MyINS: A CBR e-commerce Application for Insurance Policies

Transcription

1 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, MyINS: A CBR e-commerce Applcaton for Insurance Polces SITI SORAYA ABDUL RAHMAN, AZAH ANIR NORMAN and KOH JOE SOON Faculty of Computer Scence and Informaton Technology Unversty of Malaya Kuala Lumpur MALAYSIA st_soraya@um.edu.my, azahnorman@um.edu.my, joe_spoon@yahoo.com Abstract: - Ths paper presents the desgn and development of an nsurance polces recommender for prospect clent. The man ams of MyINS are twofold. The frst am s to smplfy the process for clent n makng decson base on the nformaton recommended through ths system. The second am s to allow clent to choose the nsurance polcy that s most sutable for them. MyINS models the reasonng process employed by nsurance sale agent n proposng polcy to the clent usng case-based reasonng algorthm (CBR). CBR, generally descrbe the process of solvng current problems based on the proposed soluton from smlar past problems. MyINS works wth a prncple assumpton that dfferent clent wth dfferent background wll have dfferent type of nsurance plan sutable to each one of ther needs. MyINS makes recommendaton of nsurance polces based on personal data and desred coverage of the polcyholder from the smlar past problems. Smlarty assessment and demonstraton of case lbrary are also presented. Key-Words: - Insurance Polcy applcaton, onlne applcaton, e-commerce soluton, Case-Based Reasonng algorthm, Web based 1 Introducton The Internet has offer great busness opportunty for entrepreneurs to expand ther busness nternatonally. Accordng to Elas Awad (2004) [1], e-commerce brngs the unversal access of the Internet to the core busness processes of buyng and sellng goods and servces. It enable busness to acheve goals such as reachng to new markets, creatng new products and servces, buldng customer loyalty, enrchng human captal wth vgorous applcaton and skll developed by the new system, makng use of the exstng and emergng technologes and last but never the least s creatng a compettve advantage whch s essental for all busness n the world. New e-commerce model (n broad categores) shows dfferent types of taxonomy such as busness-to-consumer, busness-to-busness and busness-to-government as dscussed by Tmmers (1998) [2] and Kalakota and Whnston (1996) [3]. These types of models are adopted by the busnesses to sut ther objectves and needs. As dscussed by Peter Lovelock and John Ure, the drector and deputy drector respectvely of the Telecommuncatons Research Project, Unversty of Hong Kong ( [4], explans that successes of the e-commerce are manly due to tme, dsappearance of geographc and economc boundares, dsntermedaton, open source and the catalytc effect e-commerce comprse of, whereby t has change the organsaton work structures. The e- commerce successes also are due to the ncrease of nteractvty and the ablty to create numbers of channels for knowledge dffuson n the workplace. JngTao Yao, n one of her artcles n the Journal of Electronc Commerce Research, VOL. 5, NO. 1, 2004 quoted from Grace (1998) [5], that there s no excepton for nsurance busness that s currently experencng a transformaton wth technology, an ndustry where electronc commerce wll play a sgnfcant role. The development of search engnes and n partcular the ntellgent agents or software agents have attracted more busnesses ncludng the nsurance company to partcpate n e-commerce. Efram Turban (2006) [6] explans that these software agents are used to support tasks such as comparng prces, nterpretng nformaton, montorng actvtes and workng as an assstant, where user can chat and collaborate wth the agents. Ths resulted n prolferaton of hundreds of companes to offer onlne sales and servces ncludng the nsurance companes whch offer webstes that have onlne nsurance sales and servces whch are dstrbuted va the Internet. E- commerce brngs automaton and dgtal-onlne delvery toward the nsurance busness applcaton and the system. Nowadays, clents can drectly

