ANALYSIS OF INSURANCE UNDERWRITING USING SOCIAL MEDIA NETWORKING DATA ABSTRACT

ANALYSIS OF INSURANCE UNDERWRITING USING SOCIAL MEDIA NETWORKING DATA Chiag Ku Fa Departmet of Risk Maagemet ad Isurace Shih Che Uiversity, Taipei TAIWAN ABSTRACT To make appropriate uderwritig decisios ad prevet isurace fraud, isurace compaies attempt to collect various sources of data to accurately rate the risk profile of certai classes of policyholders or applicats. I this cotext, uderwriters will ofte cast a broad et i discovery requests, seekig as much documetatio as possible to search for icosistecies i the applicat or policyholder s story or idicatios of potetial fraud. However, these traditioal techiques are labor itesive ad very expesive. Fortuately, the ew olie social etworkig techology may help isurace compaies to improve their uderwritig profits ad select prospective policyholders. However, isurers face obstacles that may impede the speed-to-market of applyig social etworkig data to uderwritig. This is because either regulators or isurers have developed guidelies for the overall use of social data, ad scietific studies have ot determied what types of social medial data are referable. To fill this research gap, the first purpose of this study is to idetify what uderwritig factors uderwriters prefer to search for i social media etworkig. The secod purpose of this paper is to explore the types of social media data that may offer the best isights o uderwritig factors for isurers to make uderwritig decisios. The fidigs may provide iformatio for those who employig social media etworkig data to make uderwritig decisio to attai uderwritig profits, select prospective policyholders, ad provide equity amog policyholders. Keywords: isurace uderwritig, social media etworkig, adverse selectio, isurace fraud. INTRODUCTION Isurace compaies are charged, o the oe had, with takig policyholders premiums to protect the isured from the risk of potetial losses; o the other had, isurace compaies are charged with servig as gatekeepers to prevet policyholders from takig too much from the risk pool. May fuctios ca help isurace compaies to take resposibility for this difficult task. Oe of the most importat fuctios of a isurace compay is the uderwritig process, icludig selectig, classifyig, ad pricig applicats for isurace. The major objective of uderwritig is to determie if a applicat is acceptable for the isurace uder the coditios idicated. Through uderwritig, a isurace compay ca produce a safe ad profitable distributio of busiess. Isurace scholars, practitioers, ad supervisors have a log history of evaluatig isurace applicats kowledge, skills, ad ability directly through a wide variety of sources, icludig applicatios, agets reports, ispectio reports, physical ispectios, physical examiatios, ad attedig physicias reports (Rejda ad McNamara, 2014). Ufortuately, may of these assessmets are at risk of fraudulece ad adverse selectio. Isurace fraud hurts the isurace compaies ad everyoe else because it adds 10% to the cost of the average policy (Nace-Nash, 2011). Progressive Academic Publishig, UK Page 52 www.idpublicatios.org

To make appropriate uderwritig decisios ad prevet isurace fraud, isurace compaies attempt to collect various sources of data to accurately rate the risk profile of certai classes of policyholders or applicats. Traditioally, uderwriters ratig loss exposure or those preseted with potetial adverse selectio or moral risk ted to rely o tools for their ispectio. The aget is told what types of applicats are acceptable, borderlie, or prohibited. Uderwriters also require certai iformatio to decide whether to accept or reject a applicat for isurace. The required iformatio icludes the applicatio, aget s report, ispectio report, physical ispectio, or physical examiatio (Rejarda, 2013). I this cotext, uderwriters will ofte cast a broad et i discovery requests, seekig as much documetatio as possible to search for icosistecies i the applicat or policyholder s story or idicatios of potetial fraud. However, these traditioal techiques are labor itesive ad very expesive (Cowa, 2011). Fortuately, the ew olie social etworkig techology may help isurace compaies to improve their uderwritig profits ad select prospective policyholders. Olie social etworkig websites