Credit risk scoring for unsecured loans


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Real Time Credit Score generation model for Unsecured Consumer durable loans at POS terminals

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Credit risk scoring for unsecured loans

  1. 1. © 2013 Valiance SolutionsCredit Risk Score – Loan ApprovalUnsecured Consumer Durable LendingAnalytics & Technology CompanyAnalyticsConsultingTechnologyConsultingBusinessIntelligence
  2. 2. © 2013 Valiance SolutionsCase Study on a Credit Risk Scoring forProminent Indian Finance company
  3. 3. © 2013 Valiance SolutionsObjective : Develop Credit Risk framework for POS loan approvalsCase Study: Credit Risk Scoring for Unsecured Loans To identify customers who are more likely to commit fraud/default onconsumer durable loans. To streamline loan approval process according to customer risk profiles.Quantitative Analysis of Credit Risk Identify attributes of customers who are most likely to commit fraud? What are patterns in customer default across cities/income/professionsegments? What is the probability of a customer to default?
  4. 4. © 2013 Valiance SolutionsCurrent Business ModelNearly 2/3rd of the retail portfolio is constituted by the Consumer Durable lending at the Point of SaleCustomer walks-in tooutlet for purchasingvarious consumerdurable itemsClient representativeat store makesproposition forconverting invoiceamount toinstallmentsLow profitabilityproducts with nointerestCustomer details arecollected andaccept/reject decisionis madeNormal Model - Approvalsand disbursals are madewithin 2 days after rigorousverificationsInstant mode -Approvals are madeinstantly within 30min
  5. 5. © 2013 Valiance SolutionsApprovals and disbursalsare made within 2 daysApprovals are madeinstantly within 30minRigorous fieldverifications are carriedoutSample set of casesare send to FraudControl unit forverificationVarious referencepoints are availablesuch as photograph,bank details etc forcapturing fraudIdentified fraudcases are sent to fieldservices for recoveryIdentified instancesof fraudClean casesPresent Fraud Management FrameworkFraud Management mechanism works differently depending on the mode of lending
  6. 6. © 2013 Valiance SolutionsNeed for Instant Mode of Lending• Makes the decision easier for the customer and leads to higherconversions• Greater customer delight at Point of Sale• Larger volumes• Reduced paperwork and scrutiny• Lower operational costs• Helps achieve scale especially during peak seasons like festivals• Empower the dealers for their good delinquency performance overtime with higher ratings
  7. 7. © 2013 Valiance SolutionsBusiness Challenge• High rate of business growth with almost 40% incremental addition to the bookper annum• Although the fraud incidence rate is low at about 0.7% it translates into Value atRisk of almost 3 million USD per annum• The recovery rate from fraudulent cases is extremely low at around 10-20%even after identification• Sampling is only possible for about 10% cases and hence the remaining onescould be going unidentified till payment default starts• The incidence of fraud goes up drastically especially during peak seasons likefestivals, vacations etc as loopholes in the system get exploited• Dealers with low past delinquencies get superior ratings and hence liberty tofollow express approval which would enhance the fraud risk
  8. 8. © 2013 Valiance SolutionsNeed for better solution• The current identification of fraud happens after the loan has beendisbursed and the customer takes delivery• There is absolutely no mechanism to assist the service representative atthe store to flag off a particular case as potential fraud• Past fraud indicators and market fraud ring trends are being used asbenchmarks but only from enabling physical verification and supportingeyeballing• Any framework/tool should be able to quantify the potential risk andhence move the case from ‘Instant mode’ to ‘Normal mode’ as the costof outright reject is very high
