Updated April 8, 2023
Introduction to Fraud detection Analytics
Fraud detection means the identification of actual or expected fraud to take place within an organization. An organization need to implement proper systems and processes to detect frauds at an early stage or even before it occurs. Fraud detection consists of the following techniques
- Proactive and Reactive
- Manual and Automated
An organization should include these techniques in its anti fraud strategy
Techniques to Detect Fraud Analytics – These days Business data is being managed and stored by IT systems in an organization. Therefore organizations rely more on IT systems to support business processes. Because of such IT systems the level of human interaction has been reduced to a greater extent which in turn becomes the main reason for fraud to take place in an organization. To detect and prevent such frauds again organizations go in for automated controls.
Why is it important?
Fraud detection technique is important for an organization to find out new type of frauds and also so some traditional frauds. Even the most effective detection technique can be circumvented by a skilled fraudster. So the organization should be very clever in developing such Fraud detection techniques.
The benefits include the following;
- Reduced exposure to fraudulent activities
- Reduced costs associated with fraud
- Find out the vulnerable employees at risk to fraud
- Have organizational controls
- Improves the results of the organization
- Gains the trust and confidence of the shareholders of the organization
Analytics for fraud monitoring
Accessibility of business data from internal and external sources have become more easy. This makes the organizations to use analytics in their fraud detection programs. Fraud Data analytics play a crucial role in the early detection and monitoring of fraud. These data analytic techniques will help the organization to detect the possible instances of fraud and implement an effective fraud monitoring program to protect the organization.
What is Fraud Analytics?
Fraud analytics is the combination of analytic technology and Fraud analytics techniques with human interaction which will help to detect possible improper transactions like fraud or bribery either before the transaction is done or after the transaction is done.
Why Fraud Analytics?
Traditional Anomaly detection and various rules-based methods are already in practice by many organizations to detect and prevent fraud. But they are not that powerful. They have their own limits. When analytics is added to such traditional methods, it enhances fraud detection capabilities and gives a new dimension to the fraud detection techniques.
Another important reason for using data analytics to handle fraud is because these days internal control systems have control weaknesses. In order to avoid this the organizations should have a control over every transaction that takes place and test the transaction using fraud analytics.
And fraud analytics also helps to measure the performance which will help you to standardize and have control for constant improvement.
Benefits of Fraud Analytics
Here are the following benefits described below.
Identify Hidden Patterns
Fraud analytics identify new patterns, trends and scenarios under which frauds take place. Whereas traditional approaches miss such things.
Fraud analytics plays an important role in integrating data. It combines data from various sources and public records that can be integrated into a model.
Enhance existing efforts
Fraud analytics does not replace the traditional rules based methods but it just adds up to your existing efforts to bring you more improved results
Harnessing unstructured data
Fraud analytics helps in deriving the best value from unstructured data. Most of the structured data are stored in data warehouse of the organization. But unstructured data is the place where more fraudulent activities take place. This is where text analytics plays an important role in reviewing the unstructured data and preventing fraud from taking place.
Improve the performance
With the use of fraud analytics you can easily identify what is working for your organization and what is not working for your organization
Data Analytics Process
Steps to create your Fraud Programme
- Create a profile that includes all the areas where fraud is expected to occur and the possible types of fraud in those areas.
- Measure the risk of fraud and the overall exposure to the organization. Prioritize the risks based on fraud.
- Follow Ad-hoc testing method to find for indicators of fraud in particular areas of organization
- Establish risk assessment and decide where to pay closer attention
- Monitor the activity and communicate it throughout the organization so that employees in the organization are aware about the happening in the organization
- If there is any fraud found out, inform the management immediately to solve out the issue and to find out why it happened
- Fix any broken controls
- Segregation of duties is very essential
- Expand the scope of the program and repeat the process
Methods of Fraud Analytics
There are five important fraud methods given below.
Sampling is mandatory for certain processes of fraud detection. Sampling will be more effective where there a lot of data population involved. But still it has its own disadvantage. Sampling may not be able to fully control fraud detection as it takes only few population into consideration. Fraudulent transactions do not occur randomly therefore an organization need to test all the transactions to effectively detect fraud.
Ad-Hoc is nothing but finding out fraud by means of a hypothesis. It allows you to explore. You can test the transactions and find out if there are any opportunities for fraud to take place. You can have a hypothesis to test and find out if there is any fraudulent activity occurring and then you can investigate on the same.
Repetitive or Continuous Analysis
Repetitive or Competitive Analysis means creating and setting up scripts to run against big volume of data to identify the frauds as they occur over a period of time.
Run the script every day to go through all the transactions and get periodic notification regarding the frauds. This method can help in improving the overall efficiency and consistency of your fraud detection processes.
Analytic techniques helps you to find out frauds that are not normal
- Calculate Statistical parameters to find out values that exceed averages of standard deviation.
- Look at high and low values and find out the anomalies there. Such anomalies are often the indicators of fraud
- Classify the data – Group your data and transactions based on specific factors like geographical area.
