Decision CAMP 2013 - sako hidetoshi - blaze consulting japan - Using Business Rules to Address...

1

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


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.


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


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


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


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


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.


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


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.


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.


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.


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


AutoDisaster

Death Disability Hospitalization Outpatient Operation


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.


[email protected]

mailto:[email protected]

Date post:	31-Aug-2014
Category:	Technology
Upload:	decision-camp
View:	158 times
Download:	0 times

Decision CAMP 2013 - sako hidetoshi - blaze consulting japan - Using Business Rules to Address...

Technology