Fraud and Risk in Big Data

Fraud and Risk in Big Data

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  Fraud and Riskin Big Data Data Engineering and Cloud Computing Department Reva University SRN:R16MDC06
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  Outline 1. Introduction 2. Whatis Big Data 3. Fraud 4. Risk
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  Introduction Big Data maywell be the Next Big Thing in the IT world. Big data burst upon the scene in the first decade of the 21st century. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning. Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
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  Big Data ‘Big Data’is similar to ‘small data’, but bigger in size but having data bigger it requires different approaches: Techniques, tools and architecture an aim to solve new problems or old problems in a better way
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  Big Data Walmart handlesmore than 1 million customer transactions every hour. Face book handles 40 billion photos from its user base. Decoding the human genome originally took 10 years to process; now it can be achieved in one week.
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  Fraud Fraud as aCrime: Fraud is a generic term, and embraces all the multifarious means that human ingenuity can devise, which are resorted to by one individual, to get an advantage by false means Corporate Fraud: Corporate fraud is any fraud committed by, for, or against a business corporation. Management Fraud: Management fraud is the intentional misrepresentation of corporate or unit performance levels
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  Fraud One of themost common forms of fraudulent activity is credit card fraud. Social media and mobile phones are forming the new frontiers for fraud.
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  Risk It would bean understatement to say that risk management is data-driven The two most common types of risk management are credit risk management and market risk management. Credit risk analytics focus on past credit behaviors to predict Market risk analytics focus on understanding the likelihood that the value of a portfolio will decrease due to the change in stock prices, interest rates, foreign exchange rates.
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  Marketing Operations BankersCEOs • Next Best Action • Recommended Interventions • Lifestyle Yield Management • Seasonal Personal Impact • Theft Profiling • Fraudulent Transaction Identification • Remote Shutdown • Site Monitoring • Recommended Interventions • Risky Customer Profiling • Call Center Monitoring • Churn Scoring • Payment System Errors • Money Laundering prevention • Compliance • Data Entry Intervention ? Personalization of offers & banking experience Risk Reduction & ComplianceCustomer Churn PreventionFraud Detection Areas of Opportunity for Financial Analytics
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  Big Data Challenges
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  Fraud Detection ReferenceArchitecture Apps data from devices News and other alerts Solution UX Provisioning API (Pull) User Profile Information Stream Processors Analytics & Machine Learning Business Integration Connectors and Gateway(s) User Recent Activity Store Gateway Data Lake Gateway App Backend Data Path Optional solution component Main solution component Thin Client Presentation & Business Connectivity Data Processing, Analytics and ManagementDevice Connectivity Personal mobile devices Trades and/or transactions Business systems
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  Benefits of BigData Newest research finds that organizations are using big data to target customer-centric outcomes, tap into internal data and build a better information ecosystem. Big Data is already an important part of the $64 billion database and data analytics market