Insurance Claim Fraud Detection System

「 SMART InsuPector 」
Enabled with SMARTS™
Jan 01, 2013
Blaze Consulting Japan Inc ...
Concept of SMART InsuPector



SMART InsuPector





SMART InsuPector is delivered with basic rules:








The ...
iFDS ( Insurance Fraud Detection System)


iFDS copes with pre-processing and post-process in the claim process.



Pre-...
Coverage by 「 SMART InsuPector 」
SMART InsuPector
Business
Business
System
System

Pre-Processing
Pre-Processing
iFDS
iFDS...
iFDS Components
IFDS is composed with Rule Engine and other accompanying components. Ideally, all
components are needed. B...
Software for SMART InsuPector
To deliver IFDS to customer, we need BRMS, DBMS, ETT tool, OLAP and analytics tool. We
use S...
Major Features of SMART InsuPector
SMARTS InsuPector provides red flags for claim evaluation, risk factor management for
p...
Issues and Lessons Learned
Current IFDSs based on statistic analytics fail to satisfy claims personnel. They focused on
an...
SMART InsuPector Approach
Without enough fraud data, analytics cannot produce the high performance model. Most
insurance c...
BRMS Rule Engine : Sparkling Logic SMARTSTM
Rules in SMARTS InsuPector are managed and executed by SMARTS from Sparkling L...
Sample Template
SMART InsuPector provides basic templates.
Based on customer’s requests, they can be customized.

トレンド分析条件...
I. 성과 모니터링 Template Sample: Process Summary by Reason Codes
Performance 화면 정의
業務区分

適用段階

業務詳細区分

分析値

~

分析年月

現状
請求 理由

...
I. 성과 모니터링 화면 정의

Performance Template Sample: Summary by Factors

業務区分

適用段階

区分

業務詳細区分

詳細区分

分析基準
請求件
数

審査
件数

調査
件数
...
Development Strategy of SMART InsuPector
Ideally, analytics and link analysis offer better fraud detection capabilities. U...
Step-by-step Approach
Predictive analytics and link analysis are hard to be used, because of the lack of data. In
the init...
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
read...
Schedule for Full Implementation
In general, implementation takes about 6~12 months, depending on customer’s
requirements ...
Schedule for Limited Implementation
Limited implementation takes 3~3.5 months including small-scale pre-consulting.

Condi...
M/M Schedule

M0

M2

M3

M4

M5

11

PM

M1
11

11

11

11

11

Rule Model
Customization

Rule
Authoring

Validation

Dep...
Classification of Rules
Phase

Classification of Rules for fraud detection

Accident Report
(Pre-Processing)

Workers' com...
Non-Life (Auto) Insurance ( Basic 146 Cases 、 >400
Rules )
Classification

Groups

Classification

Groups

Violation of Ri...
Life Insurance ( Basic 300 Cases 、 1,000 Rules )
Classification

Groups
Death

Disability

Hospitalizatio
n

Outpatient

O...
iFDSs in Korea
Most big insurance companies already deployed iFDS. Now, smaller insurance companies
are started to deploy ...
Info@blazeconsulting.co.jp

Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.

Blaze Consulting Japan ,In...
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Decision CAMP 2013 - sako hidetoshi - blaze consulting japan - Using Business Rules to Address Insurance Fraud and Claims Leakage in Asia

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Decision CAMP 2013 - sako hidetoshi - blaze consulting japan - Using Business Rules to Address Insurance Fraud and Claims Leakage in Asia

