Worldwide, businesses lose around $4 trillion annually due to fraud. As per data from the Association of Certified Fraud Examiners’ (ACFE) 2018 report, most typical organizations ran the risk of losing approximately 5% of their revenues due to fraud. Among the sectors that suffer huge losses due to fraud is healthcare, where companies lose around $68 billion annually, which amounts to 3% of the total healthcare spending.
With fraud becoming more prevalent across industries and different sizes of companies, organizations are finding it more challenging to implement efficient systems for detecting and preventing fraud.
Fraud Detection Analytics: Searching for Hidden Threats
The process of fraud detection involves identifying an actual or expected fraud that might take place in an organization. There must be systems in place to pinpoint fraudulent activity at an early stage so that measures can be taken either to prevent its occurrence or to minimize the loss caused by it. Traditional fraud management systems, which have been employed in the past, have not proven effective. Now, with easy access to data from internal and external sources, fraud analytics, which combines analytic technology and fraud analytics techniques, help in the detection and prevention of fraudulent activity either before or after it occurs.
Benefits of Fraud Analytics
Besides the fact that it helps to enhance the traditional methods of anomaly detection, fraud analytics offer several advantages.
1. Identify Hidden Patterns
Fraud analytics is far superior to traditional methods in identifying scenarios, patterns and trends when fraudulent activity occurs.
2. Data Integration
By combining data from various sources, fraud analytics simplifies the process of integrating the data into a model.
3. Enhance existing efforts
A point to note about fraud analytics is that it doesn’t do away with traditional methods but instead enhances their effectiveness to provide better results.
4. Harnessing unstructured data
While data warehouses store the structured data of the organization, it’s within the unstructured data that most of the fraudulent activity takes place. With the use of text analytics, the unstructured data can be easily reviewed to detect and prevent the occurrence of frauds.
5. Improving performance
Since every organization has different processes and systems, there is no single solution for fraud detection and prevention. Fraud analytics helps in identifying what is most suitable for an organization.
Fraud Detection Techniques
The types of fraud detection techniques that an organization employs will depend on the systems and processes followed. Accordingly, the following are common techniques.
Proactive vs Reactive
In the proactive technique, systems and processes are put in place to detect fraudulent activity before it occurs or at an early stage in the process. On the other hand, reactive fraud detection takes place after the event occurs.
Manual vs Automated
The difference lies in the level of human dependence. While manual detection is performed by employees, automated detection relies on machines to a large extent.
Using DSS for Fraud Detection Analytics
Big Data provides access to new sources of data as well as real-time events, which can be used as inputs for Decision Support System tools and models for fraud detection. By analyzing current indicators of fraudulent behavior and correlating them with occurrences of fraud, it becomes easier to identify fraud before it occurs or at an early stage in the cycle.
Different Methods for Fraud Detection
The most commonly used methods in fraud analytics are:
Sampling considers only a small population and is usually more effective when a large population of data is present. Although it is a significant process in fraud detection, its disadvantage is that it might not effectively detect fraud since it analyzes only a small portion of the data. In an ideal scenario, all transactions should be considered for fraud detection.
In this method, a hypothesis is used to test transactions and determine whether there are chances of fraudulent activities occurring. Based on the result, the events can be investigated further.
3. Repetitive Analysis
Also referred to as Competitive Analysis, repetitive analysis involves writing scripts that sift through a large volume of data to pinpoint the fraudulent events that occur over some time. While the script runs continuously, it can be set to provide periodic notifications about fraud, thereby making the process more consistent and efficient.
4. Analytics Techniques
This method focuses on identifying the anomalies to detect fraud. It includes detecting values that exceed standard deviation averages, besides an analysis of high and low values to detect abnormalities, which often indicate the likelihood of fraud. Another method is to group the data based on specific criteria such as geographical location of events.
Flexible Way to Build Fraud Detection Solutions
A dependable fraud detection framework should include the following:
1. Perform SWOT (Strengths, Weaknesses, Opportunities, and Threats)
Before implementing fraud analytics solutions, the organization should analyze its strengths and weaknesses so that a suitable fraud detection program can be devised to suit its specific requirements.
2. Build a dedicated fraud management team
To ensure a smooth flow in the fraud detection process, the organization should have a dedicated team that works on detecting and preventing frauds, including proper reporting of fraudulent events when they occur.
3. Outline relevant business rules
Since there are different types of fraudulent activities, some of which are specific to a kind of industry, it is essential for the organization to set up clear business rules with the help of experts and after researching existing resources and processes. Having well-defined rules also makes it easier for an external vendor to build a robust solution for detecting and preventing fraud.
4. Clean data
The existing data should be sifted through to delete irrelevant inputs or information. Furthermore, the data across the various databases in the organization should be integrated.
5. Setting the threshold
The significance of a threshold is that it sets boundary values that help to detect anomalies. Irrespective of whether the fraud analytics solution is built in-house or by an external vendor, the boundaries should not be set too high to prevent fraudulent events slipping through the gap. Similarly, if the limits are too low, it can result in time and resources being wasted on unnecessary tasks.
6. Predictive Modelling
Models are built using data mining tools that assign fraud propensity scores linked to unidentified metrics. Based on this, the scoring takes place automatically and presents results for review and analysis.
7. Using SNA (Social Network Analysis)
By analyzing the relationships between various entities within the organization as well as externally, the use of SNA helps to strengthen the fraud detection program, making it more effective.
Fraud Detection Use Cases: Industry-specific
The essence of fraud detection lies in flagging anomalies. Instead of wasting time and resources on identifying fraudulent services provided by clinicians, medical consultants or other parties, with the use of data science, machine learning and AI, fraud analytics can improve access to emergency or chronic care.
The insurance industry loses billions every year due to fraudulent activity, and the use of AI for fraud detection in the insurance sector can improve efficiency and increase value gains significantly. For instance, it can be used to identify hard fraud (caused by staged accidents) or soft fraud (embezzlement), both of which affect the insurer’s revenues.
Whether it’s bad checks or insider threats, fraud analytics helps to find patterns that show deviation from normal behavior within datasets. The use of multiple sets and types of data allow banks and financial organizations to identify contextual or collective anomalies that result in fraud.
While every organization understands the significance of having an effective fraud detection system, it needs to be efficient so that it does not flag legitimate activity by customers. The use of fraud detection models and machine learning can help in more accurate detection of anomalies as well as more efficient scoring to reduce the number of false alarms.
At TechAhead, our team of AI experts have extensive experience in developing DSS tools and models for healthcare, insurance and banking and financial services organizations.