Data sharing for Fraud Prevention in Medical Insurance
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1 Data sharing for Fraud Prevention in Medical Insurance Nick Mothershaw, Director Identity and Fraud Experian Limited All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Limited. Other product and company names mentioned herein may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian Limited.
2 Types of suspicious / fraudulent activity in the private medical sector Fraud-related losses cost the UK private medical insurance industry an estimated m per annum HICFG Fraudulent activity includes Patients failing to declare pre- existing conditions and claiming for them sometimes days after taking out a policy, or trying to disguise some treatments which are not covered in policies as other qualifying care Brokers understating the risk of corporate customers to win their account, resulting in the insurers grossly under pricing their policies Health practitioners up coding or billing for procedures more complex and costly than the one they actually carried out, or over-treatment where doctors charge for treatments that were not medically necessary Healthcare fraud can be undertaken by Customers / patients Brokers Health Practitioners 2
3 Evolution of Data Sharing Fraud Prevention Intelligence Sharing Known Fraud Sharing Hundreds Thousands Reactive Collaboration Next stage needs Volume Capability Shared platform Data Matching Rules Scoring Network Analysis Historical Policy, Claim and Provider Data Millions Proactive Detection Workflow Case Management Governance Compliance 3
4 Fraud Prevention - Inconsistent Information Same person, Same treatment, Different provider Same contact phone, different person / provider and address New Claim, matched to very recently underwritten policy (last 14 days) Same address, different person / provider and address 4
5 Fraud Prevention - Repeated Information Same provider, same person, within 1 month Same person and address, > 1 recent claim 5
6 Fraud Prevention through Controlled Data Sharing Insurer A Matching Rules 2010 Experian Limited. All rights reserved. Person ID Number Product Type Last Name First Name Income Liabilities Date of Birth Occupation Address (lines) Postal Code Employer Employer Address Phone Number Fax Number Mobile Phone Number Bank Account Number Credit Card Number Only records involved in rules are shared Address Terms of Product Notes on Case Insurer B Only data agreed to be shared is disclosed 6
7 Fraud Prevention through Controlled Data Sharing Insurer A Insurer B Matching Rules Lorem Ipsum Sharing as a result of a match is usually reciprocal 2010 Experian Limited. All rights reserved. 7
8 Experian s role Experian s data sharing initiatives have resulted in fraud savings of approximately 2.59 billion last year alone, supporting a range of industry sectors - 72m 205m 173m 28m 2,100m Asset Finance Credit Cards Personal Loans General Insurance Mortgages 8
9 Progress towards a trial Explore Evolution of Fraud Prevention BUPA International Expacare Experian London Risk HICFG Establish an Industry Steering Group Axa PPP Aviva Stakeholder engagement and compliance Key stakeholders including Information Commissioners Office ABI FSA Led by BUPA International / London Risk Define and agree the scope of the data pilot Project Plan Terms of reference Data agreements Data formats Pilot in progress 9
10 What data are we including in the trial INTERNATIONAL CLAIMS and POLICIES related to those claims Claim Parties: Claimant: Name, Address, DOB Provider: Name, (Address) Policy: Policyholder: Name, Address, DOB NOT Treatment data Approved by the Information Commissioner 10
11 What we will do in the trial Collate and standardise the data from the four participating insurers Process data against Experian data sets, intra-company data, cross company data and HICFG data Score the referrals to obtain prioritised list for review Analyse initial results with a view to further refining the matching or scoring routines Generate an outline business case for the four participating insurers as well as a generic business case for other interested parties 11
12 Data Sharing Pilot summary.. (Once all consents & approvals granted) 1. Data Contribution Policy Application data & Claims Application data From an agreed RETROSPECTIVE period To enable subsequent account behaviour to be observed Defined in ToR document Secure data transfer 2. Data Pilot run Matches / Referrals generated & returned per Contributor 3. Analysis & evaluation of results Investigate performance of referred cases per Contributor 4. Feedback on a 1:1 and at Group level Overall anonymised results can be discussed by agreement at group level Generally acceptable fraud detection stats & estimates Basis for RoI calculations 12
13 Fraud Types and Example Matching Rules Patients failing to declare pre-existing conditions and claiming for them sometimes days after taking out a policy, or trying to disguise some treatments which are not covered in policies as other qualifying care Claims within 14 days of Policy Inception from Same Person and Address Same Person and Address as Previous Claim (different providers) Same Person and Telephone as Previous Claim (different providers) Brokers understating the risk of corporate customers to win their account, resulting in the insurers grossly under pricing their policies Multiple Policies for Same Company across different PMI Providers Same Broker Details as Previous Fraud Same Patient Name and Address Different Employer (different providers) Health practitioners up coding or billing for procedures more complex and costly than the one they actually carried out, or over-treatment where doctors charge for treatments that were not medically necessary 2009 Experian Limited. All rights reserved. 13
