Filed Under:Carrier Innovations, Analytics & Data

Software robots: New members of the fraud investigation workforce

Using technology to augment human efforts

Software robots can do routine data mining more efficiently, allowing humans to concentrate on more important aspects of fraud investigations. (Photo: Shutterstock)
Software robots can do routine data mining more efficiently, allowing humans to concentrate on more important aspects of fraud investigations. (Photo: Shutterstock)

Fraudulent insurance claims — the hits just keep on coming, with more than 60% of insurers experiencing increases in such claims between 2013 and 2016, according to research released last year by the Coalition Against Insurance Fraud. In September 2017, the Insurance Information Institute pegged the cost of fraudulent property and casualty insurance claims at $34 billion annually.

In addition to human resources to combat the problem, insurers are taking a different tack by adding software robots (also known as robot-processed automation, or RPA, solutions) to their fraud investigation workforce. These solutions automate the process of sifting through personal and other data found online to substantiate or disprove the fraudulent nature of any claim. These are just a few of the benefits of RPA solutions:

  • Facilitating more thorough investigations of potentially fraudulent activities, at a lower cost. Unlike human investigators, software robots are relentless by nature. They can rapidly troll the internet for data that may point to fraud—including the ever-increasing volume of personal data now being shared on social media outlets such as Facebook and on collaboration platforms like Slack. The time and effort required to handle these tasks, as well as the cost, pale in comparison to the price of employing human investigators to tackle them.
    Even more importantly, software robots’ use of algorithms in mining data for correlations allows them to identify patterns and other indicators of fraud not typically discernible by human investigators. RPA technology becomes increasingly better at drawing correlations with end-user training. When machine learning is incorporated with software robots, the sheer volume of data collected and the intricacy of patterns identified is overwhelming—in a good way.

Related: Capturing insurance’s most wanted fraudsters

  • Freeing up valuable resources to handle more complex cases. Software robots can uncover most of the so-called “grunt work” pertaining to straightforward claims—i.e., searches for policyholders’ social media data and other information about potentially suspicious activity that may appear online. We see RPA usage moving in this direction, as it frees up investigators to focus on higher-profile cases and to follow up on or substantiate any instincts they may have about them.

Related: How AI and automation can transform the claims process

  • Reducing the overall incidence of fraudulent claims. Certain individuals do not see the harm in “plain vanilla” insurance fraud, believing it is a victimless crime. However, policyholders pay the price of fraudulent claims activity in the form of increased premiums. Other incidences of insurance fraud are linked to financial crimes and terrorist activities. RPA makes the activities of perpetrators in both camps easier to pinpoint and more difficult to conceal, potentially dissuading them from pursuing false insurance claims altogether.

Related: How insurers can identify fraudulent claims before they are paid

Best practices: Bigger bang for the RPA buck


Software robots clearly rank as a valuable tool that insurers can leverage when investigating and helping to mitigate fraud. However, applying best practices in their use is imperative to maximize investment in the technology. Read some of our best practice suggestions below:

  • Don’t expect “out-of-the-box” functionality. RPA is very much an across-the-board technology. It has a multitude of applications within and beyond the property and casualty sectors of the insurance industry. In order to capitalize on the functionality of any RPA solution, that solution must be tailored to handle individual organizations’ particular needs—and with time, it will become even better-trained to do its assigned job or jobs. A reputable vendor will offer expert assistance from its data scientists and machine learning engineers to ensure such an outcome.

Related: 3 ways machine learning is transforming insurance

  • Start with one small project. Implementing RPA across the board in one fell swoop will prove too overwhelming for all concerned. Instead, find a smaller project—for instance, one that involves a particular kind of investigation and doesn’t cross business lines. Collect the necessary data and assess whether it is adequate for its purpose. If not, determine what information should be added and identify any other tweaks that should be made to the system.
  • Keep legal defensibility in mind. Investigations of insurance fraud frequently lead to litigation. However, unless it can be proven that data uncovered using RPA has been collected, archived, and retrieved in a tamper-proof manner, it will not be considered admissible evidence in a court of law. This, of course, defeats the purpose of investing time and money in RPA in the first place. For best results, choose an archiving solution whose configuration removes the possibility of tampering from the equation. Limit vendor selection to those that are willing to attest to how their archiving platform supports the legal defensibility of any data stored within it.

Related: Using smart technology to combat insurance fraud

  • Debunk the myth. As is true of many technology implementations, there will be concern among some employees that the launch of an RPA solution will cost them their jobs. Such fear is not unreasonable, but it is largely unfounded. While a few ineffective staff members may no longer be needed once the technology is in place, the vast majority of employees will continue to play an important role in the organization. The only difference: employees’ time and talent will be harnessed more efficiently, at a lower cost to the organization.

RPA is here to stay. Within the next 18 months, it will be the default business model for fraud investigation in the insurance arena. Organizations that fail to embrace the technology, even to a limited degree with the flexibility to branch out later, are likely to find themselves at a great disadvantage moving forward.

Related: How RPA helps insurers remain competitive

Kevin Gibson, CEO, Hanzo an engineering company that provides “defensible collection of web and social media content.”

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