Fraud Management Solutions

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Fraud continues to proliferate across financial institutions, through multiple lines of business and banking channels. Increasingly sophisticated criminal tactics and the proliferation of organized crime rings make detecting fraud difficult and preventing it nearly impossible. Adding to the complexity is increased globalization and growth through mergers and acquisition, which make it harder to effectively monitor multiple portfolios and business lines. The presentation discussus best practices and ideas around the prevention, investigation, and detection of possible fraudulent activities across multiple industries.

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  • Hi.. Can you pls email this ppt to narsi1968@gmail.com ...very interesting and informative.. Regards
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  • Silverleaf Financial knows that it is very important to take fraud management into consideration and mitigate the risks involved. Actions by employees can really damage one reputation whether they are true or not does not matter. It only matter what is observed as said in the press or on Google. A constant approach to maintaining controls that can not be overridden will ensure that appropriate good information is distributed. - Shane Baldwin
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Fraud Management Solutions

  1. 1. make connections • share ideas • be inspired India’s Largest Analytics ForumEnterprise Fraud ManagementB.Radha KrishnaPractice Manager, Risk & Fraud ManagementSAS Institute India Pvt. Ltd Copyright © 2011, SAS Institute Inc. All rights reserved.
  2. 2. Agenda Business Issues Key Themes of Enterprise Fraud Management • Customer Classification • Detection Methodologies • Investigation − Alert Management − Case ManagementCopyright © 2011, SAS Institute Inc. All rights reserved.
  3. 3. Business Issues REGULATORY EMPHASIS INDUSTRY DRIVERS CUSTOMER ISSUES ANALYST VIEWCopyright © 2011, SAS Institute Inc. All rights reserved.
  4. 4. Findings of Forensic Security – Guidelines forprevention of fraudsCopyright © 2011, SAS Institute Inc. All rights reserved.
  5. 5. Working Report on Electronic Banking Fraud detection a) Detection of fraud Despite strong prevention controls aimed at fraud deterrence, fraudsters do manage to perpetrate frauds. In such cases, the earlier the fraud is detected, the better the chance of recovery of the losses and bringing the culprits to book. System triggers that throw up exceptional transactions, opening up channels that take note of customer/employee alerts/disputes, seeding/mystery shopping exercises and encouraging employees/customers/ well- wishers to report suspicious transactions/behaviours are some of the techniques that are used for detection of frauds. The exceptional/suspicious transactions/activities reported through these mechanisms should be investigated in detail. b) Transaction monitoring Banks should set up a transaction monitoring unit within the fraud risk management group. The transaction monitoring team should be responsible for monitoring various types of transactions, especially monitoring of potential fraud areas, by means of which, early alarms can be triggered. This unit needs to have the expertise to analyse transactions to detect fraud trends. This unit should work in conjunction with the data warehousing and analytics team within banks for data extraction, filtering, and sanitisation for transaction analysis for determining fraud trends. Banks should put in place automated systems for detection of frauds based on advanced statistical algorithms and fraud detection techniques. c) Alert generation and redressal mechanisms Appropriate mechanisms need to be established in banks, to take note of the disputes/exceptions or suspicions highlighted by various stakeholders including transaction monitoring teams in banks and to investigate them thoroughly. Banks should have a well publicised whistle blowing mechanism. Source : RBI Website Chapter 6 : Cyber Fraud Working Report on Electronic BankingCopyright © 2011, SAS Institute Inc. All rights reserved.
  6. 6. Financial Crimes in 21st century Criminals Financial Institutions Sophisticated methods Prevention siloed by line of business Dynamic attacks Detection is product or Hit multiple channels & channel-specific products simultaneously Separate investigation teams Engage insiders Act on account or customer Networked via web Rules and models have high false-positive rates Attack from remote jurisdictions Changing payments landscape: SVC, ACH, Wire, Exploit unwitting Mobile customersCopyright © 2011, SAS Institute Inc. All rights reserved.
  7. 7. Top Business Drivers, Strategies, andTechnology for Fraud and Financial CrimesManagement (2011)- Tower GroupBusiness Drivers in Financial Crimes ManagementUnderground fraud Employee fraud Data breaches/ New delivery channels New sourcingeconomy is organized, spurred by “Wikileaks” raising open new routes to strategies expandsophisticated, efficient underground market marketplace and fraud supply chain risk executive fearsNew technology (cloud, Anticipated regulatory New hires and role Technology upgrades Social networksgrid, virtual) brings new changes necessitate changes expose new pushed off for too long expand avenues forrisks computing tech. upgrades vulnerabilities must be addressed fraudstersFSIs’ Strategic Responses to Reduce Fraud RisksManage risk holistically, Manage fraud, Improve data Leverage fraud Merge AML and fraudincluding fraud risk security, compliance in governance information for new strategy, technology, coordinated fashion business opportunities and processesStandardize security and Adopt enterprise fraud Manage valuation, Use risk-based not Upgrade technologybusiness process with management with liquidity, counterparty standardized to comply with newsupply chain LOB responsibility risk with eye to fraud approach to fraud regulationsImportant Technology Trends in Financial Crimes ManagementLayered security and new Converging risk, Develop cloud security Adaptive analytics Enterprise case toolauthentication approaches security, and fraud strategy and vendor with governance, riskfor regulatory compliance platforms short-list mgmt. emphasisProactive scanning for new Cross-channel Adaptive life-cycle Visualization tools for Compliance modulepatterns in real- time, risk- profiling, security, and monitoring and root cause and early enrichment (red flags,based mode event coordination adjustments warning SARs, etc.)Source: TowerGroupCopyright © 2011, SAS Institute Inc. All rights reserved.