  9. 9. © 2013 Valiance SolutionsOur recommended approach – Analytical solutionText MiningHypothesis buildingData cleansingBuild a Java basedalgorithmEnsure compatibilitywith client’s Sales CRMsystemHost the algorithmon the client’s systemCross-validate thescores generated by thesystemConducting field visits tounderstand typical trends infraud patternsProfiling patterns Algorithm for fraud predictionRoll-out the algorithmon the live systemContinuous monitoringof through the doorpopulation for any changesin patternsStrategyroll-out andtestingImplementationframeworkFraudLikelihoodModelDevelopmentof technologysolution
  10. 10. © 2013 Valiance SolutionsMethodology• Created reference master database of some of the key product categories, brand categories andsurrogates• Used advanced text mining such as indexing, Soundex etc to identify patterns in the data• Overlaid the algorithms on the master data to enable tagging of various combinations intofinite categories• Laid out a recommendation to the client for moving these fields into ‘Fixed options’ menu toensure better accuracy which was implementedText MiningBackground• Based on business survey and understanding we had identified certain key attributes whichwere critical predictors of fraud patterns• Some of these included the type of product purchased, brand of purchase, surrogates etc.• For e.g. during summer seasons Air conditioners in general as a product and especiallycertain brands see heavy traffic and hence consequently higher frauds• This data was captured very poorly as the Sales System allowed entry of data in a free textformat
  11. 11. © 2013 Valiance SolutionsAll accountsourcedCharacteristicsCharacteristicsScoring modelLikelihood toDefaultScoring Algorithmfor CalculationPropensity todefaultLoan applicationcoming for renewalat POSLow RiskHigh RiskMedium RiskCustomers identified as notfraudFraud Likelihood modelCustomer s identified asFraud
  12. 12. © 2013 Valiance SolutionsDevelopment of technology solutionBackground• In most scenarios model scores need to be delivered over a defined frequency typically everymonth in the form of batch files as list of customers for targeting• However, in case of On-boarding Fraud the decisioning engine needs to identify risk at thePOS itself and that too on Through-the-door population• Hence, an algorithm needed to be developed which would sit on the Client Sales system andbased on inputs received output a scoreMethodology• Developed a Java based algorithm which would capture all the input parameters• The algorithm would then compute the fraud score and output the same onto the Client’sSales system• The algorithm had to be fine-tuned to be compatible with the client’s technology
  13. 13. © 2013 Valiance SolutionsCustomer walks-in to outlet forpurchasingproductsProposal toconvert invoiceamount to EMI’sCustomer Detailsfed into the systemThe algorithmdeveloped willreturn fraud scorebased on inputsFeedback ProcessResponsetrackingFeedbackLoop• Low profitabilityproducts with nointerestImplementation Framework1 2 3 4Instant mode -Approvals aremade instantlywithin 30 minMedium RiskNormal Mode-Approvals areafter rigorousverification
  14. 14. © 2013 Valiance SolutionsStrategy roll-out and testing• As Fraud is a very dynamic phenomenon, the patterns of fraud are subject to change veryrapidly• Fraudsters are looking for newer ways of creating loopholes in the system• The key is responsiveness to change• It is a very integral part of a successful fraud management program that we keep looking forthe emerging fraudulent trends in the market• In addition every month the frauds among the Through-the-door population are analyzed toassess the effectiveness of the model
  15. 15. © 2013 Valiance SolutionsKey patterns behind Fraud• Customers employed in specific industries.• High ticket sizes• Rented Accommodation• Lower down payments• Negative Regions• Time of the day and day of the week• Product categories season wise
  16. 16. © 2013 Valiance SolutionsROI of Modeling ExerciseLapse Model led to SuperiorCustomer Retention thusimproved the Bottom Line• Substantial decrease in loandisbursement to fraudulent casesat Point of Sale• Almost 10% of the originationsare referred to ‘Normal process’in which the fraud incidence is ashigh as 5% which translates intoa gross saving of almost 1.5million USD i.e. 50% of the VaR• Substantial decrease in the thirdparty cost of loan amountrecovery from the fraudulentcases.