  1. 1. Insurance Claim Fraud Detection System 「 SMART InsuPector 」 Enabled with SMARTS™ Jan 01, 2013 Blaze Consulting Japan Inc   1
  2. 2. Concept of SMART InsuPector  SMART InsuPector    SMART InsuPector is delivered with basic rules:      The rule model is designed for inferencing. Inference engine in SMARTS provides the stable and high performance. SMART InsuPector prevents claim leakages:   2 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:   A FDS solution with Case-based Analytics for claim personnel. Red flags show the level of risks. Decreasing claim losses is increasing profits. It is the framework that will be expanded to leakage prevention.
  3. 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. Business Business System System Pre-Processing Pre-Processing iFDS iFDS (Rule Engine) (Rule Engine) Claim Claim System System Insurance Insurance Association Association External Data External Data Data Data Warehouse Warehouse Data Mart Data Mart Performance Performance Monitor Monitor Early Warning Early Warning System System iFDS iFDS Rules Rules iFDS iFDS Post-Processing Post-Processing Models Models Transaction Transaction Data Data Analytic Analytic Platform Platform Link Link Analysis Analysis 3 Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  4. 4. Coverage by 「 SMART InsuPector 」 SMART InsuPector Business Business System System Pre-Processing Pre-Processing iFDS iFDS (Rule Engine) (Rule Engine) Claim Claim System System Insurance Insurance Association Association External Data External Data Data Data Warehouse Warehouse Data Mart Data Mart Performance Performance Monitor Monitor Early Warning Early Warning System System iFDS iFDS Rules Rules iFDS iFDS Post-Processing Post-Processing Models Models Transaction Transaction Data Data Analytic Analytic Platform Platform Link Link Analysis Analysis OLAP, Statistics tool, and Link Analysis tool can be selected by the customer 4 Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  5. 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 BRMS (Rule Engine) (Rule Engine)  IFDS isisthe rule engine that makes decision of possible frauds. The rule engine IFDS 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 uses IFDS+ rules and models. IFDS+ rules are rules that checks the possible fraud comparing with previous cases and experiences. Models are fraud comparing with previous cases and experiences. Models are developed using predictive analytics and converted into rules. developed using predictive analytics and converted into rules. Performance Performance Monitor Monitor  Performance monitor observes the performance of IFDS. Performance data Performance monitor observes the performance of IFDS. Performance data are used to refine rules and models to upgrade the accuracy of IFDS. are used to refine rules and models to upgrade the accuracy of IFDS. Early Warning Early Warning System System  Early warning system monitors KPIs. ItItisisused to find the suspicious patterns, Early warning system monitors KPIs. used to find the suspicious patterns, and makes early treatments for them. For example, the number of a certain and makes early treatments for them. For example, the number of a certain type of claims isisincreased in a certain area. Claim experts survey and check type of claims increased in a certain area. Claim experts survey and check the possible fraud. the possible fraud. Analytic Analytic Platform Platform  Analytics platform isisthe system such as SAS, SPSS, or R. Analytics platform isis Analytics platform the system such as SAS, SPSS, or R. Analytics platform used to develop models and KPIs for Early Warning System. (In the real world, 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.) there are not so enough data for analytics in most cases.) Link Analysis Link Analysis 5  Link Analysis is the tool to search connections among persons who are included in 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, ititis aa the claim. For example, family, relatives, friends, alumni, and so on. Ideally, is good tool. But, ititis hard to find data for analytics, especially because of privacy good tool. But, is hard to find data for analytics, especially because of privacy protection regulations. protection regulations. Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  6. 