  8. 8. Top 10 Fraud Types Automated clearing house (ACH) and wire transfer fraud Attacks on Institution Networks ATM Skimming Credit Account ‘Bust-Outs’ Variations on Phishing Schemes Increasing Check Fraud Internal Fraud Mobile Phone Scams Online Application Fraud Prepaid CardsSource: Chartis Research Copyright © 2011, SAS Institute Inc. All rights reserved.
  9. 9. Enterprise Fraud Management Key Themes CUSTOMER CLASSIFICATION DETECTION METHODOLOGIES INVESTIGATION ALERT MANAGEMENT CASE MANAGEMENTCopyright © 2011, SAS Institute Inc. All rights reserved.
  10. 10. Enterprise Financial CrimesBreadth of disciplines as defined by the market Anti-money Laundering Compliance Fraud Brokerage Activity Monitoring Cards Broker Sanctions Blocking Deposit Surveillance Know Your Payments Trade Customer/Customer Surveillance Remote Banking Due Diligence Internal Loans Rings Enterprise Investigations Management (Case Management)Copyright © 2011, SAS Institute Inc. All rights reserved.
  11. 11. Customer Risk Classification High/Medium/Low Account Score Classify Opening Weighted Risk Classifications Questionnaire Responses H/M/L Transactional and list-based classifiers Parties Products Services on monthly basis. • PEPs, NGOs, MSBs • Correspondent Banking • Wires • On-line • Cash Intensive, • Private Banking Banking Periodic review and etc. suggestion of new Risk Classification for the customer Lists Behavior H/M/L during Assessment process.Copyright © 2011, SAS Institute Inc. All rights reserved.
  12. 12. Detection Methodologies Using a Hybrid Approach for Fraud Detection Enterprise Data Suitable for known Suitable for unknown Suitable for complex Suitable for associative patterns patterns patterns link patterns Rules Anomaly Predictive Models Social NetworkCustomer Account Detection Analysis Rules to filter Detect individual and Predictive assessment Knowledge discovery fraudulent transactions aggregated abnormal against known fraud through associative Trans- Appli- and behaviors patterns cases link analysis action cations Examples: Example: Example: Example: Internal • Mort. payments from • ACH transactions on • Like credit / debit • Association to knownEmployee different accounts account exceed norm transaction patterns fraud Bad Lists • Check serial # out of • # unsecured loans on • Like account opening • Identity manipulation range network exceed norm & closure patterns • Transactions to 3rd Party Call Flags Center • Card order follows • Check velocity • Like network growth suspicious Logs address change exceeds norm rate (velocity) counterparties • New ACH payee Hybrid Approach combination of all 4 approaches at account, customer, and network levelsCopyright © 2011, SAS Institute Inc. All rights reserved.
  13. 13. Risk Ranking Alerts History Risk Score Factor Scenario Alerts Application + Activities Scoring Duration = length of time that one alert should be considered when scoring a possible future alert Execution Probability Rate Risk Ranking Bayes Weight •The number of distinct •Ranks alerts based on the •The percentage of scenario entities to match a scenario or scenario(s) that are matched, suspects that would engage risk factor during the scoring any risk factors that apply to in the behaviour. The most duration divided by the total the same entity, as well as common scenario schemes number of entities of the any scenarios and risk factors should be assigned a value of appropriate subject matched within the scoring 10, and rare schemes should duration. be assigned a value of 1.Copyright © 2011, SAS Institute Inc. All rights reserved.
  14. 14. Scenarios and Risk Factors in Practice Customers AccountsTransactions Filtering Alert with low risk score Customers Alert with medium risk score Accounts Possible alertTransactions Alert with high risk score Customers AccountsTransactions = scenario/ risk factor hits Copyright © 2011, SAS Institute Inc. All rights reserved.
  15. 15. Effective Fraud Management ProgramKey Drivers Data Quality Integrated view of relationship Hybrid Detection techniques Case ManagementCopyright © 2011, SAS Institute Inc. All rights reserved.
  16. 16. SAS Enterprise Financial Crimes PlatformIntegrated Analytics & Case Investigation – Our Competitive Differentiators Product Enterprise SAS Fraud SAS Fraud Anti-Money Case Management Framework Laundering Management Target Banking Banking Industry Banking Government Government Banking Insurance Insurance Health Care Health Care Insurance Telco Other Copyright © 2011, SAS Institute Inc. All rights reserved.
  17. 17. SAS Enterprise Financial Crimes FrameworkInsurance Agent, Call Center Company Open Update Close Manage Claim Claim Claim Simple Case Call Center, Company Or SIU Complex Case SAS Fraud Framework Data Management SIU Modeling Detection Alert & Case Initiation InvestigationCopyright © 2011, SAS Institute Inc. All rights reserved.
  18. 18. Q&ACopyright © 2011, SAS Institute Inc. All rights reserved.
  19. 19. make connections • share ideas • be inspiredIndia’s Largest Analytics ForumThank YouB.Radha Krishnaradhakrishna.b@sas.com Copyright © 2010, SAS Institute Inc. All rights reserved.

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