IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-031-50208-8_10.html
   My bibliography  Save this book chapter

AI for Car Damage Detection and Repair Price Estimation in Insurance: Market Research and Novel Solution

In: Rethinking Business for Sustainable Leadership in a VUCA World

Author

Listed:
  • Vladimir Ghita

    (University Politehnica of Bucharest
    Global Resolution Experts S.A.)

  • Denis Iorga

    (University of Bucharest ISDS)

  • Laurentiu-Marian Neagu

    (University Politehnica of Bucharest
    Global Resolution Experts S.A.)

  • Mihai Dascalu

    (University Politehnica of Bucharest
    Global Resolution Experts S.A.)

  • Gheorghe Militaru

    (University Politehnica of Bucharest)

Abstract

The current study focuses on Artificial Intelligence (AI) products for car damage detection and repair price estimation in the insurance industry. The opportunity to introduce such products in the Romanian local market is highlighted following a scoping review of potential benefits and challenges, existing commercial solutions, and existing market research. The lack of market research data concerning the challenges faced by Romanian customers and employees of insurance companies during the insurance claim process is identified as a gap. To address it, the current work presents the results of a pilot survey-based market research that sought to understand the challenges faced by two main Romanian stakeholders of the insurance claim process, namely drivers involved in car accidents (N = 20) and car damage inspectors (N = 15). The result are used to define and advance a novel architecture for an AI system for car damage detection and repair price estimation (InsureAI) that aims to: (a) streamline the communication process between customers and representatives of insurance companies, (b) minimize appointment and traveling challenges related to car damage inspection, (c) reduce the complexity of the repair decision and price estimation related to the insurance claim procedure, (d) address the challenges of customers of car insurance companies in organizing and filing in the necessary information for starting a new claims file, as well as (e) enhance the overall process transparency. Additionally, we detail the envisioned user flow and the user interface prototype.

Suggested Citation

  • Vladimir Ghita & Denis Iorga & Laurentiu-Marian Neagu & Mihai Dascalu & Gheorghe Militaru, 2024. "AI for Car Damage Detection and Repair Price Estimation in Insurance: Market Research and Novel Solution," Springer Proceedings in Business and Economics, in: Mihail Busu (ed.), Rethinking Business for Sustainable Leadership in a VUCA World, pages 167-179, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-50208-8_10
    DOI: 10.1007/978-3-031-50208-8_10
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-031-50208-8_10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.