ad microbloggig services allow users to post ad read text-based messages of up to 140 characters, such as Facebook ad Twitter. There are more tha 554 millio active registered Twitter users ad 1.11 billio people usig Facebook, accordig to reports from Twitter statistics ad Yahoo Fiace i 2012. Almost 72% of all US Iteret users are o ow Facebook, ad 70% of the etire user base is located outside of the US. I other words, Facebook is ow used by oe i every seve people o earth. Every 20 miutes, more tha 2.7 millio photos are uploaded, 2.7 millio messages are set, oe millio liks are shared, ad 10 millio commets are posted o Facebook, based o iformatio provided by WWW.ONLINESCHOOLS.ORG i 2011. Because olie social etworkig websites have both high frequecy use ad wide coverage, employers have arguably bee quicker tha orgaizatioal scietists to realize social media s assessmet potetial (Stoughto ad Thompso, 2013). Numerous studies have examied employers social media usage to select job cadidates ad observe employees (Leviso, 2011; Holdig, 2011). Idividuals have ofte bee cautioed to watch what they post or otherwise divulge via social media because employers may base hirig ad firig decisios i part o what they fid olie. Outside of the workplace, may job applicats use social media for persoal commuicatio that is uiteded for employers (Stoughto ad Thompso, 2013), ofte leavig public traces of their social commuicatio i cyberspace through forums such as blogs, tweets, ad posts o social etworkig web sites such as Facebook (Melidizadeh, 2010). I other words, job applicats olie activity, icludig Facebook activity, tweets, ad olie searches, ca serve as backgroud for employers makig hirig decisios. There is ow aother group that may also be watchig people s social etworkig ad aalyzig the data that they glea from it: isurace compaies. Social media data will pay divideds for isurers i areas such as uderwritig, claims, ad subrogatio (Keealy, 2013). Social media etworks provide a rich source of data that isurers ca use to improve a variety of operatioal processes (Keealy, 2013). However, isurers face obstacles that may impede the speed-to-market of applyig social etworkig data to uderwritig (Ha, 2011). This is because either regulators or isurers have developed guidelies for the overall use of social data, ad scietific studies have ot determied what types of social medial data are referable (Ha, 2011). To fill this research gap, the first purpose of this study is to idetify what uderwritig factors uderwriters prefer to search for i social media etworkig. The secod purpose of this paper is to explore the types of social media data that may offer the best isights o uderwritig factors for isurers to make uderwritig decisios. The fidigs Progressive Academic Publishig, UK Page 53 www.idpublicatios.org

may provide iformatio for those who employig social media etworkig data to make uderwritig decisio to attai uderwritig profits, select prospective policyholders, ad provide equity amog policyholders. LITERATURE REVIEW Iformatio Provided by Social Popular Networkig Sites Facebook, Twitter, Google +, ad LikedI will be the most popular social etworkig sites i the world by 2014 accordig to research coducted by emarketer, a compay located i New York that provides the most complete view of digital marketig available to the world s top brads, agecies, ad media compaies. The followig is descriptio of the type of iformatio available from each site. With 750 millio active users o Facebook, it is almost certai that ay applicats or policyholders will have a Facebook profile. A profile provides Facebook users with a forum for presetig their experieces, iterests, ad thoughts to a selected circle of frieds or to the public at large. Because it provides a messagig feature that allows direct commuicatio betwee Facebook users, the iformatio o Facebook ca be used to develop a picture of a perso s activities before ad after a isurace applicatio (Ramasastry, 2012). A Twitter postig is a text-based post of up to 140 characters. Tweets are essetially text messages posted i real time for commuicatio or discussio with a tweeter s followers. Usually, tweets cotai liks to other sources of iformatio, such as photograph repositories or websites. Moreover, users have direct coversatios