6. Software for SMART InsuPector To 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. BRMS BRMS (Rule Engine) (Rule Engine) Performance Performance Monitor Monitor Early Warning Early Warning System System Analytic Analytic Platform Platform Link Analysis Link Analysis 6  BRMS  Sparkling Logic  BRMS  Sparkling Logic SMARTS SMARTS  DBMS  Customer’s Choice  DBMS  Customer’s Choice  ETT Tool  Customer’s Choice  ETT Tool  Customer’s Choice For DBMS and ETT tool, we will provide For DBMS and ETT tool, we will provide the list of data(factors) and use ones the list of data(factors) and use ones that a customer prefers. that a customer prefers.  OLAP Tool  OLAP Tool  Customer’s Choice  Customer’s Choice SMART InsuPector will save all data and SMART InsuPector will save all data and histories in DB. OLAP will read data from histories in DB. OLAP will read data from IFDS data mart which SMART InsuPector IFDS data mart which SMART InsuPector stored. Any OLAP can be used. stored. Any OLAP can be used.  Analytic Tool  Analytic Tool  Customer’s Choice  Customer’s Choice SAS, SPSS, or RRcan be used. SAS, SPSS, or can be used.  Link Analysis Tool  Link Analysis Tool  Customer’s Choice  Customer’s Choice Link analysis isisan independent process. Link analysis an independent process. But, there are so many limitation to use But, there are so many limitation to use it. So, we do not recommend to use. it. So, we do not recommend to use. Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  7. 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. Warning against Fraud possibilities to improve claim business performance 2 Early Warning Early warning against risk factors for faster business reaction 3 Feedback Similarity check through comparison with previous fraud 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  Red Flag   1 Simulation of rules and their performance Refinement of rules and performance for ongoing business improvement 7 Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  8. 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. Issue #1 Analysts and engineers had no knowledge of the insurance and claim business. Business experts must be involved and lead the project. Issue #2 Because of the lack of fraud data, statistics and predictive analytics failed to deliver an effective fraud detection (score) model. Business rules were more effective than statistical/predictive analytics. Issue #3 Reason codes with incorrect scores made business people distrust IFDS and not use the results. Output must be refined by claim/insurance experts A new analytic approach is required, It should be accepted and handled by business experts. 8 Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  9. 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. Analytics-Centric Approach 2004 2007 Limit of Analytics   Lack of data No data, no analytics Poor Rules   No inferencing (Simple filtering) Little dependency on cases SMART   InsuPector Approach 2013 2012 Issues Case-Based Analytics   Hard to improve Performance    Low quality with less flexibility Inferencing Rules    System for IT, not business  Engineering-Oriented   9 Not business-oriented Analytics-oriented Focusing on frauds/misuses Based on field cases Rules that represent fraud cases 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 Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  10. 10. BRMS Rule Engine : Sparkling Logic SMARTSTM 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. Data Data Discussions Discussions 10 Dashboard Dashboard Decision Logic Decision Logic
  11. 11. Sample Template SMART InsuPector provides basic templates. Based on customer’s requests, they can be customized. トレンド分析条件の設定 事故区分 √ 선택 通 交 √ 災害 期間 病気 분석항목 自発的申込契約数 年 확률적 기준 0.1 ~ 年 순위 % 기준 % AND % 月納特約保険料 月 2 AND % % √ 保険金関連の苦情回数 0.5 % AND 3 % √ 事故件数 0.1 % AND 2 % % AND % OR 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 … 11 0.1 % 5 % 月 検索 エクセル
  12. 12. I. 성과 모니터링 Template Sample: Process Summary by Reason Codes Performance 화면 정의 業務区分 適用段階 業務詳細区分 分析値 ~ 分析年月 現状 請求 理由 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% 13,791 5,824 4,032 2,180 37.4% 15.8% 54.1% Total [ 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 請求 審査 調査 免責 審査比免責率 12
  13. 13. I. 성과 모니터링 화면 정의 Performance Template Sample: Summary by Factors 業務区分 適用段階 区分 業務詳細区分 詳細区分 分析基準 請求件 数 審査 件数 調査 件数 免責 件数 審査 免責 率 28 155,318 62,127 5,592 2,663 44 -11 34,977 11,659 1,340 [C02] 20 超過 30 以 下 -102 -43 13,664 4,880 21 [C03] 30 超過 60 以 下 -4 18 16,743 5 11 42 18 F7 40 34 [C04] 60 超過 90 以 下 要因 8 F8 100 150 [C05] 90 超過 120 以 下 -52 要因 9 F9 45 43 [C06] 120 超過 132 CPSI CSEI N O 要因名称 英文名称 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 6 要因 6 F6 7 要因 7 8 9 120 100 80 60 40 20 0 13 ~ 分析年月 CUM _HSPT_DAY_G F2 F3 F4 F5 F6 C-PSI C-SEI F7 F8 F9 P_ WO E S_WO E 7 [C01] 10 超過 20 以 下 カテゴリ [C00] 10 以下 請求比 免責率 調査比 免責率 4.3%     638 5.5%     594 272 5.6%     5,581 722 344 6.2%     6,900 2,300 412 190 8.3%     -153 4,644 988 245 108 10.9%     -62 4,492 1,449 400 154 10.6%    
