How Insurance Fraud Analytics Can Protect Your Business from Fraudulent Claims

With your experience in the insurance industry, you understand more than most about how the actions of a smattering of people can cause disproportionate damage. The $80 billion in fraudulent claims paid out across all lines of insurance each year, whether soft or hard fraud, is perpetrated by lone individuals, sketchy auto mechanic shops, or the occasional organized crime group. The challenge for most insurers is that detecting, investigating, and mitigating these deceitful claims is a time-consuming and expensive process.

Rather than accepting loss to fraud as part of the cost of doing business, some organizations are enhancing their detection capabilities with insurance analytics solutions. Here is how your organization can use insurance fraud analytics to enhance fraud detection, uncover emerging criminal strategies, and still remain compliant with data privacy regulations.

Insurance Fraud Analytics

Recognizing Patterns Faster

When you look at exceptional claim’s adjusters or special investigation units, one of the major traits they all share is an uncanny ability to recognize fraudulent patterns. Their experience allows them to notice the telltale signs of fraud, whether it’s frequent suspicious estimates from a body shop or complex billing codes intended to hide frivolous medical tests. Though you trust adjusters, many rely on heuristic judgments (e.g., trial and error, intuition, etc.) rather than hard rational analysis. When they do have statistical findings to back them up, they struggle to keep up with the sheer volume of claims.

This is where machine learning techniques can help to accelerate pattern recognition and optimize the productivity of adjusters and special investigation units. An organization starts by feeding a machine learning model a large data set that includes verified legitimate and fraudulent claims. Under supervision, the machine learning algorithm reviews and evaluates the patterns across all claims in the data set until it has mastered the ability to spot fraud indicators.

Let’s say this model was given a training set of legitimate and fraudulent auto insurance claims. While reviewing the data for fraud, the algorithm might spot links in deceptive claims between extensive damage in a claim and a lack of towing charges from the scene of the accident. Or it might notice instances where claims involve rental cars rented the day of the accident that are all brought to the same body repair shop. Once the algorithm begins to piece together these common threads, your organization can test the model’s unsupervised ability to create a criteria for detecting deception and spot all instances of fraud.

What’s important in this process is finding a balance between fraud identification and instances of false positives. If your program is overzealous, it might create more work for your agents, forcing them to prove that legitimate claims received an incorrect label. Yet when the machine learning model is optimized, it can review a multitude of dimensions to identify the likelihood of fraudulent claims. That way, if an insurance claim is called into question, adjusters can comb through the data to determine if the claim should truly be rejected or if the red flags have a valid explanation.

Detecting New Strategies

The ability of analytics tools to detect known instances of fraud is only the beginning of their full potential. As with any type of crime, insurance fraud evolves with technology, regulations, and innovation. With that transformation comes new strategies to outwit or deceive insurance companies.

One recent example has emerged through automation. When insurance organizations began to implement straight through processing (STP) in their claim approvals, the goal was to issue remittances more quickly, easily, and cheaply than manual processes. For a time, this approach provided a net positive, but once organized fraudsters caught wind of this practice, they pounced on a new opportunity to deceive insurers.

Criminals learned to game the system, identifying amounts that were below the threshold for investigation and flying their fraudulent claims under the radar. In many cases, instances of fraud could potentially double without the proper tools to detect these new deception strategies. Though most organizations plan to enhance their anti-fraud technology, there’s still the potential for them to lose millions in errant claims – if their insurance fraud analytics are not programmed to detect new patterns.

In addition to spotting red flags for common fraud occurrences, analytics programs need to be attuned to any abnormal similarities or unlikely statistical trends. Using cluster analysis, an organization can detect statistical outliers and meaningful patterns that reveal potential instances of fraud (such as suspiciously identical fraud claims).

Even beyond the above automation example, your organization can use data discovery to find hidden indicators of fraud and predict future incidents. Splitting claims data into various groups through a few parameters (such as region, physician, billing code, etc., in healthcare) can help in detecting unexpected correlations or warning signs for your automation process or even human adjusters to flag as fraud.

Safeguarding Personally Identifiable Information

As you work to improve your fraud detection, there’s one challenge all insurers face: protecting the personally identifiable information (PII) of policyholders while you analyze your data. The fines related to HIPAA violations can amount to $50,000 per violation, and other data privacy regulations can result in similarly steep fines. The good news is that insurance organizations can balance their fraud prediction and data discovery with security protocols if their data ecosystem is appropriately designed.

Maintaining data privacy compliance and effective insurance fraud analytics requires some maneuvering. Organizations that derive meaningful and accurate insight from their data must first bring all of their disparate data into a single source of truth. Yet, unless they also implement access control through a compliance-focused data governance strategy, there’s a risk of regulatory violations while conducting fraud analysis.

