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Blaze Consulting Japan Inc
Jan 01, 2013
Insurance Claim Fraud Detection System「 SMART InsuPector 」
Enabled with SMARTS™
2
Concept of SMART InsuPector
SMART InsuPector A FDS solution with Case-based Analytics for claim personnel. Red flags show the level of risks.
SMART InsuPector is delivered with basic rules: Rules are developed by a claim expert who has more than 20 years of
experiences in claim business and development of FDS. More than 400 rules that are extracted from more than 146 fraud cases Basic templates for performance monitor and early warning system
to make it deployed instantly
SMART InsuPector offers high level of fraud detection: The rule model is designed for inferencing. Inference engine in SMARTS provides the stable and high performance.
SMART InsuPector prevents claim leakages: Decreasing claim losses is increasing profits. It is the framework that will be expanded to leakage prevention.
3
iFDS ( Insurance Fraud Detection System)
iFDS copes with pre-processing and post-process in the claim process. Pre-processing scans the claim transaction and makes decisions for payments. Post-processing is to improve the performance of iFDS.
TransactionData
iFDS(Rule Engine)
Models
iFDSRules
PerformanceMonitor
Early WarningSystem
AnalyticPlatform
LinkAnalysis
iFDSData Mart
Post-ProcessingPre-ProcessingBusiness
System
ClaimSystem
DataWarehouse
External DataInsuranceAssociation
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
4
Coverage by 「 SMART InsuPector 」
TransactionData
iFDS(Rule Engine)
Models
iFDSRules
PerformanceMonitor
Early WarningSystem
AnalyticPlatform
LinkAnalysis
iFDSData Mart
Post-ProcessingPre-ProcessingBusiness
System
ClaimSystem
DataWarehouse
External DataInsuranceAssociation
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
SMART InsuPector
OLAP, Statistics tool, and Link Analysis tool can be selected by the customer
5
iFDS Components
IFDS is composed with Rule Engine and other accompanying components. Ideally, all components are needed. But, in the real world, it is impossible to gather enough data for them. Therefore, Link Analysis was failed. And, the analytic approach did not make good results.
BRMS(Rule Engine)
PerformanceMonitor
Early WarningSystem
AnalyticPlatform
Link Analysis
IFDS is the rule engine that makes decision of possible frauds. The rule engine uses IFDS+ rules and models. IFDS+ rules are rules that checks the possible fraud comparing with previous cases and experiences. Models are developed using predictive analytics and converted into rules.
Performance monitor observes the performance of IFDS. Performance data are used to refine rules and models to upgrade the accuracy of IFDS.
Early warning system monitors KPIs. It is used to find the suspicious patterns, and makes early treatments for them. For example, the number of a certain type of claims is increased in a certain area. Claim experts survey and check the possible fraud.
Analytics platform is the system such as SAS, SPSS, or R. Analytics platform is used to develop models and KPIs for Early Warning System. (In the real world, there are not so enough data for analytics in most cases.)
Link Analysis is the tool to search connections among persons who are included in the claim. For example, family, relatives, friends, alumni, and so on. Ideally, it is a good tool. But, it is hard to find data for analytics, especially because of privacy protection regulations.
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
6
Software for SMART InsuPectorTo deliver IFDS to customer, we need BRMS, DBMS, ETT tool, OLAP and analytics tool. We use SMARTS as BRMS. SMART InsuPector can read and write data on any DBMS including Oracle, mySQL, and others which supports JDBC. SMART InsuPector does not make direct interface to ETT tools and OLAP. So, any ETT tools or OLAP tools that a customer prefers can be used.
For DBMS and ETT tool, we will provide the list of data(factors) and use ones that a customer prefers.
SMART InsuPector will save all data and histories in DB. OLAP will read data from IFDS data mart which SMART InsuPector stored. Any OLAP can be used.
SAS, SPSS, or R can be used.
Link analysis is an independent process. But, there are so many limitation to use it. So, we do not recommend to use.
BRMS Sparkling Logic SMARTS
DBMS Customer’s Choice ETT Tool Customer’s
Choice
OLAP Tool Customer’s Choice
Analytic Tool Customer’s Choice
Link Analysis Tool Customer’s Choice
BRMS(Rule Engine)
PerformanceMonitor
Early WarningSystem
AnalyticPlatform
Link Analysis
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
7
Major Features of SMART InsuPector
SMARTS InsuPector provides red flags for claim evaluation, risk factor management for performance improvement, and performance monitoring to gather information for improvement.
