• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to footer
  • Articles
  • News
  • Events
  • Advertize
  • Jobs
  • Courses
  • Contact
  • (0)
  • LoginRegister
    • Facebook
    • LinkedIn
    • RSS
      Articles
      News
      Events
      Job Posts
    • Twitter
Datafloq

Datafloq

Data and Technology Insights

  • Categories
    • Big Data
    • Blockchain
    • Cloud
    • Internet Of Things
    • Metaverse
    • Robotics
    • Cybersecurity
    • Startups
    • Strategy
    • Technical
  • Big Data
  • Blockchain
  • Cloud
  • Metaverse
  • Internet Of Things
  • Robotics
  • Cybersecurity
  • Startups
  • Strategy
  • Technical

How Big Data Is Transforming Insurance

Steve Jones / 4 min read.
April 12, 2019
Datafloq AI Score
×

Datafloq AI Score: 69

Datafloq enables anyone to contribute articles, but we value high-quality content. This means that we do not accept SEO link building content, spammy articles, clickbait, articles written by bots and especially not misinformation. Therefore, we have developed an AI, built using multiple built open-source and proprietary tools to instantly define whether an article is written by a human or a bot and determine the level of bias, objectivity, whether it is fact-based or not, sentiment and overall quality.

Articles published on Datafloq need to have a minimum AI score of 60% and we provide this graph to give more detailed information on how we rate this article. Please note that this is a work in progress and if you have any suggestions, feel free to contact us.

floq.to/BvjwE

Data analysts often use the term big data to describe data sets that are too large to store and analyze with traditional methods such as relational database management systems (RDBMS). Big data is greatly affecting the operations of many businesses, including those in the insurance sector. Insurance data can be particularly challenging to use because it comes from many sources such as adjusters’ notes, fraud lists and claims databases.

The large number of claims that insurance companies receive means that adjusters often fail to review all the available information, which can result in a poor decision. The role of data analytics is becoming increasingly important in insurance for tasks such as identifying claims for closer inspection and prioritizing claims. Even a slight improvement in an insurance company‘s loss ratio can mean a great improvement on the bottom line, especially for large insurers.

The following six areas show where big data is having the greatest impact on the insurance sector:

  • Settlement
  • Loss reserve
  • Fraud
  • Activity
  • Litigation
  • Subrogation

Settlement

Settling claims quickly is one of the highest priorities for an insurance company. Most insurers have implemented fast-track processes for claims that have a low risk of being fraudulent. However, this process can result in an insurer paying for claims it shouldn’t. This possibility is especially likely when an insurer receives many claims at once from the same geographic location, which occurs during natural disasters.

Big data can help analyze claims and the claimant’s history to optimize the number of claims eligible for instant payouts. Analytics can also reduce the time needed for adjusters to manually evaluate claims. Additional benefits of processing claims quickly include savings on rental cars while the claimant is waiting for a payout.

Loss Reserve

Loss reserve is an insurer’s liability from future claims. Insurers need to estimate their loss reserve accurately in order to underwrite as many policies as possible without allowing liabilities to exceed assets.

It’s generally impractical for an insurer to predict a claim’s payout and processing time when a claimant first reports it. However, accurate vehicle insurance claims forecasting is particularly important for potentially large claims such as workers’ compensation and liability. Big data analytics can help with estimating loss reserves by comparing a new claim with processed claims similar to it. This software can then reassess the company’s loss reserve each time the claim data is updated, so the insurer always knows how much money it needs to keep on hand to meet future claims.


Interested in what the future will bring? Download our 2023 Technology Trends eBook for free.

Consent

Fraud

About 10 percent of all insurance claims are fraudulent, making it critical for an insurance company to detect as many of them as possible before making a payout. The traditional method approach to detecting fraud is to create a set of fixed rules that determine when to pay a claim. However, criminals are able to determine these rules with relative ease and figure out ways to get around them.

Predictive analytics uses rules as well, but it also uses data mining, modeling and exception handling to make it much more difficult for insurance fraud to succeed. Analytics also detects fraud earlier in the claims cycle than rule-based detection.

Activity

Insurers generally want to assign the most complex claims to their most experienced adjusters. However, it’s often difficult to determine a claim’s complexity when the claimant initially reports it. Insurers often assign a case to a new adjuster at first because the case seems simple, but end up reassigning it to a veteran adjuster when the case turns out to be more complicated than it initially appeared. Reassignments increase the time needed to process a claim, thus reducing customer satisfaction.

Analytics can use data mining and group loss characteristics to score claims on complexity and assign them based on the adjuster’s experience level. It may also be able to adjudicate and settle claims automatically.

