Digital Technology
Jul 12, 2017

Defining digital in insurance operations

The business of insurance is still about risk and indemnity — the core processes remain in place, even as the nature of risk is changing. As such, digital is evolving, and in some cases even transforming, the efficiency and effectiveness of insurance operations. At Genpact, we have focused our attention on an artificial intelligence (AI) based core platform branded Genpact Cora, which consists of the following digital technologies:

Digital Core

  • Cloud/SaaS and Blockchain - putting stuff on other people's computers over the Internet, offering software as a service and not a “thing" you install. Blockchain can be many things but fundamentally is a secure, distributed ledger (or record of accounts, if you prefer)
  • Mobility and Ambient Computing – includes applications such as claims notification, claims adjusting, and (internal) expense process approvals on mobile devices, the data for which is seemingly everywhere and accessible anytime
  • Robotic Process Automation (RPA) – RPA takes repetitive tasks and intelligently automates them (think of it like super-smart macros)
  • Dynamic Workflow – using Business Process Modeling Notation (BPMN), workflows are digitized by embedding some of the other tech in this list, but are more sophisticated by default thanks to more sophisticated algorithms within the BPMN models

Data Analytics

  • Big Data – it's about more than just a bunch of data; sorting massive amounts of real-time structured and unstructured data requires insurance analysis – e.g., finding fraud by piecing together seemingly disparate pieces of information (computers are really good at this)
  • Advanced Visualization – making all that stuff presentable so people can act on it
  • Internet of Things (IoT) – this broad range of tech is extremely popular now and includes things like sensors in cars to understand driver behavior (at some point, I'd expect this to be embedded in vehicles to understand vehicle behavior)
  • Data Engineering - techniques and technologies that transform data into useful formats for analysis and use by data scientists (sort of like ETL++)

Artificial Intelligence

  • Computational Linguistics (CL) – the more well-known name for a part of CL is natural language processing, which is key for things like chat bots, which are in turn used by RPA
  • Computer Vision – simply put, computers that see, recognize, and categorize the world in real time or through pictures
  • Conversational AI – the next step in CL, where you move beyond the written word to those that are spoken — think of Siri, e.g., "I need insurance for my new car..."
  • Machine Learning and Data Science AI – this is pretty complex tech, but at a high level it means (in part) analyzing patterns, finding anomalies, getting human help, fixing problems, and then ultimately learning without explicit programming

About the author

Frank Neugebauer

Frank Neugebauer

VP, Digital CTO for Insurance

Follow Frank Neugebauer on LinkedIn