  14. 14. 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. BRMS BRMS (Rule Engine) (Rule Engine) Performance Performance Monitor Monitor Core Core Components Components of iFDS of iFDS Early Warning Early Warning System System Analytics Platform Analytics Platform Link Analysis Link Analysis 14 Expanded Expanded Components Components of iFDS of iFDS Advanced Advanced Components Components of iFDS of iFDS When a customer has enough data for analytics, predictive models will be added. Link Analysis is hard to be used, because of regulations and lack of data. Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  15. 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. Phase I I Phase BRMS BRMS (Rule Engine) (Rule Engine)   Performance Performance Monitor Monitor   Early Warning Early Warning System System  Analytics Analytics Platform Platform  Phase IIII Phase Build iFDS with basic rules Build iFDS with basic rules that SMART InsuPector that SMART InsuPector provides. provides. Refine rules with internal Refine rules with internal data data Develop meta data to Develop meta data to expand the coverage expand the coverage Develop KPIs Develop KPIs  Refine rules  Refine rules  Expand the  Expand the coverage of IFDS coverage of IFDS  Add more cases  Add more cases  Add case  Add case management management utilities utilities  Evaluate KPIs  Evaluate KPIs  Check data readiness Develop the strategy for analytics  Add predictive  Add predictive models to iFDS models to iFDS  Develop KPIs based  Develop KPIs based on analytics on analytics  Add more  Add more models models  Refine models  Refine models  Link Analysis Link Analysis 15 Phase III Phase III   Check data readiness Check regulations Check limitations  Expand iFDS Expand iFDS to leakage to leakage prevention prevention Integrate with Integrate with claim system claim system  Add link  Add link analysis to analysis to iFDS iFDS Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  16. 16. 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. SMARTS InsuPector SMARTS InsuPector Implementation Implementation Pre-Consulting Pre-Consulting  Research data and  Research data and processes that a customer processes that a customer has. has.  Check data for iFDS  Check data for iFDS  Develop the IFDS+  Develop the IFDS+ implementation strategy implementation strategy  Research KPIs for EWS  Research KPIs for EWS     Build data mart. Build data mart. Customize basic rules Customize basic rules provided by SMART provided by SMART InsuPector. InsuPector. Develop/customize Develop/customize performance monitor and performance monitor and early warning system. early warning system. Integrate with business Integrate with business systems. systems. Post-Consulting Post-Consulting   Monitor and evaluate the Monitor and evaluate the performance of SMART performance of SMART InsuPector InsuPector Refine rules and KPIs Refine rules and KPIs Full Implementation 3 months 3 months 6~9 months 6~9 months TBD TBD 2~3 months 2~3 months TBD TBD Limited Implementation 11month month 16 Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  17. 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. Legacy integration M0 M1 M2 System Analysis Preparation Design  Requirements gathering  Survey/research (Legacy, DW, etc.) Rule Discovery  Interview  Case collection  Rule collection 17 M3 Rule Analysis  Term mapping  Gap analysis M4 Development  Data mart design  Interface design  Custom design  Project planning  Environment setting InsuPector customization M5 Test  Prototyping  Integration  Test  Documentation  Training  Technical transfer  Develop Data mart  Customization  Coding Rule Design Rule Authoring  Symbol mapping  Writing rules  Modeling  Repository design Deployment Rule Validation     Validation/verification Performance tuning Business feedback Rule tuning Rule Deployment  Rule Lifecycle Management Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  18. 