2 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, obtan nsurance quotes and purchase polces faster and easer from nsurance company webste, as the clents now play more actve role n selectng and analyzng the rght polcy that s best for them. The dedcated webstes are desgned to assst prospect clent n makng an ntellgent decson. The only problem s that, such onlne quotes are rarely understood by a prospect clent. In addton, the nsurance polces quoted cover small amount of clent s mportant aspects, example health and lfestyle. In ths paper, the dscusson wll present an approach for selectng nsurance polces that makes use of case-based reasonng algorthm. Followng ths approach, each polcyholder s represented as a case. The new case s solved based on the proposed soluton of smlar past problems (old case) the system has encountered. Accordng to Luger and Stubblefeld (2005) [7], CBR s another powerful strategy experts use to address new problem solvng soluton. Zhaohao, Fnne and Weber (2004) [8], quoted that the goal of CBR s to nfer a soluton for a current problem descrpton or enqury n a specal doman from solutons of a famly of prevously solved problems, the case lbrary. CBR systems are a partcular type of analogcal system whch has an ncreasng number of applcatons n dfferent felds such as n ntellgent Web-based sales servces and Web-base plannng as well as mult-agent systems (Zhaohao, Fnne and Weber, 2004) [8]. In general, there are 4 RE processes nvolved n a CBR cycle (Nakasum, 2003) [9] whch nclude: 1. RETRIEVE the most smlar case or cases 2. REUSE the nformaton and knowledge n that case to solve the problem 3. REVISE the proposed soluton 4. RETAIN the parts of the experence lkely to be useful for future problem solvng. In current tradtonal practce, CBR systems are loaded n the PC and run under the ndvdual operatng system. Ths creates nconvenence to the users and sgnfcantly nfluences the company n terms of creatng, dssemnatng, storng, and retrevng paper-based nformaton and ths leads to hgh operaton cost. The benefts of the tradtonal CBR can be further leveraged by portng the CBR on the Internet. Web-based CBR offers several advantages over the tradtonal CBR n four ways: (1) Accessblty: where the Internet permts unlmted access to web-based CBR system. (2) Interoperablty: n whch the Internet permts a sngle shared web-based CBR system across dfferent platforms. (3) Transferablty: whereby web-based CBR system offers new way of delverng servces and dstrbutng expert advce over the Internet whch acts as sales assstant for E- Commerce. (4). Mantanablty: the web-based CBR system cuts cost of the system mantenance snce there s only a sngle, centralzed case lbrary. Accordng to Watson (2000) [10] as quoted from Hammond et al. (1996), work on web-based CBR systems have been establshed for a few years such as the FAQFnder and FndME systems. 2 MyINS The followng subsecton further explans MyINS archtecture and CBR methodology. 2.1 MyINS Archtecture MyINS s a web-based applcaton. The users can access to the system at anytme and any place, wherever an Internet connecton s avalable. The system s based upon clent/server three-ter archtecture. The three layers nclude clent ter, applcaton ter and data ter. The clent web browser supports MyINS system nterface. The server software n the applcaton ter supports the reasonng mechansm of MyINS system. Ths server-sde reasonng capablty s programmed usng C# scrptng language and developed over Mcrosoft Wndows XP Professonal platform. The server-sde case lbrary n data ter plays a vtal role n the system development. It s used for storng cases whch are needed for nsurance polcy recommendatons. The case lbrary s desgned usng MySQL Server Database. Fgure 1 shows the three-ter web applcaton archtecture for MyINS system. Case Lbrary Applcaton Server E-commerce Server Database Connectvty MyINS System Interface WWW-Browser Data ter Applcaton ter Clent ter Fg. 1: Three-ter MyINS Archtecture Based on the three-ter MyINS archtecture above, communcaton between the clent ter to the data ter are detaled n the descrptons below:

3 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, a) User nteracts wth the web browser (Internet Explorer, Mozlla, or Netscape Navgator) whch contans MyINS system nterface n the form of a HTML page. b) C# scrptng s embedded n the HTML page usng ASP.NET. c) User nputs all nformaton requred by MyINS system n the forms presented n MyINS system nterface seen n the clent browser. Ths s a data collecton process for MyINS system. Ths process resdes n the clent ter. d) The user s web browser sends requests for data to applcaton ter. e) The applcaton ter receves data from the clent ter. f) The Mcrosoft Internet Informaton Servces web server recognzes the requested fle n ASP scrptng format, and nterprets the fle, before respondng to the page request. g) The request s evaluated and passed to the data ter. h) Certan SQL query commands wll connect to the Mcrosoft SQL Server database and request the content that belongs n the web page. ) Mcrosoft SQL Server database responds by sendng the requested content to ASP. j) The C# scrpt stores the content nto one or more C# varables, and then output the content as part of the web page. k) The ASP fnshes up by handng a copy of the HTML t has created to the web server. l) The web server sends the HTML page to the web browser as a plan HTML fle. The page output s provded by the ASP scrptng format. 2.2 MyINS CBR methodology The system developed n ths project has been dvded nto four major modules. Each of the modules performs dfferent tasks. Fgure 2 llustrates MyINS CBR model. NEW CASE Age: 25 Health Class: Good Gender: F BMI: Normal Insurance Type: Protecton Drnker (Y/N): N RETRIEVE NEW CASE: CaseID 10 Age: 28 Health Class: Excellent Gender: F BMI: Normal Insurance Type: Protecton Drnker (Y/N): Y <Suggested Product> Case Lbrary REUSE RETAIN Confrm New Case + Evaluated Insurance Product? If yes, retan straght. If no, repar manually and retan. REVISE NEW CASE: CaseID 10 Age: 28 Health Class: Excellent Gender: F BMI: Normal Insurance Type: Protecton Drnker (Y/N): Y <Suggested Product> Fg. 2: MyINS CBR model

