Business transaction data is a huge part of any bank’s functioning and is managed and stored by IT systems.
Business processes are supported by these IT systems. With these IT systems in pace, the level of human interaction has drastically reduced.
This in turn has become a huge reason for fraud to occur. Automated controls are used to keep frauds in check.
Identifying an expected fraud to take place or an actual fraud in an organization is what fraud detection is about.
Solid systems and processes need to be in place to prevent frauds from happening or to detect them at an early stage.
Proactive or reactive methods as well as manual or automated methods are used to detect fraud.
With the aim of discovering new types of frauds as well as traditional frauds, all banks have begun to realize the importance of fraud detection.
A skilled fraudster will circumvent even the most sophisticated fraud detection techniques. Hence, sound fraud detection techniques should always be in place.
Bank transactional data is now accessible pretty easily from internal and external sources.
This forces the use of advanced analytics in fraud detection programs. Fraud data analytics make it possible to detect frauds before they happen.
Making use of advanced analytics and fraud analytics techniques combined with human interaction to help detect unorthodox transactions be termed as fraud analytics.
Today, various rule based methods and different anomaly detection methods are already being used by many banks.
However, these have their own limitations and are not all that powerful. Fraud detection capabilities are enhanced with the influx of analytics and a whole new dimension to fraud detection techniques can be seen.
Along with this, performance measurement which helps standardize and maintain control for constant improvement is possible with fraud analytics.
Fraud analytics helps in the identification of scenarios, new trends and patterns under which frauds take place. The traditional methods miss out on these aspects.
Fraud analytics combs through data and combines data from multiple sources including public records and integrates it into a model.
It is not a replacement of the existing rule based methods, but is in fact a buildup of the traditional methods.
Deriving value from unstructured data is an unexplored goldmine and fraud analytics helps you attain that.
In most organizations structured data are stored in data warehouses. Unstructured data is the area where there is a high chance for fraudulent activity to take place.
This is where Text Analytics plays a huge role in reviewing this data and preventing fraud.
Steps to create your Fraud Program:
The banking industry is the most susceptible to fraud. The fraudulent activities can be internal or external.
Due to it being a highly regulated industry, banks have to adhere to many external compliance requirements and along with this they must have their fraud detection measures on guard all the time.
These are a few examples of fraud that take place in banking:
A situation where an entire site is cloned or only the pages from which orders are placed.
These are sites where customers are offered a service for a very cheap price. A site like this request a customer’s credit card details to access the content of the site.
These are valid credit card numbers which are generated by computer programs. A list of credit card account numbers are generated from the same account.
It a key element and is mandatory for certain processes in fraud detection. When data population is high, sampling is the most effective.
Entire control of the fraud detection may not be possible because it takes only data from a few population sources.
However, sampling still has its own advantages. Fraudulent transactions are not random and banks must test every transaction to effectively detect fraud
Ad-Hoc is finding out fraudulent transactions by using a hypothesis. Transactions can be tested and opportunities for fraud to take place can be explored.
This method 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 of time.
These scripts must be run every day to help go through every transaction and receive notifications regarding frauds.
The fraud detection process will become extremely efficient and consistent with this method in place.
This method will help you go beyond finding regular frauds and will help detect the abnormal ones:
Benford’s law is a law of anomalous numbers. It is often known as the first digit law.It is commonly used as an indicator of fraudulent data.
The distribution used here is non-uniform. This means smaller digits are more likely than larger digits.
Patterns will appear and the transactional numbers will be tested. When numbers that are not supposed to appear so frequently, begin to appear, those are the prime suspects.
Frauds in the banking sector are always increasing. Technological advancements open up new avenues for fraudsters.
Advanced statistical analytics, machine learning and predictive analytics are some of the ways that banks detect fraud and keep it at a minimum.
By Uma Raj
By Uma Raj
By Abishek Balakumar
Abhimanyu is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.