14 Expected outputs of the trial. 3 stage prioritised list of high risk cases per insurer covering: Matches to own data Matches with other insurers Matches to Experian data Cases to be analysed for potential fraud and outcomes recorded Value of potential fraud found to be estimated to generate business case 14
15 What the next steps after a successful trial might be Conversations with Experian about implementing the Hunter software into business processes to automate the matching and referral routines Collaboration with participating insurers to construct a data sharing framework with agreement on matching and operational rules Evaluation of the iovation device reputation software into the solution 15
16 Future State How the system would work New Policies New Claims (+ Repeat treatments & episodes) Provider Billing ID & Fraud Management Solution Authentication scores Referral policy rules Authenticate Questions Verify Validate Historical Policy data (From Insurer s own & the wider PMI Community) Experian s Data >47m Voters Roll > 200m CAPS >136m A&A >330m Address >95m Detect + MORE + Insurer s Policy Admin system XML Data Extract Online Results (optional) Fraud risk Scores Historical Claims data (From Insurer s own & the wider PMI Community) Historical Provider Billing data Insurance Hunter National Hunter Referrals & case management (From Insurer s own & the wider PMI Community) CIFAS Underwriters Record frauds & suspicions HICFG Data CUE Investigators Fraud networks Fully hosted solution 16
17 Proactive Fraud Detection through Data Verification Experian Limited All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Limited. Other product and company names mentioned herein may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian Limited.
18 Experian offers identity checks to suit each and every stage of the insurance lifecycle, covering more than 30 countries worldwide How can we run a quick, light touch check on a person s identity as part of our quotation process? How can we be confident the person is who they say they are before we put them on cover? How can we be comfortable we're dealing with the right person before we pay out on a claim? Validate Authenticate Corroborate Public data Public & private data Electronic identity check Validate & verify Electronic identity check Public & private data Validate, verify & corroborate Electronic & interactive identity checks Identity documentation checks Increasing levels of risk demand increasing levels of confidence in customer identity checks 2009 Experian Limited. All rights reserved. 18
19 What is AutoDoc ID? There are 237 countries worldwide, each with various different ID Document Types AutoDoc ID is a service which analyses an image of an ID Document and determines whether the document is valid and authentic A Document can be invalid for a number of reasons, for example: Expired Document Fake or Altered ID Document Automated ID Document checking available 24 hours a day Manual document check available by a document expert trained to border control standards Upload a scanned document from anywhere in the world with an internet connection No specialised hardware or scanners needed Low cost Archived searches available online for 12 Months and offline for a further 5 years. Mobile phone cameras can be used to capture images of ID Documents including photocopies Validate ID documents from 237 countries around the world 2009 Experian Limited. All rights reserved. 19
20 Auto Doc ID Personal details from the document are automatically captured from the MRZ code meaning less manual data entry for your team. Name Gender DOB Nationality Document number, issue & expiry dates 2009 Experian Limited. All rights reserved. 20
21 Non UK Identity Authentication IIQ Global address databases from postal authorities covering 237 countries International Directory Enquiries databases from 24 countries including the whole of Western Europe & North America Credit Reference Agency data from 5 countries inc UK, USA, Germany, Canada and Sweden Global Sanctions & PEP data covering 237 countries Global ID document check service covering 237 countries Shared cross-border fraud alert databases contributed by worldwide ecommerce 2009 merchants Experian Limited. All rights such reserved. as Apple, Dell and Expedia 21
22 Bank Wizard Absolute Verifies the existence of a current account Identifies closed accounts and those where the owner is deceased Matches the name and date of birth provided by the customer to those held by the current account provider Note: Bank Wizard Absolute may only be used where there is intention to collect or make a payment to a customer s current account (Direct Debit, Direct Credit, Faster Payment, CHAPS) Matches the address provided by the customer to that held by the current account provider Uses account open date to provide a confidence score on time with bank 2009 Experian Limited. All rights reserved. 22
23 Experian Identity & Fraud A society where interactions are trusted Experian Limited. All rights reserved. 23
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