Red FlagRed Flag
Early WarningEarly Warning
FeedbackFeedback
11
22
33
Warning against Fraud possibilitiesto improve claim business performance
Early warning against risk factorsfor faster business reaction
Refinement of rules and performancefor ongoing business improvement
Similarity check through comparison with previousfraud cases
Red Flag warning with reason codes
Management of risk factors Analysis of correlation between risk factors Alert level setting Analytic reports on risk factors
Analytics on rules Simulation of rules and their performance
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
8
Issues and Lessons Learned
Current IFDSs based on statistic analytics fail to satisfy claims personnel. They focused on analyzing data which often does not exist. A new analytic approach based on expert knowledge is increasingly preferred.
Analysts and engineers had no knowledge of the insurance and claim business.Issue #1
Because of the lack of fraud data, statistics and predictive analytics failed to deliver an effective fraud detection (score) model.Issue #2
Reason codes with incorrect scores made business people distrust IFDS and not use the results.
Issue #3
A new analytic approach is required,It should be accepted and handled by business experts.
Business experts must be involved and lead the project.
Business rules were more effective than statistical/predictive analytics.
Output must be refined by claim/insurance experts
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
9
SMART InsuPector Approach
Without enough fraud data, analytics cannot produce the high performance model. Most insurance companies do not have enough fraud data. SMART InsuPector focuses on actual fraud cases, and producing output that can be used in the business.
Hard to improve Performance
Low quality withless flexibility
System for IT,not business
Limit of Analytics
Poor Rules
Engineering-Oriented
Lack of data No data, no analytics
No inferencing (Simple filtering) Little dependency on cases
Not business-oriented Analytics-oriented
Case-Based Analytics
Focusing on frauds/misuses Based on field cases Rules that represent fraud cases
Inferencing Rules
Comparison with fraud cases Rules that can be measured Similarity check
Business-Oriented
レッドフラッグの検査担当に対するサポートに焦点 類似ケースと調査ヒントを表示
Issues2007 20132004 2012
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
SMART InsuPector ApproachAnalytics-Centric Approach
10
BRMS Rule Engine : Sparkling Logic SMARTSTM
DashboardData
Decision LogicDiscussions
Rules in SMARTS InsuPector are managed and executed by SMARTS from Sparkling Logic. Its 4-dimension interface encourages business people to develop and maintain rule by themselves.
11
Sample Template
トレンド分析条件の設定
検索
エクセル
선택 분석항목 확률적 기준 순위 % 기준
自発的申込契約数 % %
月納特約保険料 % %
保険金関連の苦情回数 % %
事故件数 % %
1 年以内に近接事故件数 % %
1 年以内に近接事故 1 年以上遅れ請求 % %
…
√
√
√
AND
AND
AND
AND
AND
OR
0.1 2
0.5 3
0.1 2
0.1 5
事故区分 √ 交通 √ 災害 病気 期間 年 月 ~ 年 月
SMART InsuPector provides basic templates. Based on customer’s requests, they can be customized.
12
請求
審査
調査
免責
審査比免責率
請求 理由現状
請求 審査 調査 免責 審査 免責率 請求対免責率 調査対免責率
1. CI 459 458 400 229 50.1% 49.9% 57.3%2. 災害骨折 789 290 200 111 38.3% 14.1% 55.5%3. 災害手術 1,535 328 165 85 25.8% 5.5% 51.5%4. 災害入院 2,104 527 420 317 60.2% 15.1% 75.5%5. 災害障害 78 78 51 27 34.3% 34.6% 52.9%6. 病気診断 1,880 1,876 642 351 18.7% 18.7% 54.7%7. 病気手術 2,545 832 725 377 45.3% 14.8% 52.0%8. 病気入院 3,456 790 845 376 47.6% 10.9% 44.5%9. 病気死亡 945 645 584 307 47.6% 32.5% 52.6%Total 13,791 5,824 4,032 2,180 37.4% 15.8% 54.1%
分析値 分析年月 ~業務区分 適用段階
業務詳細区分
I. 성과 모니터링 화면 정의
[ 請求 理由 : すべて / 分析値:件 ] 請求理由別の請求 /審査 /調査 /免責の結果の割合の現況
Performance Template Sample: Process Summary by Reason Codes
13
分析年月 ~業務区分 適用段階
業務詳細区分
区分
分析基準詳細区分
I. 성과 모니터링 화면 정의
NO 要因名称 英文名称 C-
PSIC-
SEI
1 累積入院期間 CUM_HSPT_DAY_G 16 22
2 要因 2 F2 4 10
3 要因 3 F3 25 17
4 要因 4 F4 35 100
5 要因 5 F5 14 21
6 要因 6 F6 5 11
7 要因 7 F7 40 34
8 要因 8 F8 100 150
9 要因 9 F9 45 43
020406080
100120
CUM_HSPT_DAY_G F2 F3 F4 F5 F6 F7 F8 F9
C-PSI C-SEI
カテゴリP_
WOE