18. Schedule for Limited Implementation Limited implementation takes 3~3.5 months including small-scale pre-consulting. Condition 1: Condition 2: SMART InsuPector will deliver only basic fraud cases and rules. Full support by a customer is required for installation and integration. M1 Pre-Consulting W1 W2 W3 W4 W5 W6 M2 W7 W8 W9 W 10 W 11 M3 W 12 W 13 W 14 Data Review(1)(1) Data Review Process Review(1)(1) Process Review Implementation Plan Implementation Plan S/W Installation S/W Installation DB Design (1)(1) DB Design Build Data Mart (1)(1) Build Data Mart Customize Rules Customize Rules I/F of rules and DB I/F of rules and DB Design Dashboard Design Dashboard Develop Dashboard w/ OLAP Develop Dashboard w/ OLAP Integration with legacy system (2)(2) Integration with legacy system Test && Validation Test Validation Beta Test Beta Test (1) (2) 18 Support by customer IT personnel is required. Modification of legacy system by customer’s IT personnel is required. Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
  19. 19. M/M Schedule M0 M2 M3 M4 M5 11 PM M1 11 11 11 11 11 Rule Model Customization Rule Authoring Validation Deployment Professional Services 66 SLS (1) 33(1) (1) 33(1) 22 22 11 00 11 11 BCJ 11 11 11 11 11 11 66 Requirement Analysis Design DB(2) 00 22 22 11 00 55 Dashboard(3) 00 22 (4) 33(4) 22 11 88 Legacy Integration(5) 11 22 22 11 11 77 Legacy Modification(6) 00 11 11 11 11 44 Development Test Deployment Total (1) (2) (3) 19 Includes business consultants. Customer’s IT engineers are needed. 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. 47 47
  20. 20. Classification of Rules Phase Classification of Rules for fraud detection Accident Report (Pre-Processing) Workers' compensation accident report Proximity accident Theft of false Driver substitution Auto substitution Manipulation of Accident Details Related to other car’s rider Accident records of named insured Number of investigations against named insured Accident history of the vehicle driver Number of investigations against vehicle driver Collusion of Assailant and Victim Intentional Accident by the third vehicle Investigation (Post-Processing) 20 o o o o o o o o o o o o o o o o o o o o o o o o o o o Accident history of the victim Number of investigations against victim Damage exaggerated Confirmation of history Workers' compensation accident report Victim Substitution Intentional Accident by pedestrian Possession unknown accident Collusion of Assailant and Victim Induction of intentional accident by the third vehicle Driver substitution Manipulation of Accident Details Collusion of Assailant and Victim by vehicle driver Intentional damage to third vehicle by vehicle driver
  21. 21. Non-Life (Auto) Insurance ( Basic 146 Cases 、 >400 Rules ) Classification Groups Classification Groups Violation of Rider of Age Restrictions Driver Substitution Violation of Rider of Allowed Drivers Auto Substitution Uninsured Accident Drunken Driving Leakages Paid Transportation Intentional Accident Frauds Unlicensed Driving Manipulation of Accident Date Supplier Certification Accident by Unknown Assailant Adding Victims Overall 21 Victim Damage Contractor Owner Insured
  22. 22. Life Insurance ( Basic 300 Cases 、 1,000 Rules ) Classification Groups Death Disability Hospitalizatio n Outpatient Operation Examination Treatment Disorder Termination Invalidity Death Disability Hospitalizatio n Outpatient Operation Examination Treatment Disorder Termination Invalidity Death Disability Hospitalizatio n Outpatient Operation Examination Treatment Disorder Termination Invalidity Accused Family Insurance Agent Hospital Medical Doctor Illness Claim Types Disaster Auto Disaster Stakeholders 22
  23. 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. BRMS Analytics 導入年 JRules SAS 2012 Hyundai Marine NonLife Companies Samsung Life Classified JRules SAS 2009 CleverPath Non-official 2004 1st version JRules SAS 2011 Newly developed Not Known No 2010 In 2008, prototype system was developed. LIG announced that they developed iFDS internally. Dongbu Fire LIG Under development Meritz Life Remarks Samsung Life JRules SAS 2006 Hanwha Life JRules SAS 2008 Kyobo Life Blaze Advisor SAS 2010 JRules SAS 2007 InnoRules CSPi(ezVDM) 2013 Under development Lina Life JRules NO 2012 Analytics is not included Shinhan Life Blaze Advisor Model Builder 2013 Hyundai 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? . Allianz Life Heungkuk Life 23 Under development NH Life Under development No rules uses inferencing, even with inferencing rule engines, because there was no rule engineers who can handle inferencing rules.
  24. 24. Info@blazeconsulting.co.jp Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved. Blaze Consulting Japan ,Inc.

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