How Fintech Firms Are Leveraging ML In Financial Fraud Detection

HowFintechFirmsAre LeveragingMLInFinancial FraudDetection

Asmoderntechnologygetsalongwiththeingeniousinspirationsofourtech brainiacs,thecasesofsecuritybreaches&fraudskeep arisingwithamore disastrousimpact AsperFinancialTradeCommissiondata,over28 millionindividualswerethevictimsofnancialfraud inprecedingyears, amountingtoanestimated lossof$58billionwhichis70%higherthanthe previousyear Thisinfacthasgivenrisetotheneed ofmachinelearningin nancialfraud detection

More&moreFintechsarefacingidentitythefts&transactionalfraud resultinginincreased demand foranti-thefttools&solutionstomitigatethe potentialdangers Thesedays,theapplicationofMachineLearninginfraud detectionisgainingtractionasapopulartrend amongFintechenthusiasts

MLischangingthewaybanks&nancialinstitutionsstrategizeto strengthentheiranti-theftsystemssuchasdigitalidentityverication setupstoinhibitfraudulentactivitiesincludingfakeidentityattempts,false insuranceclaims unauthorized payments moneylaundering &othercyber threatsthatcancauseamassivenancialloss

willintersectnumeroususecasesinthepresentbanking&Fintechtrends infuture

Also,read someofkeystatisticsofntechindustry2023

Nowlet’srstgiveabriefontheroleofmachinelearninginnancialfraud detection

WhatisMachineLearning&Fraud Detection?

Ofcourse,youmighthaveheard aboutAI&MLtechnologiesifyou’re readingthispieceofinformation

MachineLearningisasubsetofArticialIntelligencewhereasDeep LearningisasubsetofML And theterm‘learning’bringsabigdifferencein humanscanuseMachineLearningtechnologytoshareahugeamountof datawiththecomputermachinethatlearns&interpretsdataforsmart decision-making

WithML,technopreneurshavegraduallymodied theirfraud detection systemswiththepowerofdeep learningthatreplaced thetraditional methodsofrestrictingfraud Theold schooltechniquestotacklesuspicious attempts&theftshavecertainshortcomingsaslisted below:

Falsepositives-Gettingahighernumberoffalsepositivesmeansthat you ’relikelytorestrictgenuinecustomers Forinstance,ifyour traditionalfalsedetectionsoftwareblockstransactionsover$500 acrossablacklisted zone,thenyoumaylosealotofrealcustomers too

Fixed outcomes-Thethreshold ofxed outcomesevenifyouraverage ordervalueincreaseswillremainthesamethatcausingerrorsin trackingfraud behavior Therulesturninvalid &doesnotallowyouto adjusttheoutcomes

Inefciency&complexity-Therule-onlyapproachwithanaccustomed fraud detectionsystemwillexpand asfraud matures Itslowsdown thesystemspeed thatdemandsheavymaintenance&supportfrom thefraud analysisteam

Now,whatisfraud detection?Onlinefraud isacybercrimewherethird-party orhackersareinvolved intamperingwiththedigitalsecuritysystemsto

commitunauthorized fund transactionsfromanyone’saccount Suchcases areverycommoninonlinebankingthatcausesalossoffunds&valuable digitalassetsowned byFintechs&theircustomers And toidentify&control suchfraudulence,nancialinstitutionsusefraud detectionsoftwareto

Whyismachinelearningusedforfraud detection?

1.Real-timefunction

Nevertheless,alongeruserjourneyfromsignupstonalcheckoutscannot guaranteeaconrmed purchase

Machinelearninginfraud detectioninvolvesa teamofanalystsresponding tothousandsofqueriessimultaneously

Alongsideensuringreal-timedecisions,machinelearninganalyzescustomer behavior&trackstheirindividualactivity

2.HighlyFlexible

Anyonlinebusiness,particularlyaneCommerceplatformusesanAPIlike Stripeasapaymentgatewaytoenablehightransaction However,itlags whencongured witharule-onlysystemthatpressurizesruleslibraryas thenumberofpayments

Well,machinelearningbagsapointinthiscase,asdatabecomesbetter withML

Byleveragingmachinelearningforfraud detection,Fintechssolidifythe majordatasetsthatmakeitmoreefcienttotracegenuine&fraudulent users ItsimplymeansthatanML-based modelcandifferentiatebetween suspicious&authenticbehaviorsofcustomerstopredictthepossibilitiesof fraud transactionsinthefuture

3.Efficient&affordable

Machinelearningtechnologyworkssimilartohundredsofanalystskeeping arecord ofhundreds&thousandsofpaymentseverysecond

MachineLearningtakesalllaborsofdataanalysisthatusuallytakes100s offraud analystswhendonemanually Inplaceofhumans,machine learningalgorithmscanperformrepetitive,complex,&time-consuming taskswithinablinkofaneye

4.MorePrecision

Theusageofmachinelearninginfraud detectioniseffectiveintrackingnonintuitivepatterns ML-powered systemsareintegrated todetectabnormal orsuspiciousbehaviorofusers

Inthisway,theroleofmachinelearninginfraud detectionisobserved asan ideatotracksuspiciouscustomers

Suppose,you’vegotaneCommercemarketplacethatconnectssellers& buyersfromvariouscountriesand youareusingmachinelearningto leverageaneuralnetworkthatcanagcertainsuspicioussignalssuchas thenumberofpagesearchesmadebycustomersbeforeplanninganorder, determinecopying&pastingofinformationbyresizingwindows,reporting reviewsbyothercustomers,&more

Howdoesamachinelearningsystemfor frauddetectionwork?

