Tag Archives: fraud detection

3 Ways to Bar Fraud in Roadside Assistance

Unemployment is rising due to COVID-19, and some of the top risk management firms in the industry have indicated that fraud will also quickly increase. While claims fraud, inflated repair invoices and other common scams are probably the first that come to mind, roadside assistance fraud is another issue to which insurers should pay attention. It’s more common that one might think.

Especially as insurers increasingly offer ecosystem services such as roadside assistance to strengthen customer loyalty and generate additional revenue, it’s important that they ensure their roadside assistance partners are taking measures to protect against fraud, which can range from customers abusing the aid to get free gas, to tow operators sending fraudulent invoices. 

Here are some of the ways to protect against roadside assistance fraud:

Fast Payments Promote Trust

The adage, “An ounce of prevention is worth a pound of cure,” holds true in roadside assistance fraud. Instituting policies that reduce the incentive to commit fraud is far less painful than attempting to recover a loss, and one of these measures is to pay tow operators quickly. Especially in difficult times, those who issue payment within minutes instead of the standard Net-30 will foster loyalty that cuts down on fraud, especially while many companies are struggling to keep operations running. 

Tow operators balk at complex billing that deducts difficult-to-understand fees from their payment. The fees create unpleasant surprises that can make the difference between a profit and a loss. So, make sure that roadside assistance partners offer clear and transparent billing.

Transparency in Invoicing

With fraud on the rise, be on the lookout for roadside vendors that will attempt to bill you for “ghost” services. One way to mitigate this type of fraud is by asking your roadside vendor to provide transparency into completed services, ideally in real time. Require your roadside vendor to provide an unfiltered view into jobs, as well as customer confirmation that the job was completed. This kind of transparency makes it far less likely that you’ll be inaccurately billed or overcharged. Plus, this level of transparency gives you a better view into the customer experience. 

See also: 3 Ways AI, Telematics Revolutionize Claims  

Transparency in Operations

History tells us that, during times of high unemployment, we are likely to see more bad actors entering gig economy jobs. But fraud is often easily caught at the background check level, which can prevent bad actors from getting into the system in the first place. While it’s easy to provide a false name, it’s more difficult to provide a false Social Security number and matching drivers license. So, it’s important to have transparency in how tow operators are onboarded into your roadside assistance partner’s network and the methods they use to verify the identity of each driver and the person’s background. Ask about the types of checks they’re using to ensure identity verification, proper licensure and insurance compliance. Ideally, you want visibility all the way down to the driver level of who is servicing policyholders.

You need transparency because, while background checks have been an industry standard for years in roadside assistance, they may not be conducted at the appropriate level. For example, it’s common to accept a prior, third-party background check for a new contractor, a practice that leaves critical gaps in a contractor’s history and doesn’t necessarily report on charges or information relevant to the position. A “clear background check” usually does not tell you that the driver’s license is suspended, for instance. 

Background checks should be run annually at a minimum, but there are now next-generation background check services that will run in the background to provide live monitoring of arrest feeds, county reports and other proprietary information sites. This kind of continuous monitoring can flag events that could signal trouble, providing the opportunity to prevent fraud before it occurs.

The Importance of Analytics

Some policy holders may look to their roadside policy to help get what they see as “free fuel” as many times as possible. It’s a common scam, where drivers purposely avoid filling up and, when they run out of fuel, call the roadside assistance service to get some for free. 

This kind of fraud is most effectively detected through technology, specifically artificial intelligence, machine learning and analytics. Data analysis can identify previously overlooked trends to catch these kinds of issues and resolve them quickly. Insurers save money when machines and automation do the work instead of adding to headcount or finding problems only after the damage has been done. 

Even as fraud is anticipated to increase, roadside assistance many times has been overlooked. Don’t settle for passive fraud detection. Demand transparency and encourage the use of technology to mitigate risk, which will both reinforce your reputation and drive your bottom line.

How to Capture Data Using Social Media

Insurance carriers looking to better market and manage risks should use social media as a rich component of a robust analytics platform. By augmenting existing big data projects with social media feeds, carriers can identify key information about their insureds that would otherwise be difficult to gather in a timely manner. Social media data analytics can be a competitive advantage leading to greater sales, lower claims and increased customer satisfaction. However, insurers should be careful with the data or risk crossing the “creepy line.”

With more than one billion users on Facebook and two billion total social media users across all platforms, the data shared is immense. The data that can be extracted from social media varies by platform, but in general the information goes far beyond pure text. Social graphs describe connections and relationships; profile updates highlight life change events such as marriage and the birth of children; geolocation tags highlight travel; and continuing communication can be parsed for activities and attitude.

Modern carriers looking to leverage analytics for a competitive advantage should already have a big data capability that pulls data from policy, billing and claims systems, call center logs, portal and app usage, third party enhancement tools such as Dun and Bradstreet and other sources to build a robust picture of each insured. This data can be mined using machine learning and neural networks to identify risks that should be exited, opportunities for cross-selling and best marketing opportunities to insureds and prospects. Social media is not a replacement for this data, rather a rich addition to it. By augmenting known facts with machine processing of social data, insurers can enable a more detailed and nuanced analysis that the same analytics routines can use to further refine analysis.