  17. 17. © 2013 Valiance SolutionsWe are ready to do a Proof of Conceptto answer all the questions
  18. 18. © 2013 Valiance SolutionsBuild a Proof of Concept Model Data Requirement dependson business problem that isbeing addressed. Generallybelow data is needed. Customer Profile &Demography Purchase Details/ TransactionData Customer Feedback/CallCentre Data Predictive Model generatingcall to action. Trends and factors affectingbusiness problem. Tracking Reports Model Performance on TestDataCost of Ownership Data Required Deliverables Zero Model can hold its validityfrom 1 to 1.5 years dependingupon changes in underlyingpopulationAnalytics Proof of Concept Road MapExecutionModelOffsite /OnsiteExecutionTimeframe6-8 weeks
  19. 19. © 2013 Valiance SolutionsWhy Us? Strong team with experience in successful implementation of analytical solutions acrossBFSI sector. Expertise in BFSI domain in Customer Retention & Marketing Analytics with successfulROI driven analytics initiatives. In depth knowledge of advanced statistical tools and modeling techniques to impact visiblebusiness outcome. Successfully implemented 50+ predictive models for clients across domains. Being a startup we are strongly determined to make a visible impact in functions weoperate. We can help you quickly set up decision processes and improve agility and responsiveness.
  20. 20. © 2013 Valiance SolutionsExecutive TeamVikas Kamra (Co-Founder & CEO)• Vikas has six years of extensive experience in business and technology consulting from Fortune 100companies to startups globally• He currently serves as CEO of Valiance Solutions and works with firms to decide rightsolution/framework to address business problems in functional areas of customer acquisition,retention, marketing or risk using both technology and advanced analytics.• Vikas did his graduation from IIT Delhi and is CFA Level 2 qualified. Prior to co-founding his own firm in2011, Vikas has worked with Merrill Lynch, Bank of America, Jefferies Investment Bank on severalonsite and offshore engagements.Ankit Goel (Co-Founder & CTO)• Ankit Goel serves as CTO of Valiance Solutions and is responsible for execution and delivery oftechnology initiatives. He takes keen interest in fields cloud computing and big data technologies andhas worked on several projects in these areas.• Ankit did his graduation from IIT Kharagpur and has 8 years of experience working on technologyprojects with investment banks globally.Shailendra (Co-Founder & Head of Decision Sciences)• Shailendra head analytics function with Valiance and possesses keen business and analytical insight tosolve business problems. He has worked on several advanced level analytics initiatives with LifeInsurance companies, Mutual funds, Credit Card Companies, NBFC’s in India in Credit Risk, Marketingand Customer Analytics.• Shailendra has 5 Years of experience working with Fortune 100 Financial companies across EMEA, USand Indian Subcontinent region.• Shailendra did his graduation from DMET/MERI and holds several patents to his name.
  21. 21. © 2013 Valiance SolutionsAdvisory TeamLokesh Gupta (General Partner, Spice Investment Fund)Dinesh (PHD, IIT Delhi)• Lokesh is working as General Partner in Spice New Investment fund. In this current role, Lokesh isresponsible for identifying startup companies in Education domain and help them transform their ideasinto big enterprises. Prior to that Lokesh was heading Spice Labs as its CEO.• Lokesh holds his management diploma from IIM-Ahmadabad and bachelors degree in computers fromIIT Delhi.• Dinesh has 12 years of strong experience in data driven analytical consulting, modeling and statisticalanalysis. He currently serves as Vice President Analytics with Hansa Cequity, premier marketingAnalytics firm in India. Prior to this he has held senior positions in companies like ICICI, GE Capital,Inductis at senior positions in analytics capacity.• Throughout his career Dinesh has provided analytical & technological leadership, tactical solutions andmeasurable delivery of financial opportunities through advanced data mining/predictive analyticssolutions for various business verticals like Retail, Insurance, FMCG, Automobile, Travel & Hospitality,Telecom, Mutual Funds etc. He also collaborates with academia in trainings, course materials for MBAstudents in analytics.
  22. 22. © 2013 Valiance SolutionsExperience of Executive Team
  23. 23. © 2013 Valiance SolutionsValiance Solutions Private LimitedA-146, Opposite TCS building,Sector 63, Noida, U.P - 201306India.Contact Person: Vikas KamraOffice No: +91 120 4119409Contact No: +91 8750068961