S_WOE
請求件数
審査件数
調査件数
免責件数
審査 免責率
請求比免責率
調査比免責率
[C00] 10 以下 7 28 155,318
62,127 5,592 2,663 4.3%
[C01] 10 超過 20 以下 44 -11 34,977 11,65
9 1,340 638 5.5%
[C02] 20 超過 30 以下
-102 -43 13,664 4,880 594 272 5.6%
[C03] 30 超過 60 以下 -4 18 16,743 5,581 722 344 6.2%
[C04] 60 超過 90 以下 42 18 6,900 2,300 412 190 8.3%
[C05] 90 超過 120以下 -52 -153 4,644 988 245 108 10.9
%
[C06] 120 超過 132 -62 4,492 1,449 400 154 10.6%
Performance Template Sample: Summary by Factors
14
BRMS(Rule Engine)
Performance Monitor
Early Warning System
Analytics Platform
Link Analysis
Development Strategy of SMART InsuPector
Ideally, analytics and link analysis offer better fraud detection capabilities. Unfortunately, there is few insurance companies that have enough fraud data to be analyzed. In spite of their advantages, they were not successful in the real world.
CoreComponents
of iFDS ExpandedComponents
of iFDS AdvancedComponents
of iFDS
Link Analysis is hard to be used, because of regulations and lack of data.
When a customer has enough data for analytics, predictive models will be added.
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
15
Step-by-step Approach
Predictive analytics and link analysis are hard to be used, because of the lack of data. In the initial phase, we recommend to start with only rules. Once the rule-based system is ready, it is much easier to add analytics and more features.
BRMS(Rule Engine)
Performance Monitor
Early Warning System
Analytics Platform
Link Analysis
Phase I Phase II Phase III Build iFDS with basic
rules that SMART InsuPector provides.
Refine rules with internal data
Develop meta data to expand the coverage
Develop KPIs
Refine rules Expand the
coverage of IFDS Add more cases Add case
management utilities
Evaluate KPIs
Add predictive models to iFDS
Develop KPIs based on analytics
Expand iFDS to leakage prevention
Integrate with claim system
Add more models
Refine models
Add link analysis to iFDS
Check data readiness Develop the strategy
for analytics
Check data readiness Check regulations Check limitations
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
16
Limited Implementation1 month 2~3 months TBD
Full Implementation3 months 6~9 months TBD
標準的な SMART InsuPector 導入プロジェクト工程
In prior to the implementation of SMARTS InsuPector, we need to check the customer’s readiness. In pre-consulting, we check data readiness and develop the implementation strategy. In post-consulting, we check the performance and refine rules.
Pre-Consulting Post-Consulting
Research data and processes that a customer has.
Check data for iFDS Develop the IFDS+
implementation strategy Research KPIs for EWS
Monitor and evaluate the performance of SMART InsuPector
Refine rules and KPIs
SMARTS InsuPectorImplementation
Build data mart. Customize basic rules
provided by SMART InsuPector.
Develop/customize performance monitor and early warning system.
Integrate with business systems.
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
17
Schedule for Full Implementation
In general, implementation takes about 6~12 months, depending on customer’s requirements and environment. With basic rules, it takes about 6 months.
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
RuleDiscovery
RuleAnalysis
RuleDesign
RuleAuthoring
RuleValidation
RuleDeployment
Preparation SystemAnalysis Design Development Test Deployment
M0 M1 M2 M3 M4 M5
Project planning Environment setting
Requirements gathering Survey/research
(Legacy, DW, etc.)
Data mart design Interface design Custom design
Develop Data mart Customization Coding
Prototyping
Integration Test
Documentation Training Technical transfer
Interview Case collection Rule collection
Term mapping Gap analysis
Modeling Repository design
Symbol mapping Writing rules
Validation/verification Performance tuning Business feedback Rule tuning
Rule LifecycleManagement
Legacy integration InsuPector customization
18
Schedule for Limited Implementation
Limited implementation takes 3~3.5 months including small-scale pre-consulting.