with other users through tweets directed at idividuals usig the @ symbol. Searchig Twitter may produce iformatio relevat to whether a isured idividual suffers from sickess or ijuries (Cowa, 2011). Google + is a relatively ew player itroduced to the social etworkig field i Jue 2011. Google + is desiged to itegrate other Google services related to a user s Google profile that cotai may discussio forums. Google + also cotais ew social etworkig features, icludig Circles, Hagouts, Huddles, ad Sparks (Merlios ad Associates, 2011), which may provide a wealth of iformatio to isurace uderwriters about a policyholder s frieds, iterests, group video chats, ad text messages withi various circles. LikedI, with 225 millio members i more tha 200 coutries, is busiess orieted ad is the world s largest professioal etworkig site. LikedI users post resume-type iformatio about their curret employmet, work history, experiece, ad educatioal backgroud. The iformatio posted o LikedI may help isurace uderwriters recogize policyholders real workig situatio, experiece, ad eviromet (Cowa, 2011). The Role of Social Media i Isurace Uderwritig The immediacy of social media data eables isurers to shift uderwritig from a static process that relies upo backward-facig data to a dyamic process that relies upo real-time data (Keealy, 2013). I the ear future, isurers will be icreasigly sesitive to the coectio betwee a isured perso s credit score ad his or her potetial risk for loss. The relatio betwee the activities i which users egage olie ad their riskiess as policyholders is becomig a importat issue (Merlios ad Associates, 2011). The use of social media etworkig cotiues to grow i absolute umbers ad to expad to all age groups, ad ew approaches are usig social media data from olie etworkig sites i Progressive Academic Publishig, UK Page 54 www.idpublicatios.org

operatioal applicatios for uderwritig. Isurers should cosider social etworkig because of who uses it ad what is beig posted (Beattie ad Fitzgerald, 2011). As Ha (2011) predicted, automatically mied data from social etworkig sites may fid their way ito the uderwritig pricig process. Social media data may become a factor i determiig premiums for both persoal ad busiess isurace. Social Media Data Used as Sources of Evidece i Courts of Law i Claim Cases Fraud is a sigificat challege to the isurace busiess. The explosio of ew Iteretbased techology combied with a poor ecoomy has ecouraged uscrupulous idividuals to fid ew ways to commit isurace fraud. I this cotext, isurers ad lawyers have foud ways to take advatage of olie social media to fight fraudulet claims (Griffi, 2011). Scourig Facebook ad other social etworkig pages of policyholders is a commo practice o the claims side of the busiess. May ivestigators report that avigatig a isured idividual s olie social media page is oe of the first thigs they do whe lookig ito potetially fraudulet claims, accordig to a report from Bosto-based research firm Celet i 2011. Olie social media is a goldmie for the discovery of isurace fraud, particularly i the litigatio process (Cowa, 2011). Chastai (2011) stated that social media is obviously a importat factor i isurace fraud ivestigatio. There have bee may situatios i which the public iformatio available through social media has bee beeficial i isurace fraud ivestigatios. Social media etwork data are used extesively as sources of evidece i claim cases i courts of law. Uderwritig will be the ext area (Ha, 2011) if key techiques ca be developed or ehaced, icludig reliable autheticatio methods, improved data extractio tools, ad more advaced aalysis tools (Beattie ad Fitzgerald, 2011). Isurers have ot yet provided guidelies i terms of the overall use of social data, ad these data are ot yet approved for use i the pricig process (Ha, 2011). Importat Uderwritig Factors That Determie a Life Isurace Premium The world of uderwritig is evolvig. Paramedical exams are used more ofte, ad blood tests have become a staple of uderwritig. However, the basic factors cosidered by isurers to make uderwritig decisio are similar to those i the past (Kaltebach, 1995), accordig to may previous studies (e.g., Aiskovich, 1998; James, 2001; Velazquez, 2002; Gerste, 2010). The factors cosidered i makig uderwritig decisios iclude 11 determiats ad ca be framed as i the followig structure (Figure 1). Progressive Academic Publishig, UK Page 55 www.idpublicatios.org

