Insurance fraud and the power of prediction

Insurance fraud.

It has always been a risk for insurance providers, but never like it is today. According to the FBI, fraudulent activity continues to skyrocket, reaching a total cost to the industry of more than $40 billion in 2023.¹

Defined as a deliberate act or omission intended to obtain an unjust advantage or financial gain from an insurance policy, insurance fraud affects not only providers, but policyholders, and the economy. This article will explore the various forms of insurance fraud, its consequences, preventive measures, and the benefits that predictive analytics brings in helping insurance companies more effectively manage fraud risk.

Understanding types of insurance fraud

Insurance fraud encompasses a wide range of deceptive activities committed by policyholders, insurance professionals such as an agent or adjustor, and sometimes, organized fraud rings. The most common types include:

• False claims: The deliberate fabrication or exaggeration of events, such as accidents, thefts, or damages, with the intention to file fraudulent insurance claims.

• Staged accidents: Orchestrating fraudulent accidents to make insurance claims, often involving multiple parties who collaborate to deceive insurers.

• Premium fraud: Intentionally providing false information when applying for insurance coverage to obtain lower premiums or coverage for risks not disclosed.

• Arson and property damage: Deliberate destruction of property, including arson, to collect insurance payouts.

What are some of the factors contributing to the rise in fraud? For starters, insurers are currently faced with a record number of claims, from auto and worker’s comp to health, P&C, and catastrophic climate events. At the same time, they are rushing to meet the expectations of an increasingly demanding customer with faster, more personalized service.

In order to meet the rising expectations of digitally-savvy consumers, along with a record number of claims, insurers are forced to rely upon legacy claims management processes … as well as massive amounts of unsecured data, siloed workflows, and a verification process that is inadequate at best. The result is an exponential rise in fraud and with it, loss costs that have reached historic highs — costs that have a significant impact on both the firm’s bottom line and the consumer’s annual premium. In fact, loss costs in 2022 were $30 billion higher than the previous year.³

The critical role of technology in fraud mitigation

One of the top priorities for insurers when it comes to processing claims is the ability to quickly identify and halt the payment of any claim deemed fraudulent. The battle against fraud can be fought on a variety of fronts, utilizing a wide range of tactics; from collaboration with law enforcement and legislative measures to public awareness and technology. The most effective, by far, is technology — more specifically, artificial intelligence (AI)-powered predictive analytics.

What is predictive analytics?

Predictive analytics is not new to insurers. In fact, insurance companies have been relying on it for ages. The difference is that data analysts, who are still critical in directing the processes — with the help of AI and machine learning (ML) statistics and data mining — need no longer rely on vast amounts of unverified, unsecured, historical data and time-consuming, manual, error-prone processes.

Instead, they can now use those mountains of data — thanks to the power of AI-driven analytics — to gain valuable insights. These insights, in turn, translate into better-informed decisions, which then lead to the early identification and prevention of fraudulent activities, minimized risk and maximized profit.

How predictive analytics works

1. Identifying fraud patterns

Predictive analytics enables insurance companies to detect fraud by analyzing patterns and anomalies within their data that may indicate suspicious activity. By structuring and examining historical claims data, customer information, policy details, and external data sources, predictive models can detect patterns and connections — such as connections between individuals, policies, and claims — that are not readily evident to human analysts. As a result, anomalies such as an unusual frequency of claims, claims of unusually high value, inconsistencies in medical reports, or repeated occurrences of certain fraudulent schemes can be identified and flagged for further investigation.

2. Fraud scoring and risk assessment

Analysts are now able to assign fraud scores or risk assessments to policyholders, claims, or other entities within their organization’s ecosystem. By analyzing historical fraud patterns and correlating them with specific attributes or variables — much more efficiently with predictive analytics — insurers can predict the likelihood of fraud for a given case. Fraud scoring facilitates the prioritization of cases based on risk, which enables firms to allocate investigative resources more effectively and reduce the amount of manual effort required to identify potential fraudsters.

3. Real-time fraud monitoring and alerts

Real-time fraud alerts are another powerful application of predictive analytics. With all transactions and incoming data streams — such as claims, policy changes, or customer interactions — monitored in real-time, anomalies can be identified during the submission and review process. When anomalies are detected, automated alerts can be triggered, notifying investigators, claims handlers, or fraud detection teams to take immediate action. This proactive approach enables insurers to respond swiftly and promptly investigate suspicious claims.

4. Network analysis

Fraudsters often operate in networks, colluding with other individuals or organizations. Predictive analytics can leverage network analysis techniques to uncover these complex relationships and identify fraudulent networks. By examining the connections between policyholders, healthcare providers, legal entities, and other relevant stakeholders, analysts can uncover hidden patterns, identify suspicious clusters, and trace the flow of fraudulent activities. This network-based approach enhances fraud detection and enables insurers to identify and disrupt organized fraud networks quickly and efficiently.

5. Continuous learning, monitoring, and adapting

Like any cyber thief, insurance fraudsters continuously evolve their strategies and methods, continually adapting to current detection methods and exploiting vulnerabilities. Predictive analytics enables insurers to keep pace with these changing tactics by continuously monitoring patterns and updating models. By leveraging machine learning algorithms, predictive models adapt and improve over time, enhancing the insurer’s ability to stay ahead of emerging fraud threats.

6. Enhancing risk assessment

Predictive analytics improves an insurer’s ability to manage risk by analyzing various risk factors associated with policyholders, such as age, location, occupation, and claim history. Insurers can then develop risk profiles and assign each policyholder a risk score, which can help identify those who are more likely to engage in fraudulent activities. By quickly identifying and focusing their resources on high-risk policyholders, insurers are better prepared to address suspicious activities in a timely manner while making more efficient use of resources.

Prediction keeps insurers a step ahead

Insurance companies face significant challenges in combating insurance fraud, which can result in substantial financial losses and damage to their reputation. By harnessing the power of AI and predictive analytics, insurers can enhance their fraud prevention capabilities — from suspicious pattern identification and risk assessment, to real-time monitoring and network analysis.

As technology continues to advance, insurers must continue to invest in the AI-powered solutions, such as predictive analytics, that can enhance their ability to manage risk, ensure the security of their data, and facilitate an enhanced customer experience.

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