29 January 2020 * 5 min read
Written by Chudi Nwachukwu
Usually, when one thinks of fraud investigation or simply investigation what comes up is manual and reactive techniques — the use of paper, one on one interviews and taking action after the event has occurred. Wouldn’t it be an icing on the cake if you could detect the fraud before it happened; nipping it in the bud.
The significance of this is cost savings in profit, not loss, brand name still intact, customers’ trust not eroded (which you can’t really put a value on) and going concern integrity, etc. The list is not exhaustive. Welcome to the future now happening. Welcome to the world of data analytics.
Fraud detection using data analytics involves the use of proactive and automated techniques. An organization should include these Fraud detection techniques in its anti-fraud strategy.
The benefits of fraud detection include:
- Reduced exposure to fraudulent activities
- Reduced costs associated with fraud
- Exposing vulnerable employees at risk of fraud
- Improves the results of the organization
- Trust and confidence of the shareholders and customers
Analytics for fraud monitoring
Accessibility of business data from internal and external sources has become easier paving the way for organizations to use analytics in their fraud detection programs. Fraud data analytics plays 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.
The What and Why of Fraud Analytics
Fraud analytics is the combination of analytic technology and fraud analytics techniques with human interaction, which will help to detect potential improper transactions like theft either before or after the transaction is done.
When you add analytics to traditional anomaly detection methods already in practice by many organizations, it enhances the fraud detection capabilities and gives a new dimension to the fraud detection techniques. Fraud analytics also helps measure performance which in turn sets the pace for constant improvement.
Benefits of Fraud Analytics
Identify Hidden Patterns: Fraud analytics identify new patterns, trends, and scenarios under which frauds take place.
Data Integration: Fraud analytics plays an important role in integrating data. It combines data from various sources and public records that can be integrated into models.
Enhance existing efforts: Fraud analytics does not replace the traditional rules-based methods but it just adds up to your existing efforts to bring improved results.
Harnessing unstructured data: Fraud analytics helps in deriving the best value from unstructured data. Unstructured data is where more fraudulent activities take place. Analytics review the unstructured data and prevent fraud from occurring.
Improves performance: With the use of fraud analytics, you can easily identify what is working for your organization and stop that which is not working.
Explained in brief details below are 5 important fraud detection methods; Sampling, ad-hoc, repetitive or continuous analysis, analytics techniques, and Benford’s law.
Sampling is more effective where there is a lot of data population involved. However, it may not be able to fully detect fraud as it takes only a few populations into consideration. Fraudulent transactions do not occur randomly, therefore, an organization needs to test all the transactions to effectively detect fraud.
This is simply finding out fraud by means of a hypothesis. It allows you to explore. You can test the transactions and find out if there is an opportunity 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 investigate.
Repetitive or Continuous Analysis
This means creating and setting up scripts to run against big data to identify frauds as they occur over a period of time. Scripts are run every day to go through all the transactions and get periodic notification regarding the frauds.
Analytic techniques help you to find out frauds that are not normal, for example:
- 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 indicators of fraud
- Classify the data — group your data and transactions based on specific factors like geographical area.
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.
Implementing Data Analytics for Fraud Detection
A dependable framework is needed to make the fraud detection process more successful. Some steps on how to implement analytics for fraud detection are:
Before embracing fraud analytics an organization should do a SWOT analysis to match with its strengths and weaknesses to enable fraud detection program work to the fullest.
Build a dedicated fraud management team
It is important to have a dedicated team that works to find and prevent fraud 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. Should they build or purchase an analytical fraud detection solution from a vendor? A few important factors to be considered while purchasing a fraud analytics solution like cost, user interface, scalability, ease of integration, etc.
Integrate all the databases in the organization and remove all unwanted things from the databases.
Setting the threshold
Whether the solution is in-built or purchased from outside the company, it 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.
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 effective fraud detection program. This will help you to eradicate any new frauds that might develop in the future.
In a nutshell, fraud will increase as the transaction volume of your business increases. Fraud Analytics 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 fraud detection project and then start expanding. It can take as little as a few weeks.
With the advances in data analytics, it is easier to do more. But data analytics cannot directly detect fraud. Most times data analytics is used to determine anomalies, but it is only after investigation and verification that an audit can assess whether a particular transaction is fraudulent or not.
Chudi Nwachukwu is a Fraud Expert and professional with a keen eye for detail and has exceptional ability to resolve fraud cases and ensure that work performed is in conformance to existing policies and procedures. He is successful at reducing company costs by employing results focused-problem solving techniques.