Fraud is a billion-dollar business and it is increasing every year. Fraud involves one or more persons who intentionally act secretly to deprive another of something of value, for their own benefit.
Fraud is as old as humanity itself and can take an unlimited variety of different forms. However, in recent years, the development of new technologies has also provided further ways in which criminals may commit fraud.
Why is fraud detection important ?
Fraud detection technique is important for an organization to find out new type of frauds and also some traditional frauds. The benefits of fraud detection includes the following.
What is Fraud Analytics ?
Fraud analytics is the combination of analytic technology and Fraud analytics techniques with human interaction which will help to detect the possible improper transactions like fraud or bribery either before the transaction is done or after the transaction is done.
One early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell. Retail industries also suffer from fraud at POS. Some supermarkets have started to make use of digitized closed-circuit television (CCTV) together with POS data of most susceptible transactions to fraud.
Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence
● Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data.
● Calculation of various statistical parameters such as averages, quantiles, performance metrics, probability
distributions, and so on. For example, the averages may include average length of call, average number of calls
per month and average delays in bill payment.
● Models and probability distributions of various business activities either in terms of various parameters orprobability distributions.
● Computing user profiles and Time-series analysis of time-dependent data.
● Clustering and classification to find patterns and associations among groups of data.
● Matching algorithms to detect anomalies in the behavior of transactions or users as compared to previously known models and profiles. Techniques are also needed to eliminate false alarms, estimate risks, and predict future of current transactions or users.
3 BVOC ANALYTICS