See also: Should Social Media Have a Place?

Examples of enhanced capabilities with this more robust analysis include:

  • Prescriptive marketing: Asses the marketing mechanisms and messaging that will be most effective in converting the prospect to an insured through analysis of social graphs, profile data and language usage. By parsing the semantics of a user’s language and analyzing their social graph for the type of language they are accustomed to seeing and, importantly, that they have chosen to see, marketing can be best tailored for the prospect.
  • Life event based cross-selling: Identify changes in relationship, location, job or family structure that enable marketing or sales to proactively contact the insured to recommend additional products or services. An example is increasing term life coverage for a new parent. By contacting insureds with relevant products at the moment of a life event, agents can be highly effective at converting new sales.
  • Continuous risk assessment: Continuously assess insureds’ risk profiles by expanding the analysis of an insured beyond their behaviors with the carrier to their behaviors with all other parties as evidenced in their social media communications. Updates about employment, travel, family circumstances or other items can impact how a framework understands the facts of an insureds’ interactions with the carrier. By understanding this, a carrier can better tailor reserve models or reevaluate whether to renew the policy.
  • Claim fraud detection: Identify potential claim fraud activity by monitoring geolocation, language and other data elements to confirm reported stories and check for telling language used in public communications. For example, a claim for workers compensation could be identified for potential challenge if a system identifies geolocation data from a golf course.
  • Customer sentiment: Be proactive with alerts of customer dissatisfaction with claim handling or price adjustments through text mining, allowing for remediation prior to losing a customer. By identifying dissatisfaction, the carrier can take better next steps in communication and outreach to maintain a client’s goodwill and business.

These aspects of insurance sales, risk management and claim management are beneficial for carriers. However, there are risks and challenges associated with social media data:  

  • Language is complex data: Because social media is so dependent on written words, language analysis is a common basis for analysis. Semantic assessment is useful in identifying underlying emotions and intent. However, words have different meanings in different sub-cultures, geographies, friend groups and even in different transmission medium. As such, language parsing should often be used to augment existing analysis, not to serve as a primary source of facts.
  • Usage of social media varies: In general, social media has widely different usage by age group and other demographic segments. Uptake rakes are not the same across all demographic groups, as demographic analysis of Facebook vs. Snapchat bear out and actual usage of the tools varies by group. The amount of data shared by younger users typically, but not always, dwarfs that of their parents. Analytical frameworks need to be configured to account for these differences and not draw unwarranted conclusions from different behavior patterns.  
  • Usage of social media starts and stops: Users of social media will start, stop and potentially resume use many times. Details of usage may also change as users’ needs or privacy concerns change. This requires analytical tools to be flexible in analysis — to understand that lack of data, limited data or infrequent posting is not necessarily an indicator of underlying behaviors of the prospect or insured.
  • Security is tricky: In the post-Snowden era, concerns about data privacy and usage are increasingly spotlighted by the media. Insurers should be cautious about how they collect, how they store and how they take action based on social media information. De-identification and storing only the analysis of the underlying data are potential paths among others. This should be continuously evaluated.

See also: 2 Concepts on Social Media and Analytics

A final note on risks: In 2010, then-Google CEO Eric Schmidt said, “Google policy is to get right up to the creepy line but not cross it.” This brought about much criticism from the public and watchdogs as many took it to mean Google would use the data it had in ways customers were not comfortable with. Insurance is as much about trust as it is about financial contracts. Therefore, insurers should be careful in using data that some may consider private or semi-private rather than public. They should also be cautious in drawing inferences and interpretations from data in a manner which would cause insureds to question them as warranted and justifiable. The use of data to further the carrier’s understanding of its customers must be approached as a relationship that can benefit both parties, and insurers must avoid being seen as “big brother” looking to squeeze extra premium from insureds.

Customers may not embrace the concept of their behaviors being analyzed. However, good analytics programs within insurance companies should be doing that today. By combining the facts of policy, billing and claims systems along with behavior evidenced in call center data, portals, digital apps and through other mechanisms, carriers should be analyzing customers robustly. In this framework, social media data becomes an enhancement layered on top that adds new dimensions and nuances to existing analysis. By leveraging neural networking and other machine learning approaches, carriers can better market, rate and manage risk and claims. These are net positives for insurers and potentially positives for customers. But, there are some substantial risks that must be managed as part of the total analytics strategy. By focusing first on the known facts and actual behaviors and only then expanding into the nuances of social media carriers, insurers can better enable robust and sound analysis that generates a return on investment for all parties.

6 Opportunities for Carriers in ‘Big Data’

As insurers increasingly collect “big data” — think petabytes and exabytes — it’s now possible to use new data tools and technologies to mine data across three dimensions:

  • Large size/long duration — Traditional data mining usually was limited to three to five years of data. Now you can mine data accumulated over decades.
  • Real-time — With the advent of social media and the different sources, data pours in at ever-increasing speeds.
  • Variety of types — There’s more variety of data, both structured and unstructured, that are drastically different from each other.

