Insurers are increasingly challenged by unexpected business interruptions as they struggle to provide quality customer experience and drive profitability. Adopting the right data-driven technology in the age of digital transformation is crucial to property and casualty (P&C,保险,人寿保险和非人寿保险,以及 纳税人 alike to ensure a streamlined approach to claims processing, 业务优化, 快速的欺诈检测, 风险和损失评估, 和客户保留.


As fraudulent activities increase with time and technology, insurers must keep one-step ahead by deploying new anti-fraud tactics around predictive modelling, 链接分析, 异常报告, 和人工智能. Raw data arriving in PDF or text-based reports from clients and 3rd party systems can promote common schemes like double-payments, 重复索赔提交, 保费和资产转移, 费大量生产, 以及其他类型的欺诈.


  • Automate the extraction and transformation of data from unstructured, siloed formats while easily applying advanced fraud detection techniques such as Benford’s Law or the Gestalt tests.
  • Generate and deploy business rules to spotlight probable fraudulent activities.
  • 为输入之间的复杂关系建模, 输出, 并在大量数据中发现欺诈模式.

No code data transformation for insurance, instantly make data ready.



从监管和政策变化到新的债务, disruptive world events are altering risk assessment and loss analyses overnight, making it more important than ever to streamline underwriting and actuarial processes. Repeatable data transformation and machine learning and artificial intelligence (MLAI) represent a huge opportunity in determining general risk and that of new insurance applicants to ensure a sound investment.

  • Compare disparate policy and claims data quickly and precisely, outside of parsing through Excel or semi-structured data.
  • Compile siloed data sources that indicate and measure liability in a self-service, 没有代码环境, 消除手工, 容易出错的工作流程.
  • Apply predictive analytics to past loss 趋势 to determine proper rates and reserves and overall planning of risk management.


As more businesses use robotic processing automation (RPA) to better operationalize and assess efficiency gaps, 要充分实现它的好处还存在障碍. ope苹果客户端®®君主 complements RPA initiatives by automating repeatable data transformation processes using models that ensure standardized report formats designed to meet end user requirements, 赶出效率低下, 减少成本和精力.

  • 简化数据工作流并创建共享, 受治理的资产,为进一步分析做准备, 比如计算保费和打击欺诈.
  • Implement RPA for claims comparison and auto-adjudication by blending data across claimants to uncover complex patterns, 趋势, 和异常.
  • Connect dozens of applications and databases across geographies and departments to minimize time spent on reconciliation and standardize financial reporting.


Digital transformation has forced carriers and agents to rapidly respond to customer expectations at every part of the insurance process. 从购物到按需服务, 客户现在期望的是闪电般的速度, 个性化的, 和高质量的体验. By leveraging repeatable data collation across all channels and user touchpoints, you can refine outreach initiatives and tailor policies to suit exact needs.

  • Refine customer outreach and tailor policies to provide a 个性化的 experience based on historic customer and demographic data and behavioral 趋势.
  • Anticipate risk of cancellation through AI-backed insights of customer experiences and early identification of signs leading to churn.
  • Test scenarios against changes in controllable and uncontrollable variables to deploy a strategy that reaches the right audience, 用正确的信息, 使用正确的渠道.
  • Anticipate the success of marketing campaigns by automating and repeating processes used in machine learning models.


Guide to Using 数据分析 to Prevent Financial Fraud

Financial fraud takes countless forms and involves many different aspects of business including; insurance and government benefit claims, 零售回报, 信用卡购买, 谎报和瞒报税务信息的, 还有抵押贷款和消费贷款申请.


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作为一个成长中的组织, Cape Regional Health System struggled to bring together information from different databases and reports from patient records, insurance providers and other organizations into a comprehensive business analysis for the management team.