Figure 1. Determiats of Uderwritig Decisio Makig Useful Social Media Data i Uderwritig As users iteract with multiple social etworkig sites, purchase items olie, ad commuicate with others i public forums, they leave behid data about their prefereces, lifestyle, operatios, ad habits. Aother piece of useful iformatio that social media data ca provide is the social graph, which shows how idividuals or compaies are liked together, providig a picture of who is frieds with whom, who follows whom, ad people s frieds of frieds. I additio to idetifyig fraud orgaizatios, these graphs ca give uderwriters further isight ito how a idividual may perform i terms of risk based o the behavior of those to whom he or she is coected (Grisdela, 2011; Ha, 2011). I geeral, useful iformatio ca be searched by uderwriters through social media etworkig sites, icludig idividuals iteractio with multiple social etworkig sites, purchase of items olie, commuicatio with others i public forums, ad social graph. METHODOLOGY The purposes of this study are to idetify what factors uderwriters prefer to search o social media etworks ad to explore what types of social media data may provide the best isights for isurers to judge uderwritig factors. To satisfy the purposes of the research, this study first reviews prior studies to idetify the factors cosidered i uderwritig by isurers ad the types of social media data typically posted o social media etworks. The, this study employs the aalytic hierarchy process (AHP) to idetify the weight of each cosidered factor. To compare the weight of each factor, Progressive Academic Publishig, UK Page 56 www.idpublicatios.org

this study idetifies the factors that are searched most frequetly by uderwriters o social media etworks. Additioally, by coductig AHP, this study explores the appropriate type of social media data that ca be provided to uderwriters i their judgmet of uderwritig factors (Figure 2). Figure 2. Research Procedures As a decisio-makig method that decomposes a complex multicriteria decisio problem ito a hierarchy (Saaty, 1980), AHP is a measuremet theory that prioritizes the hierarchy ad cosistecy of judgmetal data provided by a group of decisio makers. Usig pairwise comparisos of alteratives, AHP icorporates the evaluatios of all decisio makers ito a fial decisio without havig to elicit their utility fuctios o subjective ad objective criteria (Saaty, 1990). The steps of AHP are as follows. Step 1. Establish a hierarchical structure Complex issues ca be addressed effectively by usig a hierarchical structure give the iability of humas to compare more tha seve categories simultaeously. A hierarchy should ot cotai more tha seve elemets. Uder this limited coditio, a ratioal compariso ca be made, ad cosistecy ca be esured (Saaty, 1980). The first hierarchy of a structure is the goal. The fial hierarchy ivolves selectig projects or idetifyig alteratives, ad the middle hierarchy levels appraise certai factors or coditios. The hierarchy structure of this study is show i Figure 3. Progressive Academic Publishig, UK Page 57 www.idpublicatios.org

Figure 3. The Hierarchy Structure The uderwritig factors that uderwriters prefer to search o social media etworks act as evaluatio factors to select the best types of social media data that provide the most isights ito uderwritig factors for isurers to make uderwritig decisios. Step 2. Establishmet of pairwise compariso matrix Based o a elemet of the upper hierarchy, the evaluatio stadard, a pairwise compariso is coducted for each elemet. Although elemets are assumed, (-1)/2 elemets of the pairwise compariso must be derived. Let C 1, C 2,, C deote the set of elemets, whereas a ij represets a quatified judgmet o a pair of elemets C i, C j. The relative importace of two elemets is rated usig a scale with the values 1, 3, 5, 7, ad 9, where 1 deotes equally importat, 3 deotes slightly more importat, 5 deotes strogly more importat, 7 represets demostrably more importat, ad 9 deotes absolutely more importat. This yields a -by- matrix A as follows: 1 2 C 1 1 a12 a1 (1) C 2 1/ a12 1 a 2 Aa ij C 1/ a1 1/ a2 1 C C C The results of the compariso of the elemets are iserted ito the upper triagle of the pairwise compariso matrix A. The lower triagle values are relative positios for the Progressive Academic Publishig, UK Page 58 www.idpublicatios.org