The ability to master the complexities of capturing, processing and organizing big data has led to several data-centric opportunities for carriers.

Personalized marketing

Big data is playing an increasing role in sales and marketing, and personalization is the hot industry trend. Gathering more information about customers helps insurance companies provide more-personalized products and services. Innovative companies are coming up with new ways to gather more information about customers to personalize their buying experience.

One example is Progressive’s Snapshot device, which tracks how often insureds slam on the brakes and how many miles they drive. It lets insurers provide personalized products based on customers’ driving habits. A device like Snapshot captures information from the car every second, collecting data like how often drivers brake, how quickly they accelerate, driving time, average speed, etc. According to facethefactsusa.org, U.S. drivers log an average of 13,476 miles per year, or 37 miles a day. Big data systems have to process this constant stream of data, coming in every second for however long the user takes to travel 37 miles. Even if only 10% to 15% of customers use the device, it is still a huge amount of data to process. The systems have to process all this information and use predictive models to analyze risks and offer a personalized rate to the user.

People are increasingly using social media to voice their interests, opinions and frustrations, so analyzing social feeds can also help insurance companies better target new customers and respond to existing customers. Using big data, insurers can pinpoint trends, especially of complaints or dissatisfaction with current products and services. Getting ahead of the curve is crucial because bad reviews can spread like wildfire on the web.

Risk management 

The wealth of data now available to insurance companies — from both old and new data sources — offers ways to better predict risks and trends. Big data can be used to analyze decades of information and identify trends and newer dimensions like demographic change and behavioral evolution.

Process improvement and organizational efficiency

Another popular use is for constant improvement of organizational productivity by recording usage patterns of an organization’s internal tools and software. Better understanding of usage trends leads to:

  • Creation of more useful software that better fits the organization’s needs.
  • Avoidance of tools that do not have a good return on investment.
  • Identification of manual tasks that can be automated. For example, logs and usage patterns from tools at the agent’s office are important sources of information for understanding customer preferences and agency efficiency.

Automation of manual processes results in significant savings. But in huge, complex organizations, there are almost always overlapping or multiple instances of similar systems and processes that result in redundancy and increased cost of maintenance. Similarly, inadequate and inefficient systems require manual intervention, resulting in bottlenecks, inflated completion times and, most importantly, increased cost.

Using data from internal systems, systems can study critical usage information of various tools and analyze productivity, throughput and turnaround times across a variety of parameters. This can help managers understand inadequacies of existing systems and identify redundancy.

The same data sources are also used to predict higher and leaner load times, so the infrastructure teams can plan for providing appropriate computing resources during critical events. These measures add up quickly, resulting in significant cost savings and improved office efficiency.

Automated learning

While big data technologies now help perform regular data-mining on a much bigger scale, that’s only the beginning. Technology companies are venturing into the fuzzy world of decision-making via artificial intelligence, and a branch of AI called machine learning has greatly advanced.

Machine learning deals with making computer systems learn constantly from data to progressively make more intelligent decisions. Once a machine-learning system has been trained to use specific pattern-analyzing models, it starts to learn from the data and works to identify trends and patterns that have led to specific decisions in the past. Naturally, when more data — along all of the big data axes — is provided, the system has a much better chance to learn more, make smarter decisions and avoid the need for manual intervention.

The insurance and financial industries pioneered the commercial application of machine learning techniques by creating computational models for risk analysis and premium calculation.  They can predict risks and understand the creditworthiness of a customer by analyzing their past data.

While traditional systems dealt with tens of thousands of data records and took days to crunch through a handful of parameters to analyze risks using, for example, a modified Gaussian copula, the same is now possible in a matter of hours, with two major improvements. First, all available data can be analyzed, and second, risk parameters are unlimited.

Predictive analytics

Machine language technology can use traditional and new data streams to analyze trends and help build models that predict patterns and events with increased accuracy and convert these predictions into opportunities.

Traditional systems generally helped identify reasons for consistent patterns. For example, when analysis of decades of data exposes a consistent trend like an increase in accident reporting during specific periods of the year, results indicated climatic or social causes such as holidays.

With big data and machine learning, predictive analytics now helps create predictions for claims reporting volumes and trends, medical diagnosis for the health insurance industry, new business opportunities and much more.

Fraud Detection

The insurance industry has always been working to devise new ways to detect fraud. With big data technology, it is now possible to look for fraud detection patterns across multiple aspects of the business, including claims, payments and provider-shopping and detect them fairly quickly.

Machine learning systems can now identify new models and patterns of fraud that previously required manual detection. Fraud detection algorithms have improved tremendously with the power of machine learning. Consequently, near-real-time detection and alerting is now possible with big data. This trend promises to only keep getting better.

These six opportunities are just the tip of the iceberg. The entire insurance industry can achieve precise and targeted marketing of products based on history, preferences and social data from customers and competitors. No piece of data, regardless of form, source or size, is insignificant. With big data technology and machine learning tools and algorithms, combined with the limitless power of the cloud computing platform, possibilities are endless.