4 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, RETRIEVE module In CBR, the smlarty of cases n the case lbrary to a query case s defned by smlarty functon. MyINS used Nearest Neghbour algorthm to computes the smlarty of the query case to all the cases n the case lbrary based on weght features. Secton 4.1 further explaned Nearest Neghbour algorthm employed n MyINS. Havng computed the smlarty, MyINS retreves cases n the case lbrary wth smlarty scores that exceed a gven threshold for reused module REUSE module The reuse phase n MyINS smply returns the old solutons of the retreved cases unchanged, as the proposed soluton for the query case. Accordng to De Mántaras (2006) et al [11], reuse becomes complex when there are sgnfcant dfferences between the query case and the retreved case. If the case falls under ths scenaro, the retreved soluton mght need to be adapted to account for the dfferences. [11] further states, adaptaton become partcularly relevant when used for constructve problem-solvng tasks such as desgn, confguraton, and plannng. In MyINS, t s lkely that the exact soluton of the retreved case be appled drectly to the target problem. of the reasoner. [12] descrbes three major parts of a case nclude the problem descrptons, the soluton and the outcome. Below are the three major parts of MyINS case The problems descrpton In MyINS, the problem descrpton s the lst of features descrbng polcyholder n the case. MyINS system uses flat feature-value lsts as ts case representaton technque. The system has specfc features where each feature s wth dfferent weghts assgned to each of the feature values. The weghts allow specfc features to have dfferent mportance n terms of ts value. The weghts are suppled by the knowledge engneer and the doman expert. Table 1 shows the samples of the features of a case along wth ts values, remarks and ts weghts. (Note: The weghts assgned to each of the features are accordng to how qualfed or how common the value compared to the others. The total weght value for each feature s ) The soluton The soluton n MyINS s to ntroduce the rght nsurance polcy REVISE module At ths revse phase, the soluton s evaluated. The revse phase provdes a means for the system to learn from falure when a soluton generated s ncorrect or a close match. Thus, requres necessary repar to the case soluton. MyINS reparng soluton s done manually by the doman expert The outcome In MyINS, the outcome s the lst of rght nsurance polcy recommendatons for prospect clents. The lsts of rght nsurance polces are derved by retrevng past cases of polcyholders whch has smlar problem and reusng them to current problem RETAIN module Case s retaned based on the fnal confrmaton from the user. If the soluton s an exact match and accepted by the user, t wll be retaned as a new case n the case lbrary. If the soluton s not accepted, user wll be prompted to ether dscard the soluton proposed or restart the process agan. 3 Case Example Kolodner (1993) as adapted from Bnh V. Nguyen (2002) [12], defned a case as a contextualzed pece of knowledge representng an experence that teaches a lesson fundamental to achevng the goal 3.1 An example case The followng depct an example of a case n MyINS system. Each case conssts of problem descrpton wth approprate product recommendaton useful for future problems. Problem descrpton Age: 28 Gender: F Health: Excellent Polcy: Protecton BMI: Normal Drnker (Y/N): Y Recommendaton: Product 1

