1. Insurance Claim Fraud Detection System
「 SMART InsuPector 」
Enabled with SMARTS™
Jan 01, 2013
Blaze Consulting Japan Inc
1

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Info@blazeconsulting.co.jp
Copyright © 2013 Blaze Consulting Japan, Inc. All rights reserved.
Blaze Consulting Japan ,Inc.