to determine which bird species is/are on a given audio recording collected in field conditions.
- Amazon is looking for a model to "predict an employee's access needs, given his/her job role." If new employees are starting with inadequate permissions, then it is a costly time suck for them to submit access request paperwork, get supervisor approval, and get granted access by IT. If Amazon is able to create a smarter permissions system with a machine learning model, they can save quite a bit of time and money.
- Benchmark Solutions is trying to predict the trade price of U.S. corporate bonds.
- StackOverflow wants a model that will predict which new questions will be closed.