Is Fraud Analytics the New Whistleblower for Banking Fraud?
Although fraudulent activities have been common in banking transactions over several years, digitization has caused a manifold increase in the frequency of such malpractices. This is where advanced technology such as fraud analytics comes into the picture. Fraud analytics encompasses the use of advanced analytics and fraud analytics techniques coupled with human interaction in order […]
Although fraudulent activities have been common in banking transactions over several years, digitization has caused a manifold increase in the frequency of such malpractices. This is where advanced technology such as fraud analytics comes into the picture. Fraud analytics encompasses the use of advanced analytics and fraud analytics techniques coupled with human interaction in order to detect any fraud or unorthodox transactions. The rising number of cyber crimes are prompting all banks around the world to leverage fraud management solutions that can tackle both traditional fraud and new types of fraud in banking.
The senior management and the board are ultimately responsible for a fraud management program. However, internal audit can be a key player in helping address fraud. Internal audit can show an organization how it is prepared for fraud management by providing an evaluation of the potential for the occurrence of fraud. Some of the common examples of banking-related fraud include:
- Cash Fraud
- Check Tampering Fraud
- Financial Statement Fraud
- False merchant sites
- Site cloning
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Importance of fraud analytics in banking
Today, banks already use various rule-based methods and anomaly detection methods, but they come with their own set of limitations. The influx of analytics enhances fraud detection capabilities, and a whole new dimension to fraud detection techniques can be seen. Fraud analytics also facilitates performance measurement which helps standardize and maintain control for constant improvement. Some of the key advantages of using fraud data analytics in banking include:
Recognition of hidden patterns
Fraud analytics helps identify scenarios, new trends, and patterns under which there are chances for frauds to take place. The traditional methods often miss out on these aspects. Analytics can dive deep into data and easily identify patterns that indicate potential fraud.
Fraud analytics analyzes data and combines data from multiple sources. This includes data from public records and integrates it into a model.
Enhance existing efforts
Fraud analytics is not a replacement of the existing rule-based methods. In fact, it is a buildup of the traditional methods.
Harness unstructured data
Fraud analytics helps in deriving value from unstructured data. In most organizations, data warehouses are used to store structured data. But did you know that unstructured data is the area where there is a high chance for fraudulent activity to take place? Here text analytics plays a big role in reviewing the data and thereby help in fraud prevention.
Improve the performance
Organizations can easily identify what is working for them and what is with the help of fraud analytics. This helps businesses to discontinue efforts that are not fruitful and invest more in activities that help the business.
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Common fraud analytics techniques
Five important fraud monitoring and detecting techniques are:
Sampling is a mandatory element for certain processes in fraud detection. Sampling is most effective in the case where the data population is high. However, here the entire control of fraud detection may not be achieved as it takes only data from a few population sources.
This method of fraud analytics relies on setting up scripts which run against large values of data with the aim of identifying frauds as they occur over a certain period. These scripts must be run on a daily basis so that they go through every transaction and the notifies in case of any fraud detection.
Ad-Hoc is all about detecting fraud through a hypothesis. This fraud analytics technique provides the scope to explore. Transactions can be tested to find out if there are any opportunities for potential fraud. A hypothesis can test and identify fraudulent activities which can then be investigated.
This method helps to go beyond finding regular frauds and detects the abnormal ones. This is done by calculating statistical parameters to identify values that exceed averages of standard deviation. Anomalies can be detected based on the high and low values in the perimeter. These anomalies are the indicators of fraud.
Benford’s law is one of the key fraud analytics techniques. It is commonly used as an indicator of fraudulent data. The distribution used here is non-uniform which means smaller digits are more likely than larger digits. The transactional numbers will be tested as the patterns appear. In case the numbers that are not supposed to appear so frequently, begin to appear, they are mostly the prime suspects for fraud.