Machine Learning yabo重庆时时彩Platform for Insurance

Challenges

Customers are switching to insurance providers who can provide the fastest claims processing,best pricing,and most responsive customer support.Executive,product,and engineering teams at insurance companies can achieve this using Machine Learning,but face several hurdles:

Lack of AI/ML talent
There are not enough people with experience in developing and deploying ML solutions for insurance companies.
Regulations
Strict internal and regulatory policies prohibit deployment of untested,unhardened solutions.
Point solutions
Specialized solutions that utilize AI or ML to solve just one use case do not provide enough flexibility,控制,and transparency.
Infrastructure
Common ML solutions may not be compatible with the hybrid,Java-based,secured environments of insurance companies.

Solution

Insurance providers are leveraging Artificial Intelligence and Machine Learning (AI/ML) to automate painful and costly manual workflows.Skymind是一个平台,yabo重庆时时彩帮助产品和工程团队更快地将这些ML功能交付到企业应用程序中。

Ship ML-powered applications faster

Empower engineering teams to use the language to build ML functionality for Java applications in enterprise environments,using the language and environments they already know.

Reduce operational overhead

消除数据科学和工程团队之间的依赖性和互操作性问题。Get enterprise-grade model reliability,performance,and scaling without maintenance,all on the JVM stack.

Automate business processes based on structured or unstructured data

Build ML-powered capabilities into existing insurance workflows using streaming or stored data from ERP and CRM systems,data lakes,Spark,卡夫卡and elsewhere.Improve model performance over time with quality monitoring,A/B testing,and winner selection.

Make data science work useful

Engineers can integrate and deploy Python-based models developed by data science research teams into JVM environments and applications,or build their own models in a tracked and collaborative environment.

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Perspective: Machine Learning in the Insurance Industry

根据Forbes,claims processing is a notoriously laborious process.Along with the manual data entry of printed forms,the claims process tends to miss cases of fraud which result inover $40 billion dollars in losses per year.大量的索赔使得人类分析师不可能及时对每个案例进行适当的审查。

On top of claims processing,insurers face competitive pressures to improve the customer experience.For example,underwriting is still largely done manually over the phone.Insurance advisory requires a limited supply of human experts to recommend plans that best fit a customer's unique circumstances.Customer acquisition and support require call centers which can have long customer wait times.All of the above create negative customer experiences.

机器学习是保险公司用来自动执行人工任务的强大工具,increase the efficiency of human analysts,and improve the customer experience.

Common applications of Machine Learning for insurance companies include:

  • Claims Fraud: Score claims by the likelihood that it is fraudulent.This enables a fraud analyst to focus effort on blatant cases of fraud.
  • 欺诈环:神经网络能够发现索赔欺诈的来源(例如a specific individual,a specific organization,or clusters of people working together).
  • Document Digitization: A technique called optical character recognition (OCR) is commonly used to digitize handwritten documents.This enables a computer to extract data and automatically process documents without human intervention.
  • Lead Scoring: Rank visitors by the likelihood that they will purchase insurance using patterns in web activity.
  • Churn prediction: Understand and prevent churn by measuring the likelihood that a customer will switch to a different insurance provider.
  • Search: Improve the relevancy of search results to help customers better locate tools,tips,resources,and insurance information.
  • 相关答案:通过推荐常见问题的答案,帮助客户解决常见问题。
  • Customer Support: Better engage customers using a chatbot to provide real-time insurance advice.Help customers resolve common inquiries using a virtual assistant.
  • Recommendation Engines: Recommend insurance plans based on a person's unique history and situation.
  • Image Analysis: Evaluate if evidence (e.g.images or video) have been fraudulently tampered.
  • Risk Assessment: Improve risk assessment and predict reasonable premium rates for policyholders based on historical,demographic,and personal data.

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