Pre-Consulting
W 1 W 2 W 3 W 4 W 5 W 6 W 7 W 8 W 9 W 10 W 11 W 12 W 13 W 14
M 1 M 2 M 3
Data Review(1)
Process Review(1)
Implementation Plan
S/W InstallationDB Design (1)
Build Data Mart (1)
Customize RulesI/F of rules and DBDesign DashboardDevelop Dashboard w/ OLAPIntegration with legacy system (2)
Test & ValidationBeta Test
(1) Support by customer IT personnel is required.(2) Modification of legacy system by customer’s IT personnel is required.
Condition 1: SMART InsuPector will deliver only basic fraud cases and rules.Condition 2: Full support by a customer is required for installation and integration.
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
19
M/M Schedule
SLSBCJ
DB(2)
Dashboard(3)
Legacy Integration(5)
Legacy Modification(6)
M0 M1 M2 M3 M4 M5
Professional Services Rule ModelCustomization
RuleAuthoring Validation Deployment
RequirementAnalysis Design Development Test Deployment
3(1) 3(1) 2 2 1 01 1 1 1 1 1
0 2 2 1 00 2 3(4) 2 11 2 2 1 10 1 1 1 1
1 1 1 1 1 1PM 6
116
5874
47Total(1) Includes business consultants.(2) Customer’s IT engineers are needed.(3) Customer’s IT engineers are required.
(4) Includes 1 UI designer.(5) Customer’s IT engineers are needed.(6) Customer’s IT engineers are required.
20
Classification of Rules
Phase Classification of Rules for fraud detectionAccident Report(Pre-Processing)
o Workers' compensation accident reporto Proximity accidento Theft of falseo Driver substitutiono Auto substitutiono Manipulation of Accident Detailso Related to other car’s ridero Accident records of named insuredo Number of investigations against named
insuredo Accident history of the vehicle drivero Number of investigations against vehicle drivero Collusion of Assailant and Victimo Intentional Accident by the third vehicle
Investigation(Post-Processing)
o Accident history of the victimo Number of investigations against victimo Damage exaggeratedo Confirmation of historyo Workers' compensation accident reporto Victim Substitutiono Intentional Accident by pedestriano Possession unknown accidento Collusion of Assailant and Victimo Induction of intentional accident by the third
vehicleo Driver substitutiono Manipulation of Accident Detailso Collusion of Assailant and Victim by vehicle
drivero Intentional damage to third vehicle by vehicle
driver
21
Non-Life (Auto) Insurance ( Basic 146 Cases 、 >400 Rules )
Leakages
Classification
Violation of Rider of Age Restrictions
Violation of Rider of Allowed Drivers
Uninsured Accident
Paid Transportation
Unlicensed Driving
Supplier Certification
Overall
Groups
Victim Damage Contractor Owner Insured
Frauds
Classification
Driver Substitution
Auto Substitution
Drunken Driving
Intentional Accident
Manipulation of Accident Date
Accident by Unknown Assailant
Groups
Adding Victims
22
Life Insurance ( Basic 300 Cases 、 1,000 Rules )
ClaimTypes
Classification
IllnessDeath
Groups
Disability Hospitalization Outpatient Operation
Examination Treatment Disorder Termination Invalidity
DisasterDeath Disability Hospitalizati
on Outpatient Operation
Examination Treatment Disorder Termination Invalidity
AutoDisaster
Death Disability Hospitalization Outpatient Operation
Examination Treatment Disorder Termination Invalidity
Stakeholders Accused Family InsuranceAgent Hospital Medical
Doctor
23
iFDSs in Korea
Most big insurance companies already deployed iFDS. Now, smaller insurance companies are started to deploy iFDSs. SMART InsuPector has rules that are used by major insurance companies, and redesigned to improve the performance with inferencing capability.
Classified Companies BRMS Analytics 導入年 Remarks
Non-Life
Samsung Life JRules SAS 2012Hyundai Marine JRules SAS 2009
Dongbu FireCleverPath Non-official 2004 1st version
JRules SAS 2011 Newly developed
LIG Not Known No 2010In 2008, prototype system was developed. LIG announced that they developed iFDS internally.
Meritz Under development
Life Samsung Life JRules SAS 2006Hanwha Life JRules SAS 2008Kyobo Life Blaze Advisor SAS 2010
Allianz LifeJRules SAS 2007
InnoRules CSPi(ezVDM) 2013 Under development
Lina Life JRules NO 2012 Analytics is not includedShinhan Life Blaze Advisor Model Builder 2013 Model Builder は分析ツールではないので
他ツール使用か? .Hyundai Life Blaze Advisor Model Builder 2013Heungkuk Life Under development
NH Life Under developmentNo rules uses inferencing, even with inferencing rule engines, because there was no rule engineers who can handle inferencing rules.
Blaze Consulting Japan ,Inc.
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.