5 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, Features Values Remarks Weghts Age Classfcaton: Juvenle: 0<age<=18 Young Adult: 18<age<=40 Mddle Age: 40<age<=55 Old: 55<age<=70 General Insurance knowledge: Young Low premum & hgh return Old Hgh premum & Low return Gender Smoker Health Polcy Body Mass Index (BMI) Drnker Male or Female Ether the applcant s a smoker or non-smoker (wthn last year perod) Excellent, Good, Poor You can choose ether Protecton polces or Health polces Classfcaton: Normal Underweght Overweght Obesty Ether the applcant s a alcoholc or non-alcoholc drnker Female has a lttle more prvleges to certan products. Smoker pays hgher premums. A person wth poor health or has crtcal llness may need to pay a hgher premum or may not qualfy for certan polces. Protecton protects from unexpected events and hardshps and also acts as a value bulder too. Health remburses medcal expenses ncurred due to accdents or llness. It s a way of dentfyng the category of a person s body mass. BMI = weght/(heght*heght) Drnker pays hgher premums. Table 1: MyINS case representaton Juvenle = 0.4 Young Adult = 0.3 Mddle Age = 0.2 Old = 0.1 Female = 0.6 Male = 0.4 Non-smoker = 0.7 Smoker = 0.3 Excellent = 5 Good = 3 Poor = 0.2 Protecton = 0.6 Health = 0.4 Normal = 0.4 Underweght = 0.3 Overweght = 0.2 Obesty = 0.1 Non-drnker = 0.6 Drnker = MyINS Case Lbrary MyINS system uses the nearest neghbour algorthm for retreval of nsurance polcy. The cases are stored usng flat feature-value lsts case representaton technque. 4.1 The reasonng As descrbed n Table 1, each case stored n MyINS system s descrbed as a set of features. The smlarty between an nput and a retreved case stored n case lbrary can be defned as the weghted sum of the smlarty between correspondng cases features. The hgher the smlarty score, the more smlar two cases are. A typcal equaton for smlarty computaton s shown below: n = 1 W sm ( f n = 1 w, f R Where w s the weght for the feature, the hgher the value of w, the more mportant the feature s. sm s the smlarty functon, and f I and f R are the values for feature of the nput case and retreved case respectvely Smlarty for numercal features To compute the smlarty for numercal features, the approach s to start wth a Dstance Functon. Dstance Functon s defned as: ) (1)

6 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, d f ( nput, retreved) = r / range Smlarty =1 d f ( nput, retreved) (2) (3) Case ID 33 shows hgher smlarty value (wth a gven smlarty threshold) than Case ID 12, therefore Product 2 s recommended. Based on the equaton (2) and (3) above, the followng example depcts the dervaton of smlarty metrc for numercal feature (.e. age) between the nput value and the retreved value. Input case (New Problem): Age = 21 Retreved case: Age = 42 Range (Age) = 0 70 years old d = / 70= 0.3 Age( nput, retreved ) Smlarty Age( nput, retreved ) = 1 0.3= 0.7 The smlarty measure s to score 1 for feature wth equal value between nput case and retreved case. 4.2 CBR scenaro example: The case of MyINS Example: Input case s the new target problem and Case ID 12 and 33 are one of the stored cases n the case lbrary. And the smlartes between the features respectvely have been calculated usng the equaton (2) and (3). Input Case Case ID Age: 21 Age: 42 Gender: Female Gender: Female 0.6 Smoker (Y/N): Y 0.8 Health: Excellent Health: Good Polcy: Protecton Polcy: Protecton 0.9 BMI: Underweght BMI: Normal Drnker (Y/N): N Drnker (Y/N): N Recommendaton Product 1 Smlarty ( New, CaseID :12) = (0.3* * + 0.7* * * + 0.3* *) ( ) = 3.1/3.6 = 0.86 Note: Numbers n brackets are the feature s weght values found n the case lbrary. Input Case Case ID Age: 21 Age: 30 Gender: Female Gender: Female Health: Excellent 0.8 Health: Good Polcy: Protecton Polcy: Protecton BMI: Underweght 0.9 BMI: Overweght Drnker (Y/N): N 0.8 Drnker (Y/N): Y Recommendaton Product 2 Smlarty ( New, CaseID : 33) = (0.3* * + 0.7* + 0.5* * + 0.3* *0.8) ( ) = 3.31/3.6 = System Demonstraton MyINS frst step requres acqurng the user personal data (.e. case query) from the case lbrary. The second step nvolves the uses of nearest neghbour algorthm to help MyINS to retreve the most smlar case from the case lbrary. Once data s computed, MyINS wll then dsplay the lst of recommendatons for the rght nsurance polcy for each prospect user. If prospect user s happy and accept the recommendaton, MyINS wll then store the case (computed for the partcular prospect user) to the case lbrary. The system demonstrates that the nteracton and transacton of data are accordng to the three ter archtecture dscussed prevously. All cases computed are requred to be stored n the case lbrary as a carbon copy to ensure that the most smlar cases could be retreved for the usage of other prospect user. Hence t wll offer sold references for the system to compute and recommend the best polces to future prospect users.

