With technological advances over the past decade, insurers have made strides in implementing technology to pay claims for the good guys faster while deterring opportunistic fraud. Although fraud technology used at first notice of loss (FNOL) has made it increasingly difficult for individuals to commit insurance fraud, the industry has a long way to go.

There are several challenges with the technology used, including straight-through processing (STP), Natural Language Processing (NLP), and the use of Personally Identifiable Information (PII) for insurers to overcome.

In this article, we’ll cover how the advances in risk assessment tools that use voice analytics have helped insurers overcome these challenges.

What is Voice Analytics? 

Voice analytics in insurance is a rapidly emerging technology that is transforming the way insurance companies operate and interact with their customers. This innovative tool leverages advanced speech recognition and natural language processing algorithms to analyze customer interactions, particularly those that occur over the phone.

By scrutinizing these conversations, voice analytics can help insurance companies gain valuable insights into customer sentiment, needs, and preferences. It enables insurers to assess the effectiveness of their customer service representatives, identify areas for improvement, and even detect potential fraud or compliance violations through voice pattern analysis.

Find out how voice analytics assists insurance tools. 

#1 – Enabling Straight Through Processing

One of the advances insurers have embraced to improve the claims journey is straight-through processing. STP enables the insurer’s policy, claims, and payment systems to process claims automatically without requiring manual intervention or human input.

For STP to work efficiently, claims triage has to start at FNOL, accurately identifying legitimate claims versus those that are questionable and require additional research. And to meet customer expectations, your claims need to be cleared as efficiently as possible at the moment that matters: when your customer needs your support.

But according to Insurance Thought Leadership [1], only 40% of insurers have made strides in implementing STP to pay legitimate claims faster while deterring opportunistic fraud. And on average, fewer than 10% of claims are processed straight through in any line. It’s most common in personal lines and (for payouts) in annuities.

One of the reasons for the slow adoption of STP by insurers is that the risks must be well understood for STP to work in a claims environment. Many insurers have turned to Natural Language Processing (NLP) to understand these risks and reduce fraudulent claims. The challenge is that NLP systems require a large amount of data to be accessible and available. The more data analyzed, the greater the accuracy of identifying fraud patterns.

By contrast, with an accuracy rate greater than 97%, the addition of voice analytics through simple, automated questionnaires enables insurers to understand the risks well.

This new approach to voice analytics does not require the vast amount of data NLP systems need to identify legitimate and fraudulent claims at FNOL.

#2 – Identifying Legitimate Claims at FNOL

By introducing voice-based questionnaires at FNOL, insurers can assess and action legitimate claims with very high accuracy. These questionnaires consist of a few simple yes/no questions, such as:

“Do you know where the lost item is now?”

“Were you on a mobile phone when the incident occurred?”

“Were there any passengers in your vehicle at the time of the accident?”

Based on the responses, the risk is assessed and an overall risk score is provided. STP systems use this score to identify legitimate claims at FNOL and move them forward.

#3 – Deflecting Opportunistic Fraudulent Claims; Before They Are Submitted

Logically, the best way to reduce opportunistic fraudulent claims is to discourage individuals from submitting them in the first place. 

At the same time, individuals would not even consider submitting opportunistic claims if their chances of being caught were nearly guaranteed. When customers were asked to respond to a voice-based questionnaire in a recent pilot with a personal lines auto insurer, there was a substantial increase in claims withdrawals.

The questionnaire can be as simple as asking the individual submitting the claim, “Did you purposely provide any false or misleading information related to this claim?”

#4 – Flagging High-Risk Claims

For individuals that a voice-based questionnaire may not deter, there’s another hurdle to jump through: the SIU becoming involved sooner than expected.

By adding three simple questions in a voice-based questionnaire at FNOL, high-risk claims are identified earlier in the claims experience process. This triggers the SIU to become involved earlier in the process than expected.

In another recent pilot, an auto insurer experienced an increased claim withdrawal rate when the SIU became involved quickly, resulting in significant savings. 

How Does This Affect Customer Experience?

Customers are very concerned about insurance fraud.

According to the Coalition Against Insurance Fraud, “insurance fraud is one of America’s largest crimes — at least $308.6 billion is stolen each year.”

Most consumers understand that the $308.6B stolen yearly increases their premiums and slows their carrier’s ability to pay legitimate claims. But insurers continue to be under pressure from consumers and regulators to pay legitimate claims faster while eliminating fraudulent claims at the same time.

Insurers can identify fraudulent claims using the customer’s personal data or Personally Identifiable Information (PII). However, not all customers are comfortable with insurers using their personal data to identify fraudulent claims.

#5 – Avoiding the Challenges of Using Personally Identifiable Information

According to Coalition Against Insurance Fraud report, The Ethical Use of Data to Fight Insurance Fraud Study, “When asked about their level of concern with insurance fraud and how their data is used to fight fraud, an amazing 84% of respondents said they are either “very concerned” or “concerned” about these issues.”

Voice-based questionnaires do not require the use of PII data, side-stepping this issue entirely.

An auto insurer recently piloted a voice-based questionnaire that received a 5-star customer satisfaction rating from all insureds who went through the process, while another insurer’s pilot has reduced claim payment times by 4.5x.

Learn more about insurance trends with Clearspeed!

Let’s Pay the Good Guys

Each category in the voice analytics ecosystem offers value to enterprises. Your opportunity is to design your organization’s portfolio of voice technologies to increase security, optimize operations, and improve customer experience.

Don’t Make Yourself an Easy Target

Opportunistic insurance fraud is tempting as it can appear as easy money. But most individuals will follow the least path of resistance.

The advances in voice technology have proven to be an effective deterrent for opportunistic fraud, forcing individuals to move on to easier targets for committing insurance fraud.

Let’s turn the tables on opportunistic fraud by forcing them to determine who the high-risk carriers are for submitting fraudulent claims.

To learn more about how Clearspeed voice analytics can integrate with other voice technologies and your existing infrastructure, request a demo today.

If you would like to know more about voice analytics enhances the claims process, request a voice analytics demo today.