Fraud Analytics for the banking sector!

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.

Fraud detection:

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.

Why is fraud detection important for a bank?

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.

Fraud monitoring Analytics

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.

Fraud Analytics – The Meaning

Making use of advanced analytics and fraud analytics techniques combined with human interaction to help detect unorthodox transactions be termed as fraud analytics.

Fraud Analytics – The Importance

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 – Advantages

Hidden Pattern Recognition:

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.

Data Integration

Fraud analytics combs through data and combines data from multiple sources including public records and integrates it into a model.

Enhance existing efforts

It is not a replacement of the existing rule based methods, but is in fact a buildup of the traditional methods.

Harnessing unstructured data

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.

Advanced-Analytics

Data Analytics – The Process

Steps to create your Fraud Program:

  • A profile which contains all areas where fraud might occur and the different types of fraud that may occur must be created.
  • Use the ad-hoc testing method to find indicators of fraud across different areas in the organization.
  • Make use of a risk assessment matrix and decide as to which areas need more attention
  • All the activity should be monitored and communicated to everyone across the organization so that awareness about the happenings in the organization is maintained.
  • In case of any fraud discovery, inform the management and fix the issue immediately. Find out why the issue happened and ensue prevention mechanisms are in place.
  • Broken controls are to be fixed
  • Segregate duties clearly
  • Repetition of the process and expansion to a bigger scale

Banking frauds and remedial measures

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.

Banking Related Fraud Schemes:

These are a few examples of fraud that take place in banking:

  • Corruption
  • Cash Fraud
  • Billing Fraud
  • Check Tampering Fraud
  • Skimming
  • Larceny
  • Financial Statement Fraud

Most common internet banking frauds:

Site cloning

A situation where an entire site is cloned or only the pages from which orders are placed.

False merchant sites

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.

Credit card generators

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.

Fraud Analytics – Methods and best Practices

Sampling

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

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.

Repetitive Analysis

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.

Statistical Analytics Techniques

This method will help you go beyond finding regular frauds and will help detect the abnormal ones:

    • Statistical perimeters must be calculated to find out the values that show in excess of the averages of the standard deviation.
    • Anomalies must be detected based the high and low values in the perimeter. These anomalies are the indicators of fraud.
    • Classification of data is very crucial as transactional anomalies can be detected based on geography, timeline etc.

Benford’s Law

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.

Here are a few other data mining tools that can be used to detect fraud:

  • Data Matching– If any data matches exactly with other data, this method will fish it out for you. Duplicate transactions can be avoided with the use of data matching.
  • Sounds like– Variations of valid company employee names always exist and this method helps counter this issue.
  • Gaps– This method allows you to find out missing sequential data. Purchase orders in sequence are a great example of this. Missing data can be found when the PO’s are not in sequence.

Conclusion

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.

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