Benford’s law can often be used as an indicator of fraudulent data. Benford’s distribution is non-uniform with smaller digits more likely than the larger digits. Using Benford’s law you can test certain points and numbers and identify those which appear frequently than they are supposed to and therefore they are the suspect.
There are several other detection data mining tools to detect fraud
- Data Matching – This method will find out if there is any data that exactly matches with another data.
- Sounds like – This is another powerful method where it identifies variations of valid company employee names.
- Duplicates – This is another method that is most commonly used by a lot of organizations to identify fraud as well as any error occurring within all the business transactions.
- Gaps – In this method, you can find out the missing sequential data. For example, if you have purchase orders which are issued by the company in sequential order and if anything is missing you can easily find out. This is an easy method and it will work out great if used correctly.
Fraud Analytics in Insurance Companies
Data analysis has proved really reliable in fraud detection in various fields. Let’s take an example of Insurance company using Fraud detection methods
Three fraud detection methods used by Insurance company
Social Network Analysis (SNA)
SNA method follows the hybrid approach to detect fraud. The hybrid approach includes organizational business rules, statistical methods, pattern analysis and network linkage analysis. When you search for fraud in link analysis, you need to look for clusters and how clusters relate to others. Several data sources like records, judgements and bankruptcies can be integrated into a model.
The below figure explains the flow of SNA fraud detection method in an insurance company
Fraud Detection Predictive Analytics for big data
Predictive analytics uses text analytics and sentiment analysis to look at big data for fraud detection. Predictive analysis has been widely used by a lot of organizations as it helps in proactively detecting frauds. In the beginning, Predictive analysis was used to analyze statistical information stored in structured databases but now it is extended to the big data realm. The picture given below represents the flow of fraud detection using big data analysis
Social Customer Relationship Management (CRM)
Social CRM is a process of fraud detection program. In these days it is very crucial for insurance companies to link social media to their CRM. Linking social media to CRM increases transparency with the customers. This transparency gains the customers trust over the organization. This customer-centric ecosystem benefits the business to a great extent and also see through that the customers are in control. The following diagram represents the flow of Social CRM in insurance companies
Implementing Data Analytics for Fraud Detection
Many insurance companies use different Fraud detection tools to detect fraud. But a more dependable framework is needed to make the fraud detection process more successful. We have listed here few steps on how to implement analytics for fraud detection
Many organizations have realised the increasing the importance of fraud analytics. But in a hurry they are opting for expensive fraud detection solutions that do not match with the company’s strengths and weaknesses. Therefore organizations should do SWOT analysis before starting with fraud detection program in order make it work to the fullest.
Build a dedicated fraud management team
Traditional companies do not have a specific team for fraud detection. But these days it is important to have a dedicated team that works to find and prevent frauds in the organization. The team should have a proper flow and a proper reporting fraud detection system.
Build or buy option
Once SWOT analysis is over and team allocation is done it is important for the companies to decide how they want to implement analytics and what resources are required. Companies need to know whether they are capable of building an analytics solution for themselves or should they purchase an analytical fraud detection solution from an vendor. If there is a need to purchase then the company should do a research about the different fraud detection vendors and their products available in the market that fits their company. There are few important factors to be considered while purchasing fraud analytics solution like cost, user interface, scalability, ease of integration and others.
Integrate all the databases in the organization and remove all unwanted things from the databases.
Layout relevant business rules
Companies should come up with business rules after doing research on the resources and expertise of the company. There are different types of fraud and few of which are specific to particular industry. The external vendor cannot build a robust fraud detection solution without getting the proper inputs from the organization or company.
Setting the threshold
Whether the solution is in-built or purchased from outside the company should provide boundary values for different anomalies. Thresholds are set using anomaly detection. If boundaries are set too high then there are chances of frauds to slip through in between. If the boundaries are set too low then a lot of time and resources are wasted. Therefore an organization should be very clever in determining the thresholds
Data mining tools are used to build models that produce fraud propensity scores which is linked to unidentified metrics. After the scoring is done automatically, the results are established for review and further analysis.
SNA has proved to be the most effective detection program by modelling relationships between various entities.
Build an integrated case management system leveraging social media
A case management system lets an investigator to know about all the important findings that are relevant to an investigation and it can be either structured or unstructured data. Metrics are the indicators of fraud and it can be helpful for comparison at the organizational level or network level.
Forward looking analytics solutions
Companies should always look out for any additional sources of data and should integrate them with the current fraud detection program to build the most efficient and effect fraud detection program. This will help you to eradicate any new frauds that might develop in the future.
Frauds will increase as the transaction volume of your business increases. Technology advancement is a plus as well as a minus to your business as it opens up new avenues for fraudsters. analytics to detect Fraud can play a very important role in identifying fraud in the early stages and protecting your business from heavy loss. It does not require a lot of time and resources to get fraud analytics running for your business. Get started with a small detection project and then start expanding. It can take as little as few weeks.
This has been a guide to fraud detection analytics. Here we discuss the basic concept, benefits, methods, process, importance along with Implementing Data Analytics. You can also go through our other suggested articles to learn more –