reciprocal values of the upper triagle. Where a ij = 1 ad a ji = 1/a ij, i, j = 1, 2,,, two elemets (C i, C j ) become oe quatizatio value for a importat relative judgmet. I matrix A, aij ca be expressed as a set of umerical weights, W 1, W 2,, W, i which the recorded judgmets must be assiged to the elemets C 1, C 2,, C. If A is a cosistecy matrix, relatios betwee weights W i ad judgmets a ij are simply give by W i, ad judgmets a ij are simply give by W i /W j = a ij (for i, j = 1, 2,, ) ad matrix A as follows: A C C 1 C C C 1 2 w1 w1 w1 w1 w2 w w 1 w 2 2 2 w1 w C w w w A w 1 2 C C C 1 2 w1 w1 w1 C w1 w2 w 1 w 1 w C 2 2 2 w1 w C w w w w 1 2 1 1 (2) Step 3. Compute the eigevalue ad eigevector Matrix A multiplies the elemets weight vector (x) equal to x, i.e., (A- I)x = 0, where x is the eigevalue () of the eigevector. Give that aij deotes the subjective judgmet of decisio makers, the actual value (W i /W j ) has a certai degree of differece. Therefore, Ax =.x caot be established. Saaty (1990) suggested that the largest eigevalue λ max would be. (3) max If A is a cosistecy matrix, eigevector X ca be calculated by (4). j1 max W j aij Wi ( A I) X 0 Step 4. Perform the cosistecy test Saaty (1990) proposed utilizig a cosistecy Idex (CI) ad cosistecy ratio (CR) to verify the cosistecy of the compariso matrix. CI ad RI are defied as follows: CI ( (5) max ) /( 1) 0, (6) CR CI / RI where RI represets the average CI over umerous radom etries of same order reciprocal matrices. If CR 0.1, the estimate is accepted; otherwise, a compariso matrix is solicited util CR 0.1. Step 5. Compute the etire hierarchical weight After various hierarchies ad elemet weights are estimated, the etire hierarchy weight is computed, ultimately eablig decisio makers to select the most appropriate strategy. Step 6. Calculate the whole level weight to select the best alteratives I a alterative hierarchy level, there are five types of social media data. Progressive Academic Publishig, UK Page 59 www.idpublicatios.org

DECISION MODEL APPLICATION AND RFESULTS The estimatio model i this study cosists of two phrases. I the first phrase, uderwritig factors for uderwriters are idetified usig the literature reviewig. The secod phrase, i which the weights of the uderwritig factors, also used as the decisio evaluatio criterio, are calculated ad types of social media data, which may provide the best isights o uderwritig factors for isurers to make uderwritig decisio, is evaluated- both by employig the AHP theory. The secod phrase is described i detail as follows. Step 1: Desigate the AHP participats There are 30 life isurace compaies i Taiwa i 2014. Twety uderwritig maagers of life isurace compaies are selected to comprise the group of experts uder the coditio that each experts has: (a) at least 10 years of professioal experiece i the life isurace sector, ad (b) participated i the decisio-makig process of uderwritig i life isurace compaies. However, oly 11 qualified uderwritig maagers agreed to share their opiio ad aswered the AHP questioaire. Step 2: Establish a hierarchy structure The cosidered factors i uderwritig process that selected from previous literature by this study i the 1 st phrase are also evaluatio factors for explorig a appropriated type of social media data, which comprise several level, icludig the goal hierarchy, criteria hierarchy, sub-criteria hierarchy ad alterative hierarchy (see Figure 1). Step 3: Establish a pairwise compariso matrix Based o the opiio of experts to assig weight values, the geometric mea value is used to calculate comprehesive decisio-makig scores from experts. I doig so, the stadard weight values ca be established to select the most appropriate type of social media data. For istace, the mai criteria are formed as the sample, as show i Table 1. Formula (1) ad (2) are used to calculate the aggregate pairwise compariso matrix. Table 1. Aggregatio of the Pairwise Compariso Matrix for Criteria of Mai Criteria Level 2 Criteria Physical Factors No-Physical Factors Physical Factors 1 0.5 No-Physical Factors 2 1 CI =0.00 ; CR = 0.00 < 0.1 Sep 4: Compute the eigevalue ad eigevector The pairwise compariso matrix of the criteria ad sub-criteria is used to obtai each hierarchical factor weight, i which the eigevector is calculated by formula (3) ad (4). Table 2 summarizes those results. Step 5: Perform the cosistecy test Based o formula (5) ad formula (6), the pairwise compariso matrix of cosistecy is determied for each hierarchy, as show i Table 1. If the results of the six experts i terms of Progressive Academic Publishig, UK Page 60 www.idpublicatios.org