7 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, Fgure 4 shows Man form page of MyINS system. Ths page acqures users personal data as a new target problem to solve. Fg. 5: Results from MyINS sesson Fgure 5 depcts the result from MyINS recommendaton sesson whch ncludes the percentage of smlarty score above gven threshold. Fg. 3: Health Class Identfer page Fgure 3 shows the Health Class Identfer page where short questonnares wth 10 smple questons are asked to help fnd out the health class of the user. Ths Health Class Identfer page s a requred page to track the users health class n order to gve better recommendaton of nsurance polcy. 6 Conclusons and Future Work MyINS s a proof-of-concept prototype of applyng CBR algorthm n an nsurance ndustry. The methodology presented n ths paper may serve as a generc framework and can be used for other domans. However, there are many aspects of the prototype ntroduced n ths paper deserve more attenton n future research. Further research may nclude dscusson on tems such as: 1) Introducng more features for a case to mprove the result of system n recommendng the rght nsurance polcy thus better reuse of exstng case. 2) Combnng two or more retreval strategy to lead to hgh qualty to retreved soluton. 3) Buldng case bases n the case lbrary usng a form of smlarty based reasonng suggested by [8], wth the objectve to reduce the case lbrary sze to only most approprate cases. Fg. 4: Man Form page References: [1] Elas M. Awad, Electronc Commerce: From Vson to Fullflment, Prentce Hall, 2004 [2] Tmmers, P, Strateges and models for busness-to-busness tradng, New York: John Wley,1998.

8 Proceedngs of the 5th WSEAS Internatonal Conference on E-ACTIVITIES, Vence, Italy, November 20-22, [3] Kalakota, R. and Whnston, A.B., Fronters of Electronc Commerce, Readng, Mass, Addson-Wesley, [4] Peter Lovelock and John Ure, The New Economy: Internet, Telecommuncatons and Electronc Commerce?, September 2006 URL: omy.pdf [5] JngTao Yao, Ecommerce Adopton of Insurance Companes n New Zealand Journal of Electronc Commerce Research, VOL. 5, NO. 1, 2004, pp [6] Turban Efram et al. Ecommerce 2006: A Manageral Perspectve New Jersey Pearson Prentce Hall, [7] Luger G., Stubblefeld A. W. Artfcal Intellgence: Structures and Strateges for Complex Problem Solvng, Addson-Wesley, [8] Zhaohao S., Fnne G., Weber K, Case Base Buldng wth Smlarty Relatons, Informaton Scences, An Internatonal Journal VOL. 165, 2004, pp September 2006 URL: Img&_magekey=B6V0C-49XPCKB-3-7D&_cd=5643&_user= &_org=searc h&_coverdate=09%2f03%2f2004&_sk= &vew=c&_ald= &_rdoc=1 &wchp=dglbvtzzskzk&md5=222e7e8dd6b4b43656aa86c4850 f948b&e=/sdartcle.pdf [9] Nakasum M, Credt Rsk Management System on e-commerce: Case Base Reasonng approach, ACM Internatonal Conference Proceedng Seres, Proceedngs of the 5th Internatonal Conference on Electronc Commerce; Vol. 50, 2003, pp , September 2006 URL: 2/p438- nakasum.pdf?key1=948062&key2= &coll=&dl=ACM&CFID= &CFT OKEN= [10] Watson, I. A Case-Based Reasonng Applcaton for Engneerng Sales Support usng Introspectve Reasonng. In, Proc. 17th. Natonal Conference on Artfcal Intellgence (AAA!-2000) and the 12th Innovatve Applcatons of Artfcal Intellgence Conference (IAAI-2000), Austn Texas, AAAI Press. ISBN , July 30 - August 3, 2000, pp [11] De Mantaras et al. Retreval, reuse, revson and retenton n case-based reasonng. The Knowledge Engneerng Revew, Cambrdge Unversty Press, Unted Kngdom Vol. 20:3,.2006, pp [12] Bnh. V. Nguyen, AAA: Another Academc Advsor usng Case-Based Reasonng. Oho Unversty. September 2006 URL: pdf