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Harnessing Data Insights: How Machine Learning and Predictive Analytics Combat Insurance Fraud

Jun 15, 2023
Insurance Fraud

When insurers are asked, “What is one of the most significant challenges your industry is currently facing?” The most prevalent response heard across several insurance sectors is Claims Fraud. Fraudulent insurance claims are one of the most preventable losses that hurt insurers worldwide.

According to the Coalition Against Insurance Fraud, in the US alone, fraud steals at least $80 billion every year from American consumers. This indicates the inefficiency of current fraud detection techniques deployed by insurers.

In this article, we take a look at the challenges that insurers are facing today with fraud detection and explore the different ways in which machine learning and predictive analytics can be used to combat these frauds.

Three Types of Insurance Frauds within the P&C Insurance Lifecycle

Fraudulent activities in property and casualty insurance can take various forms, putting insurers at risk and impacting honest policyholders. From falsified claims to staged accidents, these deceitful practices undermine the integrity of the claims process. To address this, let’s understand the different types of fraud that insurers can encounter.

Fraud at Point of Sale (POS)

Occurs when the applicant purposely hides or distorts facts when obtaining insurance coverage.

Workers’ compensation is most exposed to such fraud, where employers reduce premiums by easily manipulating data such as age, previous claims, zip code, and various other factors like high-risk jobs.

Fraud at the Claims Stage

Occurs when filing or settling insurance claims. Policyholders or third parties intentionally manipulate information, provide false documentation, or engage in deceptive practices to obtain illegitimate financial benefits from the insurance company.

Fraud at the claims stage can take various forms, such as inflating the extent of damages, staging accidents or thefts, or submitting fraudulent invoices and receipts.

Fraud in Personal Lines

Occurs when an insured claims fraudulent auto injuries, which are estimated to rise in the next few years.

One such type of fraudulent activity involves a policyholder falsely reporting an incident, such as the theft of a vehicle. In such cases, the insured may choose to dispose of, conceal, or sell the car, among other actions, and subsequently file a fraudulent insurance claim.

By identifying these fraudulent schemes, insurance companies can implement targeted strategies to detect and prevent such activities, ensuring fair and reliable claims processing for all policyholders.

The Impact of Fraud

The impact of fraud in property and casualty insurance is far-reaching, affecting insurers, honest policyholders, and the industry. Here is how they may affect the system:

  1. Direct financial losses

Resulting in substantial financial losses, affecting insurers’ profitability, increasing operating costs, and reducing the availability of affordable insurance coverage.

  1. High premiums

Creating a financial strain for honest policyholders and burdening them with higher premiums as insurers bear the cost and financial losses due to these fraudulent activities.

  1. Indirect costs

Including additional indirect costs, expenses associated with investigating fraudulent claims, implementing fraud detection systems, and legal proceedings.

  1. Slow claims processing

Diverting valuable resources and attention away from legitimate claims. As insurers intensify their efforts to combat fraud, claim processing times may increase, causing delays for honest policyholders who need assistance during challenging times.

Traditional Vs Machine Learning Fraud Detection 

Traditional methods come with a demand for manual intervention, leaving high chances of human errors and longer lead times. Let’s explore some of them:

Traditional Method

With the increase in fraudulent cases, insurers need to implement advanced fraud detection techniques that efficiently analyze untapped data and help minimize fraud losses.

Machine Learning – Revolutionizing Fraud Detection 

Considering the above limitations, machine learning (ML) algorithms are designed to learn from data patterns and make predictions or decisions without being explicitly programmed. They play a pivotal role in insurance fraud detection by analyzing large volumes of data to identify suspicious patterns and anomalies.

Fraud Detection

What advantages does Machine Learning (ML) offer to assess and resolve insurance fraud?

Here are some of the benefits that ML models and algorithms offer:

  1. Fraud Pattern Recognition – Learns from historical fraud cases and identifies common patterns and indicators that signify potential fraud. These models can then be used to flag suspicious claims for further investigation.
  2. Real-time Monitoring – Identifies potential fraud cases swiftly as they occur by continuously analyzing data in real-time. This proactive approach allows insurers to take immediate action, minimizing losses and preventing fraudulent activities.
  3. Claim Predictions – Assesses claim characteristics, such as claimant demographics, past claim history, and loss severity, to predict the likelihood of fraud. This can help insurers prioritize claims for investigation, focusing resources on high-risk cases.

Predictive Analytics – Safeguarding the Insurance Industry Against Frauds

Predictive analytics uses historical data and statistical algorithms to forecast future events or behaviors. Many insurers have started adopting it to predict the occurrence of fraudulent claims early in the claims cycle. This helps them quickly and accurately determine whether or not a claim needs further investigation.

How does Predictive Analytics improve fraud detection?

In the context of insurance fraud prevention, predictive analytics offers several ways to identify fraud. Here are some of them:

  1. Anomaly Detection: Establishes standard behavior patterns to detect anomalies that deviate from the expected norms. These anomalies are red flags, enabling insurers to identify potential fraud and take appropriate action.
  2. Network Analysis: Analyzes data to identify relationships and connections between individuals, policies, and claims. This network analysis helps uncover complex fraud schemes involving multiple parties and enhances the effectiveness of fraud detection efforts.
  3. Risk Scoring: Assigns risk scores to policies and claims based on various factors. Insurers can then prioritize their investigations by focusing on high-risk cases with the highest probability of fraud.

Wrapping Up

P&C insurance carriers have been battling fraudulent claims since their commencement. With the increase in fraudulent cases, traditional fraud detection techniques are becoming less effective. The future is clear. Machine learning algorithms and predictive analytics offer underwriters, claims handlers, and investigators an advanced and statistically reliable solution to revolutionize their fraud management processes. Adopting these latest technologies may be challenging, but the result is worth it.

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