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How to Become a Fraudster in 14 Days
What is fraud and how to deal with it
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On average, fraudulent activities contribute to approximately 9-10% of the total billing cost within the voluntary health insurance (VHI) system. While most clinics are known for providing good and responsible healthcare, sometimes the desire for extra money can lead to bad behavior by healthcare providers. This might include charging too much for services or even trying to trick insurance companies. In this article, we're going to look at how some healthcare providers (HCP) can end up doing things that hurt the health insurance system, which many people rely on.
publication date:
August 15, 2023
Tags:
Health Insurance
Trends
Fraud
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Health Insurance
Trends
Fraud
Let's start with the definition of fraud. In this article, fraud refers to intentional systematic deception or distortion of information with the aim of obtaining payment for services from an insurance company. One key thing about fraud is that it's not just a one-time thing – it involves a repeating pattern of behavior over time.
The share of unjustified increases in the cost of medical services in established VHI markets may increase during periods of socio-economic turbulence, making this problem more acute.
The share of unjustified increases in the cost of medical services in established VHI markets may increase during periods of socio-economic turbulence, making this problem more acute.
What is fraud?
Duplicating bills
Provision of services not covered by the insurance policy
Adding additional diagnoses or intentionally assigning a more serious diagnosis to justify more expensive tests
Duplication of services or splitting comprehensive services into more expensive individual ones
Prescribing an extended treatment plan that does not correspond to the standard
Inclusion of services in bills that were not actually provided to the insured
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Yes. Insurance companies can easily identify standard fraudulent schemes, such as:
Can fraud be detected?
Such patterns are reliably identified by medical experts or can be determined using relatively simple mathematical models.
The challenge of detecting advanced fraud lies in the mimicry of invoices submitted to insurance companies to resemble plausible medical histories. These fake bills are designed to closely resemble genuine medical records. When the list of services on the bill mimics the ones that the insurance policy actually covers based on the person's diagnosis, it becomes much harder for the insurance company to reject the payment.
The challenge of detecting advanced fraud lies in the mimicry of invoices submitted to insurance companies to resemble plausible medical histories. These fake bills are designed to closely resemble genuine medical records. When the list of services on the bill mimics the ones that the insurance policy actually covers based on the person's diagnosis, it becomes much harder for the insurance company to reject the payment.
pic. Where’s Wolly Fraud?
Fraud weakness
However, despite the similarity of mechanisms between fraud and mimicry as evolutionary phenomena in nature, their goals are different. The evolutionary goal of mimicry is to maintain the accuracy of imitation for the longest possible time. The goal of fraud is to achieve the highest possible quick income, as over time, fraud detection methods improve, and the risk of discovering unacceptable behavior and corresponding penalties increases.
Detecting fraud statistically relies on at least two key factors:
Detecting fraud statistically relies on at least two key factors:
- a large number of bills, which enables the recognition of diverse trends in how different clinics offer medical services;
- the extent to which the costs of services exceed reasonable limits, which provides a financial incentive for fraudulent actions.
Periodic alignment of mutual document workflow practices between the insurance company and the HCPs to minimize technical errors that may create opportunities for "disguising" fraud
Pre-approval of services by the insurance company
Data analysis and application of machine learning to identify anomalous and suspicious patterns
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Regular collection of feedback from the insured about types, quantities, and dates of services rendered
Auditing (internal and external): regular selective and comprehensive expert medical checks to confirm or alter the level of trust in the administration of the HCPs
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There are several main ways to combat fraud:
Main methods of combating the fraud
Our experience in detecting fraud in the bills of several major insurance companies over the past few years has allowed the identification of several dozen behavioral patterns that serve as an indicator of fraud. Some patterns are characteristic of many HCPs regardless of the country, but some are specific, as they are based on the exploitation of medical information systems and practices.
An example of a common pattern involves an insured individual consistently visiting the same healthcare provider's office at fixed intervals, such as two visits every 14 days.
For instance, such a pattern was identified in one of the large polyclinics - over the course of 1 year, over 300 insured individuals were treated using the scheme: 2 visits every 2 weeks. On its own, such a treatment scheme is not suspicious, especially if telemedicine services were provided, or medication was prescribed for 2 weeks along with a doctor's consultation for the continuation of medical treatment and provision of medication for treatment completion. However, if in the clinics of the region the percentage of such treatment schemes was 0.9%, then in the considered clinic, these treatment schemes were applied to 19% of the insured.
An example of a common pattern involves an insured individual consistently visiting the same healthcare provider's office at fixed intervals, such as two visits every 14 days.
For instance, such a pattern was identified in one of the large polyclinics - over the course of 1 year, over 300 insured individuals were treated using the scheme: 2 visits every 2 weeks. On its own, such a treatment scheme is not suspicious, especially if telemedicine services were provided, or medication was prescribed for 2 weeks along with a doctor's consultation for the continuation of medical treatment and provision of medication for treatment completion. However, if in the clinics of the region the percentage of such treatment schemes was 0.9%, then in the considered clinic, these treatment schemes were applied to 19% of the insured.
Case of Example of detected sophisticated fraud detection
A more detailed examination of the services and prescriptions provided to the insured revealed that the vast majority of services on both days were the same and even included radiological studies, which is not only excessive from a medical standpoint but also does not provide additional information for the doctor, or several trade names of drugs that are excluded for simultaneous use.
Direction of such statistical anomalies to the insurance company and further expert verification confirmed the illegitimacy of prescriptions in 73% of the cases detected.
And this is just one rather simple pattern. In the process of working with historical data, various fraud schemes and even combinations of patterns are revealed.
In the upcoming articles, we will describe fraud patterns specific to various European, Middle Eastern, and South American countries.
Direction of such statistical anomalies to the insurance company and further expert verification confirmed the illegitimacy of prescriptions in 73% of the cases detected.
And this is just one rather simple pattern. In the process of working with historical data, various fraud schemes and even combinations of patterns are revealed.
In the upcoming articles, we will describe fraud patterns specific to various European, Middle Eastern, and South American countries.
Given the high combined ratios of insurers approaching 100% and the already extensively employed cost-cutting methods, combating fraud can provide additional resources for the development of the insurance business.
Thus, the search for such statistical anomalies leads to the possibility of a more detailed study of treatment patterns, identifying their connection with clinics, specific doctors, and diagnoses to identify new patterns. This is a rather painstaking and complex but accessible and effective way to identify fraud, especially when medical expertise is available to generate patterns and verify search results.
The most effective approach is the search for fraud within aggregated data from various insurance companies in different countries, aiming to identify and formalize diverse patterns of fraudulent activities.
Thus, the search for such statistical anomalies leads to the possibility of a more detailed study of treatment patterns, identifying their connection with clinics, specific doctors, and diagnoses to identify new patterns. This is a rather painstaking and complex but accessible and effective way to identify fraud, especially when medical expertise is available to generate patterns and verify search results.
The most effective approach is the search for fraud within aggregated data from various insurance companies in different countries, aiming to identify and formalize diverse patterns of fraudulent activities.
Prospects of combating fraud
In the next article you will find more practical insights to the fraud behavior.
