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Ep. 161 – ClaimShare a use case in confidential computing

Ep. 161 – ClaimShare a use case in confidential computing

Released Sunday, 23rd May 2021
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Ep. 161 – ClaimShare a use case in confidential computing

Ep. 161 – ClaimShare a use case in confidential computing

Ep. 161 – ClaimShare a use case in confidential computing

Ep. 161 – ClaimShare a use case in confidential computing

Sunday, 23rd May 2021
Good episode? Give it some love!
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Chaim Finizola is the ClaimShare Director and the head of business development for emerging markets over at IntellectEU. In this podcast we discuss ClaimShare’s confidential computing solution built on top of R3’s Conclave and Corda Enterprise platform for the detection and prevention of “double dipping” fraud in the insurance industry which runs in the several billions of dollars each year.

 What is blockchain?Blockchain is a technology that allows different actors to collaborate with each other without having to trust each other. Having a database in the form of a distributed ledger you can have not only the data decentralised, but also the way the data is handled in a decentralised manner.

Independent of the discussion of centralised versus decentralised, Chaim reminds us what is important is to focus on the business use case and then determine the best approach.

 What is confidential computingConfidential computing allows different actors to perform private computations on specific data sets and process data without other actors being aware of each other and without them being able to see what data is being processed.

The party that is hosting this black box whether it’s a regulator or a network operator they can’t see what is being processed within the black box.

An example of such a black box is the Intel SGX chip which has enclaves where the data can be processed in a fully confidential way without revealing any data to external parties.

Insurblocks recorded a podcast with Richard Brown, CTO at R3 entitled "Confidential computing - introduction to R3's Conclave".“Double Dipping” FraudKPMG has estimated that detected and undetected fraud make up between 5% to 10% of insurers’ total claim payouts. “Double-dipping” fraud a key contributor to fraud, costs the insurance industry several billion dollars each year, which inevitably leads to higher household insurance costs

Double dipping happens when one actor for one loss event goes to multiple insurers to request a same payout. For example, a customer whose had a car accident will go to insurers A, B and C to get a payout from each one of them. This is quite a large problem for insurers which today has been extremely hard to detect. Insurers are usually unaware of this problem as they do not have a way to detect if their customer are insured with another insurer and if a payout has been made on a claim or not.

There has been attempts by insurers to share information via a centralised database but that came up with a number of complexities from a regulatory standpoint and from a GDPR one. In addition, centralised databases run the risk of getting hacked or of leaked sensitive information.

 IntellectEUIntellectEU are the developers of the ClaimShare solution. The firm was founded over 15 years ago as an integration company in the payment sector. They have done over 400 integrations, mainly with SWIFT, in addition to other payment rails. Since 2014 they have been working with DLT and were the first to perform a SWIFT to Ripple integration.

In the blockchain space, IntellectEU has been working first with Ripple, then with Ethereum and in 2016 they were one of the founding members of Hyperledger. Since 2017 they have been working closely with R3

Up to now they have been working with 40 capital market, insurance and telco projects for using blockchain and emerging technologies such as AI, confidential computing and quantum computing.

 ClaimShare

Chaim introduced ClaimShare is the first platform that allows the detection and prevention of double dipping fraud in the insurance industry. ClaimShare uses blockchain technology to allow the sharing of public information to match data and match claims based on colour, location and date, for example. They then use, confidential computing part to match sensitive data of the claims that can be the named user, their address and birthdate.

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