Litigation

Insurers spend a significant portion of their loss adjustment expenses in defending claim denials. Analytics can calculate a claim’s propensity for litigation, allowing insurers to assign claims with a greater likelihood for litigation to a senior adjuster. This strategy helps insurers settle claims more quickly and for smaller amounts.

Subrogation

Subrogation cases occur when a third party is responsible for paying off an insurance claim. Detecting these cases early in the claims process reduces loss expenses and increases recovery rates. The large volume of data needed to identify these cases often means they go unidentified, but text searches can locate phrases in this unstructured data that could indicate a subrogation case.

Summary

Insurers are making the analysis of big data part of their regular claims process. This capability is especially useful in insurance due to the many sources of unstructured data in this industry. The potential for reducing the cost of each claim can result in large savings for insurers and their policyholders.

Categories: Big Data
Tags: Big Data, big data analytics, insurance, risks

About Steve Jones

Are you on e-commerce retailer still in the dark about the data available you and how you can use the data to boost sales and find new opportunities? Are you unable to access the right data and consequently unable to correctly measure your marketing ROI? Are you unable to link all the individual customer data together because of lack of resources or the right technology?

If you answered 'yes' to all or some of the questions above, you are in luck. The widespread use of social media apps like Facebook, Twitter, Facebook, and WhatsApp, as well as the rise in social media groups and pages, is an indication of the fact that e-commerce is a huge game changer for businesses.

Primary Sidebar

E-mail Newsletter

Sign up to receive email updates daily and to hear what's going on with us!

Publish
AN Article
Submit
a press release
List
AN Event
Create
A Job Post

Related Articles

The Advantages of IT Staff Augmentation Over Traditional Hiring

May 4, 2023 By Mukesh Ram

The State of Digital Asset Management in 2023

May 3, 2023 By pimcoremkt

Test Data Management – Implementation Challenges and Tools Available

May 1, 2023 By yash.mehta262

Related Jobs

  • Software Engineer | South Yorkshire, GB - February 07, 2023
  • Software Engineer with C# .net Investment House | London, GB - February 07, 2023
  • Senior Java Developer | London, GB - February 07, 2023
  • Software Engineer – Growing Digital Media Company | London, GB - February 07, 2023
  • LBG Returners – Senior Data Analyst | Chester Moor, GB - February 07, 2023
More Jobs

Tags

AI Amazon analysis analytics application Artificial Intelligence BI Big Data business China Cloud Companies company crypto customers Data design development digital engineer engineering environment experience future Google+ government Group health information learning machine learning mobile news public research security services share skills social social media software solutions strategy technology

Related Events

  • 6th Middle East Banking AI & Analytics Summit 2023 | Riyadh, Saudi Arabia - May 10, 2023
  • Data Science Salon NYC: AI & Machine Learning in Finance & Technology | The Theater Center - December 7, 2022
  • Big Data LDN 2023 | Olympia London - September 20, 2023
More events

Related Online Courses

  • Oracle Cloud Data Management Foundations Workshop
  • Data Science at Scale
  • Statistics with Python
More courses

Footer


Datafloq is the one-stop source for big data, blockchain and artificial intelligence. We offer information, insights and opportunities to drive innovation with emerging technologies.

  • Facebook
  • LinkedIn
  • RSS
  • Twitter

Recent

  • 5 Reasons Why Modern Data Integration Gives You a Competitive Advantage
  • 5 Most Common Database Structures for Small Businesses
  • 6 Ways to Reduce IT Costs Through Observability
  • How is Big Data Analytics Used in Business? These 5 Use Cases Share Valuable Insights
  • How Realistic Are Self-Driving Cars?

Search

Tags

AI Amazon analysis analytics application Artificial Intelligence BI Big Data business China Cloud Companies company crypto customers Data design development digital engineer engineering environment experience future Google+ government Group health information learning machine learning mobile news public research security services share skills social social media software solutions strategy technology

Copyright © 2023 Datafloq
HTML Sitemap| Privacy| Terms| Cookies

  • Facebook
  • Twitter
  • LinkedIn
  • WhatsApp

In order to optimize the website and to continuously improve Datafloq, we use cookies. For more information click here.

settings

Dear visitor,
Thank you for visiting Datafloq. If you find our content interesting, please subscribe to our weekly newsletter:

Did you know that you can publish job posts for free on Datafloq? You can start immediately and find the best candidates for free! Click here to get started.

Not Now Subscribe

Thanks for visiting Datafloq
If you enjoyed our content on emerging technologies, why not subscribe to our weekly newsletter to receive the latest news straight into your mailbox?

Subscribe

No thanks

Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Necessary Cookies

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

Marketing cookies

This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.

Keeping this cookie enabled helps us to improve our website.

Please enable Strictly Necessary Cookies first so that we can save your preferences!