Themechanismofmachinelearningforfraud detectionsolutionsis distinguishablefromthetraditionalrule-based approachtodetecting suspected fraud ThebelowimageillustrateshowtheML-drivenapproach usesAI&replacesrule-based methodsthatrequirehumaninterventionsto

PIN

Overviewofusingdifferentformsof machinelearningforfrauddetection

1.Inputdata

MachineLearningneedsmoredata&it’sbetterforeffectivefraud detection Inthecaseofsupervised ML,thedatamustbecategorized asgood orbad

2.Extractfeatures

Featuresusuallydenecustomerbehavioraswellasfraudulentbehaviors thatarecollectivelyreferred toasfraud signals

Therearevemaincategoriesofgroup features,eachofwhichhas thousandsofdistinctfeaturesaslisted below:

Identity

No ofdigitsintheusers’emailaddress,no ofactivedevicesused by individualcustomers,ageoftheiraccounts,&fraud rateofusers’IP addresses

Orders

No ofordersplaced byusersintherstweek,no offailed transactions, ordervalue,&suspiciousbasketcontents

Paymentmodes

Fraud rateofissuingcompanyorbank,comparisonb/wcustomername& billingname and usageofinternationalcards

Locations

Similaritiesbetweenshippingaddress&billingaddress,shippingcountry similartocustomer’sIIPaddresscountry,and fraud rateofacountry

Network

No ofemails,paymentmodes,contactnumbersshared withinanetwork& ageofthecustomer’snetwork

3.Trainalgorithm

Analgorithmisdescribed asasetofrulestobefollowed whensolving difcultissues

Atrainingsetisscheduled totrainthealgorithmusingtheownhistorical dataofanonlineseller Themachinewillhavemoreexamplestolearnwith morefraud casesinthetrainingset

4.Buildamodel

TheprocessofleveragingMLforfraud detectionwillfurtherindulgein specifyingamodeltodetectfraud

Financialfrauddetectionusingmachine learning–Usecases

ImplementingML-based fraud detectionsetupsisbenecialforFintechs planningtosecuretheiroperationsfromtherisksofmissingfraudulent transactions,humanerrors,&variouscasesofsecuritybreaches

Modernmachinelearningalgorithmsaredesigned toprocessmassive volumesofdata&safeguard itfrompotentialfrauds

Oneoftheprimaryusecasesofmachinelearninginfraud detection appeared whenastartup named complianceaiincorporated anadaptive machinelearningmodelinFintechtoenableresearch&tracknancial regulationsalongwiththeirupdatesallinoneplace

Anotherusecaseofmachinelearningforfraud detectioninFintechis observed around companieslikePayPalthatareusingML-powered systemstoboosttheirfraud detection&riskmanagementstrengths

Paypal’sriskmanagementengineworkswiththecombined poweroflinear, neuralnetworks,and deep learningtechnology Itcanevaluatetherisk levelswitheverycustomerinjustafewmilliseconds

MLempowered avastsegmentofFintechs&nancialinstitutionsto mitigatetheissuesoffakeaccountsthatarealsomonitored bythedigital identityVericationsystems,suspicioustransactions,and paymentfrauds

Furthermore,ittakescareofalleffortsrequired todopredictiveanalytics& dataanalysistomakeorganizationsmoresecureagainstfraud

Secureyourbusinesswithnew-age AI/MLtechnologies

Globalbusinesseshavestarted leveragingdatasciencetokeep nancial fraud atbay Machinelearningisemergingasthemostpromising& favorabletechnologytodrop downthecasesofdigitalfraud thatmay causebiglosseseachyear

However,withtheimplementationofmodernMachinelearningtoolsfor fraud detection,Fintechsprefertodeploysecure&advanced solutionsin theirsystemsthataredifcultforfraudstersorscammerstomanipulate At HieHQ,weco-build industry-specicsolutionsforFintechsaswellas variousbusinessdomainsfromHealthcare&Fitness,eCommerce,gaming, SaaSdevelopment,AI/ML,&othertrendingtechnologies

Ourexpertiseincraftingworld-classdigitalproductsisdrivenbyour startup-centricapproach&ideologyinshapingrobustproductinnovations followed byideating/brainstorming,productplanning,UI/UXdesign, development,Q/Atesting,deployment,maintenance&support

AtHieHQ,weadheretoourclient’ssatisfactoryefforts&gobeyond our limitstomeettheirexpectations

Wedon’tjustcreate,weco-build todeliveraproductthatmakesa difference

FAQs

1.Howcanbanksusemachinelearningforfraud detection?

BanksleverageAI&ML-powered solutionstocollectbigdatainsights& usethemtoconductpredictiveanalyticsofsuspiciousbehaviorofusers The roleofmachinelearningforfraud detectionisnoted wherebanksareusing ittopreventfakeaccounts,paymentfrauds,promotionabuse,account takeover,&anytypeofunauthorized transactions

2.Howmachinelearningimprovesfrauddetectionin corporatebusinesses?

Machinelearningforfraud detectiondealswithfalsepositivesthatusually happenintraditionalmethodsofdetectingfraud Itismoreeffectivethan humansindetectingnon-intuitivepatternstotracksuspiciousactivity

RelatedTopics

#FINANCE #FINANCIAL FRAUD #FINTECH #MACHINE LEARNING