How Fraud Analytics Can Protect the Banking Industry

Introduction

Business transaction data is an important part of any bank's operation and is managed and stored by IT systems. Business processes are compatible with these IT systems. With these IT systems in pace, the level of human interaction has been drastically reduced. This, in turn, has become a major reason for the occurrence of fraud. Automated controls are used to keep fraud under control.

Identifying an expected fraud or actual fraud in an organization is what fraud detection is. Solid systems and processes must be implemented to prevent fraud or 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 to a bank?

With the aim of discovering new types of fraud as well as traditional frauds, all banks have begun to realize the importance of fraud detection. A specialist swindler will avoid even the most sophisticated fraud detection techniques. Therefore, robust fraud detection techniques should always be in place.

Google Analytics Fraud Monitoring

Bank transactional data is now easily accessible from internal and external sources. This requires the use of advanced analysis in fraud detection programs. Analyzing fraud data allows you to detect fraud before it occurs.

Fraud Analysis - The Significance

The use of advanced fraud analysis and analysis techniques combined with human interaction to help detect heterodox transactions is called fraud analysis.

Fraud Analysis - The Importance

Many banks are now using multiple rule-based methods and different anomaly detection methods. However, these have their own limitations and are not as powerful. The fraud detection features are enhanced with the influx of analysis and a new dimension of fraud detection techniques can be seen. Along with this, you can measure performance that helps you standardize and maintain control for constant improvement with fraud analysis.

Fraud Analysis - Advantages

Recognition of hidden patterns

Fraud analysis helps identify scenarios, new trends and patterns under which fraud occurs. Traditional methods ignore these aspects.

Data integration

Fraud analysis combines data and combines data from various sources, including public records, and integrates them into a model.

Improve existing efforts

It is not a substitute for methods based on existing rules, but, in fact, it is an accumulation of traditional methods.

Use of unstructured data

Achieving value from unstructured data is an analysis of scams and unexplored gold mines that helps you achieve it. In most organizations, structured data is stored in data warehouses. Unstructured data is the area where there is a high probability of fraudulent activity. This is where text analysis plays a very important role in reviewing such data and preventing fraud.

Data analysis - the process

Steps to Create Your Fraud Program:

• A profile should be created that contains all areas where fraud can occur and the different types of fraud that may occur.

• Use the ad hoc test method to find fraud indicators in different areas of the organization.

• Use a risk assessment matrix and decide which areas need more attention.

• All activity must be monitored and communicated to everyone in the organization so that knowledge about the events in the organization is maintained.

• If fraud is detected, inform management and resolve the problem immediately. Find out why the problem occurred and check for prevention mechanisms.

• Broken controls should be repaired.

• Clearly separate tasks

• Repetition of the process and expansion on a larger scale.

Bank cheats and corrective measures

The banking sector is the most susceptible to fraud. Fraudulent activities can be internal or external. Due to being a highly regulated industry, banks have to adhere to many external compliance requirements, and along with that, they must have their fraud detection measures on guard all the time.

Schemes of fraud related to banks

These are some examples of fraud that occur in the banking sector:

• Corruption

• Cash fraud

• Billing Fraud

• Check fraud against counterfeiting

• Skimming

• Furto

• Financial Statement Fraud

Most common bank cheats on the Internet:

Cloning sites

A situation in which a cluster is cloned or only the pages from which the requests are placed.

False merchant sites

These are sites where customers receive a service at a very low price. A site like this requests the details of a customer's credit card to access the content of the site.

Credit card generators

These are valid credit card numbers generated by computer programs. A list of credit card account numbers is generated from the same account.

Fraud Analysis - Methods and Best Practices

sampling

It is a key element and is mandatory for certain processes in the detection of fraud. When the data population is high, sampling is the most effective. All control of fraud detection may not be possible because it only takes data from some population sources. However, sampling still has its own advantages. Fraudulent transactions are not random, and banks must test all transactions to effectively detect fraud

Ad hoc

Ad-Hoc is discovering fraudulent transactions using a hypothesis. Transactions can be tested and fraud opportunities can be exploited.

Repetitive analysis

This method depends on the configuration of scripts that are executed in connection with large data values in order to identify frauds as they occur in a given period. These scripts must be run every day to help pass through all transactions and receive notifications about fraud. The fraud detection process will be extremely efficient and consistent with this method.

Statistical analysis techniques

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

• Statistical perimeters must be calculated to discover the values that show more than the averages of the standard deviation.

• The anomalies should be detected based on the high and low values in the perimeter. These anomalies are the indicators of fraud.

• The classification of the data is very important since transactional anomalies can be detected based on geography, on the timeline, etc.

Benford's Law

Benford's law is a law of anomalous numbers. It is often referred to as the first digit law. It is commonly used as an indicator of fraudulent data. The distribution used here is not uniform. This means that smaller digits are more likely than larger digits.

The patterns will appear and the transactional numbers will be tested. When the numbers that should not appear so frequently begin to appear, these are the main suspects.

Here are some other data mining tools that can be used to detect fraud

• Data Correspondence - If any data exactly match other data, this method will be performed for you. Duplicate transactions can be avoided with the use of data correspondence.

• It seems that - Valid name variations of company employees always exist and this method helps to combat this problem.

• Lagoons - This method allows you to discover missing sequential data. Purchase orders in a sequence are a great example of this. The missing data can be found when the POs are not in sequence.

conclusion

Fraud in the banking sector is always increasing. Technological advances open new paths for scammers. Advanced statistical analysis, machine learning and predictive analysis are some of the ways in which banks detect fraud and keep it to a minimum.

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