cosistecy ratio ad cosesus of CR are smaller tha 0.1 they coform to priciples of cosistecy. Table 2.Weights of the Criteria ad Sub-criteria Criteria Physical Factors No-Physical Factors Criteria Weight Sub-Criteria Sub- Criteria Weight 0.333 Age Geder 0.090 0.090 0.030 0.030 Smokig 0.176 0.059 Occupatio ad 0.199 0.066 Hobbies Physical Coditio 0.106 0.035 Health History 0.138 0.046 Foreig Travel 0.201 0.067 0.667 Additioal Isurace 0.127 0.085 Fiacial Iformatio 0.373 0.249 Moral Hazard Morale Hazard Step 5: Compute the relative weight of each hierarchy 0.272 0.227 Weights of Overall Levels 0.181 0.152 Table 2 summarizes the results for the relative weight of the elemets for each level. Accordig to this table, the life isurace compay selects a appropriated type of social media data based o the followig rak: Physical Factors (0.333) ad No-Physical Factors (0.667). Evaluatio results of the sub-criteria are summarized as Table 2. Step 6: Calculate the whole level weight to select the most appropriate type of social medial data I alterative hierarchy level, there are four types of social media data may provide the best isights o uderwritig factors for isurers to make uderwritig decisios. The most appropriate type of social media data is selected based o the highest score, i the followig order: Social graph (0.393), Purchase items olie (0.298), Commuicate with others i public forums (0.175), ad Iteract with multiple social etworkig sites (0.134), Cofirmig that Social graph provides the most appropriate type of social media data based o the opiio of the experts from the viewpoit of uderwritig effectiveess, as show i Table 3. Table 3.Life Isurace Compay Applicatio of the AHP Model to Select a Appropriate type of Social Media Data to Improve the Effectiveess of Uderwritig Criteria Criteri Social Purchase Commuicate Iteract with a graph items with others i multiple social Weigh olie public forums etworkig sites ts Physical 0.299 0.169 0.136 Factors 0.396 0.333 No- 0.297 0.178 0.133 Physical 0.392 Factors 0.667 Rak 1 2 3 4 Progressive Academic Publishig, UK Page 61 www.idpublicatios.org

CONCLUSIONS AND RECOMMENDATIONS Accordig to the decisio model applicatio ad results, this study has coclusios as follows: 1. Life isurace uderwriters prefer o-physical factors to physical factors searched o the social media etworkig sites. This is because most of the physical factors, such as age, geder, the occupatio, ad the health history, are declaratios ad required to fill i the applicatio form. Therefore, this kid of physical factor is ot ecessary to be searched by uderwriters o the social media etworkig sites. Moreover, through studyig a body examiatio report, uderwriters ca idetify isured s physical coditio ad the makes the uderwritig decisio. 2. The o-physical factors, such as fiacial iformatio, moral hazard, ad morale hazard, are ot required items to fill i the applicatio form, but very importat for uderwriters to make uderwritig decisio accordigly. I order to improve uderwritig profit, uderwriters hope to search more iformatio related to o-physical factors o the social media etworkig sites. 3. To compare all the uderwritig factors, Fiacial Iformatio, Moral Hazard, ad Morale Hazard are the most three useful factors that uderwriters wat to search o the social media etworkig sites. O the other had, age, geder, ad physical coditio are the factors that seldom eed be idetified by uderwriters through social media etworkig searchig. 4. If uderwriters wat to search the useful iformatio related to Fiacial Iformatio, Moral Hazard, ad Morale Hazard, the social media data type of social graph is the best choice. This is because social graph shows how idividuals are liked together, providig a picture of who is frieds with whom, who follows whom, ad people s frieds of frieds. I other words, social graphs ca give uderwriters further isight ito how a idividual may perform i terms of risk based o the behavior of those to whom he or she is coected. 