Claims Fraud Network Analysis

The world today has become quite impertinent and malicious. People do not flinch committing heinous crimes and the sad part is, most of us have come to terms with it. Social media outrages and agitation are transient and thus, most criminals( no matter how big or small) get away with different kinds of transgressions. One such kind of misdeed about the business sector, is Claims Fraud, especially in the insurance industry.

Meaning:

Insurance today is considered both as a form of security and investment. It gives a sense of assurance to its client- the courage to mitigate unforeseen mayhem in life. But with the influx of fraudulent activities and felony across various industries, the insurance sector stands to be no exception. One of the ways that miscreants try to get money from insurance companies is through Insurance Claims Fraud.

Insurance Claim Frauds may be defined as the act of wilful deception or creating a hoax, to secure unlawful or unfair gain, mostly pecuniary benefits. These are false insurance claims filed with fraudulent intention towards the insurance provider. It is said that insurance fraud has existed whenever insurance policies are curated, taking different forms, to suit the economic scenario. Fraudulent claims account for a substantial portion of all claims received by insurers and cost billions of dollars annually. These, in turn, affect the lives of innocent people, both directly through accidental or intentional injury or damage, and indirectly as these crimes lead to the higher insurance premium, posing as an unjust practice towards the innocent masses. Fraud drains profit and also puts a company at a competitive disadvantage.

Types of Frauds

With the rise losses and costs, detecting and preventing fraud has been consistently ranked among the top three investment and strategic priorities for insurance executives, at the time of formulating various insurance policies. They type of such fraudulent activity can broadly be divided into two types:

• Opportunistic fraud :

It is usually perpetrated by an individual who simply has a chance to exaggerate his claim or may have window-dressed his estimate for losses and repairs to the company. It is common practice, where the claimant demands an inflated amount of money for damages, while the real value stands marginal. Such frauds are very convenient and most people believe to have gotten away with the same. The miscreant, sometimes, may have some inside information, which helps him or her to fabricate incidents convincingly and thus earn easy money.

• Professional Fraud:

Such kinds of frauds are carried out by organized groups with multiple, false identities, targeting multiple organizations or brands. They are seasoned criminals, who are aware of the loopholes of the fraud detection systems and work on the same, to curate plans to remain, just below the radar. These crime rings often place or groom outsiders, to help in the intrusion, through several company channels. It is also speculated that these criminals know the fraud detection systems and they routinely check thresholds, to determine the extent of their malpractice. They usually aim for bigger clients, with high net-worth or valuation in the market.

Techniques of deterring Claim Frauds :

With the exacerbation of unfair and fraudulent claims in the insurance industry, the insurers are required to become resourceful and inventive, to deter these criminals and discourage their motives. By using a combination of approaches-and by exploiting the advantages of analytics-based techniques, it is possible to detect these claim fraud networks, to recognize their deceptive motives and thwart their plan. Some of these techniques can be described as under:

1. Business Rules and Data-Based Searching :

It is a mechanism under which each transaction is been tested against a predefined set of algorithms or business rules and policies to address any known type of fraud based on specific patterns of activity. These systems flag any claims that look suspicious to their aggregate scores or relation to threshold values. Claims that have been flagged can be investigated and reviewed using database searching and it also provides for third party database searching, to learn the criminal history of the claimant flagged, as to whether he is on the hotlist or not. Data-Based Searching is a simplistic approach and can be seen as an auto claim management software.

2. Anomaly Detection:

Also known as outlier analysis, anomaly detection is a step in data mining that identifies various data points, events, and observations that deviate from a dataset's normal behavior and thereby detect outliers of the same. This analytical tool is very helpful for fraudulent claims management. With this, Key Performance Indicators (KPIs) associated with tasks or events are baselined and thresholds are set. When a threshold for a particular measure is exceeded, then the event is reported. This in turn helps in predicting unknown patterns or fraud. It is a simplistic claim fraud management solution, as it is easy to implement and easy to evaluate individual performance to identify problems.

3. Test Mining:

Test Mining is an analytical tool which can be used as an auto claim management software, that can be used to process large volumes of text-based information such as adjuster notes, customer service calls, claimant interview, etc- in short, unstructured text, then process into meaningful data and analyze the newly created data to gain a deeper understanding of the claim. A newly added feature of this tool is the ability to analyze the huge amount of data available within social media like Facebook, YouTube, etc, for discriminating evidence against the claimant. Thus help if effectively mining and analysis of unstructured data in meaningful ways.

4. Social Network Analysis :

A contemporary approach for the claims management system of insurance, which combines the hybrid approach of the analytical method, is Social Network Analysis (SNA). The hybrid approach includes organizational business rules, statistical models, pattern analysis, and network linkage analysis to uncover a large amount of data to show relationships via links. When one looks for fraud in link analysis, one looks for clusters and how these clusters link into other clusters. Public records such as judgments, criminal records, address change frequency, etc. can be integrated into a single model and thus ease the process of analysis.

5. Predictive Analytics for Big Data:

Predictive Analytics include the use of text analytics and sentiment analysis to look at big data for fraud detection. Claim reports span across multiple pages, leaving little room for text analytics to detect scam easily. Big data analysis helps in mining through various unstructured texts and helps proactively detect frauds and other foul practices. An important point to note here is that people who usually commit frauds alter their stories over time. This fraud detection system can spot such discrepancies, using text analytics and sentimental analysis.

It has been pertinent for various insurance companies and businesses to exploit the existing technologies at their disposal and use various claim management tools to effectively manage, detect, and report frauds. It is time, they invested in technologies to prevent claims fraud, before it reaches epidemic proportions.