Design a BI system for fraud detection .Describe all the steps from data collection to Decision Making clearly?

Fraud detection for Telecommunication Industry

  • The telecommunications industry has expanded dramatically in the last few years with the development of affordable mobile phone technology.
  • Fraud is an adaptive crime, so it needs special method of intelligent data analysis to detect and prevent it.
  • These method exits in the areas of Knowledge Discovery in Databases (KDD), Data Mining, Machine Learning and Statistics. They offer applicable and successful solutions in different areas of fraud crimes.
  • At a low level, simple rule-based detection systems use rules such as the apparent use of the same phone in two very distant geographical locations in quick succession, calls which appear to overlap in time and very high value and very long calls.
  • At a higher level, statistical summaries of call distributions (often called profiles or signature at the user level) are compared with thresholds determined either by experts or by application of supervised learning methods to known fraud/nonfraud cases.
  • Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, and otherwise support a claim of financial fraud.
  • The main steps in forensic analytics are (a)data collection,(b)data preparation,(c)data analysis, and(d)reporting.
  • For example, forensic analytics may be used to review an employees’ purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.

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  • Techniques used for fraud detection fall into two primary classes: Statistical techniques and Artificial intelligence.
  • Examples of Statistical data analysis techniques are:

    1. Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data.
    2. Calculation of various statistical parameters such as averages, performance metrics .For e.g., the average may be include average length of call, average number of calls per month.
    3. Computing user profiles.
    4. Time –series analysis of time-dependent data.
    5. Clustering and classification to find patterns and association among groups of data.
  • Examples of AI techniques are:

  1. Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
  2. Expert systems to encode expertise for detecting fraud in the form of rules.
  3. Pattern recognition to detect approximate classes, clusters, or patterns of suspicious behavior either automatically or to match given inputs.

    Machine learning techniques to automatically identify characteristics of fraud.

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