How Insurers Are Using Technology to Deal with Insurance Claims Fraud

 This post details the growing menace of insurance claims fraud. It is a problem that nearly all insurers are battling, but with limited success. The fact that fraudsters are always a step ahead by misusing technology to achieve their nefarious deeds makes it even more difficult to manage the problem. Technology is the only way of mitigating the instances of claims fraud and empowering insurers to deliver their services more effectively.

Insurance claims fraud is an area of serious concern for insurance providers, as the instances of such scams are proliferating globally. Scammers indulge in inflating claims by manipulating facts and making a pile of cash illegally. It forces insurance companies to invest heavily in system upgrades, technology, and manpower to proactively detect such fraud attempts and mitigate potential losses.

The Challenges:

Insurance providers have been relying primarily on the judgment of agents and industry experts to detect instances of frauds. While this was a reliable method in the past as there were limited number of cases, in the modern world it is impossible to keep a track on fraud using human resources.

• It is virtually impossible to scrutinize all claims manually.

• Fraudsters are using newer and more sophisticated ways of perpetrating such crimes.

• This has entailed the need to use technology to deal with this growing and serious crime.

The rise of technology such as data analytics has created a world of endless potential for organizations operating in the insurance sector. While cost concerns and regulations have been the reasons for slow adoption of data analytics by the insurance sector in the past, all that is rapidly changing because of the intense pressure on the industry to cut down the colossal losses caused by claims fraud.

There is a lot of regulatory importance placed on protection of personal information, with new layers of security being added regularly. This has made it extremely tough for insurance companies to access data from various sources for fraud predictions. Data analytics has proved to be a game-changer for the industry and is helping insurers manage claims fraud instances better.

Fraud Analytics Software

Insurers are incorporating insurance fraud analytics software into their business processes to detect fraudulent practices and detect such incidents early and proactively.

Fraud analytics software systems can reduce claims fraud by gathering data from various sources and collating it into meaningful and valuable information. Predictive capabilities of the software can help companies apply the process to a large area of business operations and improve fraud detection significantly. The software can be used for:

• Identifying optimal risk level

• Gaining qualitative insights from data

• Mitigate fraud risk at insurer’s end

• Eliminating fraud risk at the agent’s end

• Moving from manual to automated underwriting

• Establishing accepted limits of risks

• Automating procedures to identify risk assessment for determining various factors such as coverage, profitability and others

Understanding Legacy Systems

One of the biggest challenges of adopting analytics software is the need to upgrade systems to the latest versions. With technology evolving rapidly and continuously, this might entail a fairly substantial investment. Also, there are concerns around use of a third-party service or software because of privacy protection issues. Insurance companies might not have absolute control over data which can result in significant liability.

Insurance Fraud Detection Solutions:

Advanced claims fraud detection solutions are driven by technology. It works in the following ways:

Detection of Anomalies:

Multiple metrics are created to compare the behavior of various entities. The system makes use of statistical analysis to detect any anomalies that deviate from the normal behavior patterns of agents and customers.

Analysis of Claim Notes

Analytics can help in extracting information by going beyond structured data. In simple terms, it means that it can pull data about circumstances of a specific incident, the parties involved, the damage caused, treatment costs and other related data vital for claims settlement.

Investigation of Anomalies:

Agents and customers pointed out as anomalous on more than one count are singled out and detailed analysis carried out to find out the reasons for the anomalies. Business rules can be set up to prevent misuse and future frauds.

Conclusion

Insurance frauds are generally carried out in a very organized manner across the globe. In the modern world, it will be difficult to detect them without using sophisticated analysis methods. Insurers must leverage newer technologies to prevent, detect, and filter frauds. This will help improve claim adjustment expenses and improve the efficiency and performance of the industry.

How Insurers Are Using Technology to Deal with Insurance Claims Fraud

 This post details the growing menace of insurance claims fraud. It is a problem that nearly all insurers are battling, but with limited success. The fact that fraudsters are always a step ahead by misusing technology to achieve their nefarious deeds makes it even more difficult to manage the problem. Technology is the only way of mitigating the instances of claims fraud and empowering insurers to deliver their services more effectively.

Insurance claims fraud is an area of serious concern for insurance providers, as the instances of such scams are proliferating globally. Scammers indulge in inflating claims by manipulating facts and making a pile of cash illegally. It forces insurance companies to invest heavily in system upgrades, technology, and manpower to proactively detect such fraud attempts and mitigate potential losses.

The Challenges: 

Insurance providers have been relying primarily on the judgment of agents and industry experts to detect instances of frauds. While this was a reliable method in the past as there were limited number of cases, in the modern world it is impossible to keep a track on fraud using human resources.

• It is virtually impossible to scrutinize all claims manually.
• Fraudsters are using newer and more sophisticated ways of perpetrating such crimes.
• This has entailed the need to use technology to deal with this growing and serious crime.

 

The rise of technology such as data analytics has created a world of endless potential for organizations operating in the insurance sector. While cost concerns and regulations have been the reasons for slow adoption of data analytics by the insurance sector in the past, all that is rapidly changing because of the intense pressure on the industry to cut down the colossal losses caused by claims fraud.

There is a lot of regulatory importance placed on protection of personal information, with new layers of security being added regularly. This has made it extremely tough for insurance companies to access data from various sources for fraud predictions. Data analytics has proved to be a game-changer for the industry and is helping insurers manage claims fraud instances better.

Fraud Analytics Software

Insurers are incorporating insurance fraud analytics software into their business processes to detect fraudulent practices and detect such incidents early and proactively.

Fraud analytics software systems can reduce claims fraud by gathering data from various sources and collating it into meaningful and valuable information. Predictive capabilities of the software can help companies apply the process to a large area of business operations and improve fraud detection significantly. The software can be used for:

• Identifying optimal risk level
• Gaining qualitative insights from data
• Mitigate fraud risk at insurer’s end
• Eliminating fraud risk at the agent’s end
• Moving from manual to automated underwriting
• Establishing accepted limits of risks
• Automating procedures to identify risk assessment for determining various factors such as coverage, profitability and others

 

Understanding Legacy Systems

One of the biggest challenges of adopting analytics software is the need to upgrade systems to the latest versions. With technology evolving rapidly and continuously, this might entail a fairly substantial investment. Also, there are concerns around use of a third-party service or software because of privacy protection issues. Insurance companies might not have absolute control over data which can result in significant liability.

Insurance Fraud Detection Solutions:

Advanced claims fraud detection solutions are driven by technology. It works in the following ways:

Detection of Anomalies:

Multiple metrics are created to compare the behavior of various entities. The system makes use of statistical analysis to detect any anomalies that deviate from the normal behavior patterns of agents and customers.

Analysis of Claim Notes

Analytics can help in extracting information by going beyond structured data. In simple terms, it means that it can pull data about circumstances of a specific incident, the parties involved, the damage caused, treatment costs and other related data vital for claims settlement.

Investigation of Anomalies:

Agents and customers pointed out as anomalous on more than one count are singled out and detailed analysis carried out to find out the reasons for the anomalies. Business rules can be set up to prevent misuse and future frauds.

Conclusion

Insurance frauds are generally carried out in a very organized manner across the globe. In the modern world, it will be difficult to detect them without using sophisticated analysis methods. Insurers must leverage newer technologies to prevent, detect, and filter frauds. This will help improve claim adjustment expenses and improve the efficiency and performance of the industry.

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.