5. As a result of the growig amout of iformatio that is posted to social media etworkig sites, uderwritig professioals, ad the experts they egage, have discovered that social media ca be a useful ivestigative tool for coductig research ad ucoverig relevat iformatio o uderwritig. Data derived from social media sites ca serve to provide further cofirmatio of the iformatio filled i a isurace applicatio form, thereby assistig uderwritig professioals to develop a proper social media uderwritig guidelie. The impact ad ifluece of social media o uderwritig hadlig, fraud prevetig, ad adverse selectio avoidig caot be igored. REFERENCES Aiskovich, P. P. (1998). Try Idividually Uderwritig Life applicats. Natioal Uderwriter/Life Health fiacial Service, 102(46), 15-23. Beattie, C. ad Fitzgerald, M. (2011). Usig social Data i Claims ad Uderwritig, CELENT. [Assessed 10 th October 2013] Available from World Wide Web: http://www.celet.com/reports/usig-social-data-claims-ad-uderwritig Progressive Academic Publishig, UK Page 62 www.idpublicatios.org

Chastai, P. T. (2011). Iside the Mids, I M. Silvaic (Eds), Isurace Fraud: Prevetio, Ivestigatio, ad Defese. (pp.47-76) Bosto: Aspatore. Cowa, J. L. (2011). Iside the Mids. I M. Silvaic (Eds), Usig Social Media Sites to Research ad Ucover Isurace Fraud (pp.27-45) Bosto: Aspatore. Ha, Y. (2010). I Few Years, social Network Data May Be Used i Uderwritig, Isurace Joural, October, 20-23. Holdig, R. (2011). Ca you be fired for bad-mouthig your boss o Facebook? [Assessed 25 th October 2013] Available from World Wide Web: http://www.time.com/time/atio/article/0,8599,2055927,00.html Gerste, A. D. (2010). Impaired Risk Uderwritig, Life Isurace Sellig, 85(7), 16-18. Griffi, C. E. (2011). Iside the Mids. I M. Silvaic (Eds), Strategies for Usig Social Media i the fight agaist Fraudulet Isurace Claims (pp.99-111) Bosto: Aspatore. Grisdela, M. (2011). Social Media May Ifluece Uderwritig, Isurace Defese Marketig. [Assessed 13rd October 2013] Available from World Wide Web: http://www.isuracedefesemarketig.com/ews/social-media-isurceuderwritig/ James, R. (2001). Critical Illess US Life Isurace: The Uderwritig does differ. Natioal uderwriter/life, Health Fiacial Services, 105(46), 17. Kaltebach, T. M. (1995). World of Uderwritig cotiues to Evolve, Best s Review/Life & Health Isurace Editio, 96(4), 60. Keealy, B. (2013). Social Media Helps Isurers Maage Uderwritig, Claim ad Risks i Real-time. Busiess Isurace. [Assessed 30 th October 2013] Available from World Wide Web: http://www.busiessisurace.com/article/20130630/news07/306309992#. Leviso M. (2011). Social etworks: ew hotbed for hirig discrimiatio claims. [Assessed 30 th October 2013] Available from World Wide Web: www.computerworld.com/s/article/9215907/social_networks_hotbed_for_hirig_di scrimiatio_claims. Melidizadeh S. (2010). Self-presetatio 2.0: arcissism ad self-esteem i Facebook. Cyberchology, Behavior, ad Social Networkig, 13, 357-364. Merlios & Associaes (2011). Social Network Data ad Uderwritig: Comig to a Isurace Compay Near You. Merlios-Actuaries & Cosultats. [Assessed 12 th August 2013] Available from World Wide Web: http://merliosic.com/socialetwork Ramasastry, A. (2012). Will Isurers Beig to Use Social Media Postigs to Calculate Premiums. Cosumer Law. [Assessed 5 th October 2013] Available from World Wide Web: http://verdict.justia.com/2012/01/03/will-isurers-beig-to-use-social-mediapostigs-to-calculate-premiums Rejda, G. E. & McNamara, M. J. (2014). Priciple of Risk Maagemet ad Isurace (12 th Editio). Eglad: Pearso Educatio Limited. Sheryl Nace-Nash (2011). What Isurers Could Do with Your Social Media Score? Daily Fiace. [Assessed 29 th October 2013] Available from World Wide Web: http:// www.dailyfiace.com Stoughto, J. W. & Thompso, L. F. (2013). Big Five Persoality Traits Reflected i Job applicat s social Media Postig. Cyberpsychology, Behavior, ad Social Networkig. X(X), 1-6. Velazquez, D. (2002). The dagers of over Uderwritig. Natioal Uderwriter/Life, Health fiacial Services, 106(7), 12. Progressive Academic Publishig, UK Page 63 www.idpublicatios.org