One way to limit your exposure is to create a data access layer that tokenizes the data, replacing any sensitive PII with unique identification symbols to keep data separate. Paired with clear data visualization capabilities, your adjusters and special investigation units can see clear-cut trends and evolving strategies without revealing individual claimants. From there, they can take their newfound insights into any red flag situation, saving your organization millions while reducing the threat of noncompliance.

Want to learn more about how the right analytics solutions can help you reduce your liability, issue more policies, and provide better customer service? Check out our insurance analytics solutions page for use cases that are transforming your industry.


Where Does a Modern Data Warehouse Fit in an Organization?

In part 1 and part 2 of our modern data warehouse series, we laid out the benefits of a data warehouse and compared the different types of modern data warehouses available. In part 3, we take a step back and see how the modern data warehouse fits in your overall data architecture.

A modern data warehouse is just one piece of the puzzle of a modern data architecture that will ultimately provide insights to the business via reporting, dashboarding, and advanced analytics.

There are many factors to consider when it comes to modern data warehousing, and it’s important to understand upfront that it’s a huge endeavor. With that in mind, a well-designed modern data warehouse will help your organization grow and stay competitive in our ever-changing world.

Download Now: Modern Data Warehouse Comparison Guide [Snowflake, Redshift, Azure Synapse,and Google BigQuery]

The ultimate goal of modern architecture is to facilitate the movement of data not only to the data warehouse but also to other applications in the enterprise. The truth of the matter is that a modern data architecture is designed very similarly to how we at 2nd Watch would design an on-premise or traditional data architecture, though with some major differences. Some of the benefits of a modern data architecture are as follows:

  • Tools and technology available today allow the development process to speed up tremendously.
  • Newer data modeling methodologies can be used to track the history of data efficiently and cost-effectively.
  • Implementation of near real-time scenarios is much more cost-effective and easier to implement utilizing cloud technologies.
  • With some SaaS providers, you can worry much less about the underlying hardware, indexing, backups, and database maintenance and more about the overall business solution.
  • While technology advances have removed some of the technical barriers experienced in on-premises systems, data must still be modeled in a way that supports goals, business needs, and specific use cases.

Below you will find a high-level diagram of a modern data architecture we use at 2nd Watch, along with a description of the core components of the architecture:

modern data architecture diagram

Raw Data Layer vs. Data Hub vs. Enterprise Data Warehouse

Technical details aside, 2nd Watch’s architecture provides key benefits that will add value to any business seeking a modern data warehouse. The raw data layer enables the ingestion of all forms of data, including unstructured data. In addition, the raw layer keeps your data safe by eliminating direct user access and creating historical backups of your source data. This historical record of data can be accessed for data science use cases as well as modeled for reports and dashboards to show historical trends over time.

The transformation-focused data hub enables easy access to data from various source systems. For example, imagine you have one customer that can be tracked across several subsidiary companies. The business layer would enable you to track their activity across all of your business lines by conforming the various data points into one source of truth. Furthermore, the business layer allows your organization to add additional data sources without disrupting your current reporting and solutions.

The enterprise data warehouse provides a data layer structured with reporting in mind. It ensures that any reports and dashboards update quickly and reliably, and it provides data scientists with reliable data structured for use in models. Overall, the modern data warehouse architecture enables you to provide your end users with near real-time reporting, allowing them to act on insights as they occur. Each component of the architecture provides unique business value that translates into a competitive advantage.

If you depend on your data to better serve your customers, streamline your operations, and lead (or disrupt) your industry, a modern data platform built on the cloud is a must-have for your organization.

Contact us for a complimentary whiteboarding session to learn what a modern data warehouse would look like for your organization.


Blockchain: The Basics

Blockchain is one of those once-in-a-generation technologies that has the potential to really change the world around us. Despite this, blockchain is something that a lot of people still know nothing about. Part of that, of course, is because it’s such a new piece of technology that really only became mainstream within the past few years. The main reason, though, (and to address the elephant in the room) is because blockchain is associated with what some describe as “fake internet money” (i.e., Bitcoin). The idea of a decentralized currency with no guarantor is intimidating, but let’s not let that get in the way of what could be a truly revolutionary technology. So, before we get started, let’s remove the Bitcoin aspect and simply focus on blockchain. (Don’t worry, we’ll pick it back up later on.)

Blockchain, at its very core, is a database. But blockchains are different from traditional databases in that they are immutable, unable to be changed. Imagine this: Once you enter information into your shiny new blockchain, you don’t have to worry about anybody going in and messing up all your data. “But how is this possible?” you might ask.

Blockchains operate by taking data and structuring it into blocks (think of a block like a record in a database). This can be any kind information, from names and numbers all the way to executable code scripts. There are a few essential pieces of information that should be placed in all blocks, those being an index (the block number), a timestamp, and the hash (more on this later) of the previous block. All of this data is compiled into a block, and a hashing algorithm is applied to the information.

blockchain After the hash is computed, the information is locked and you can’t change information without re-computing the hash. This hash is then passed on to the next block where it gets included in its data, creating a chain. The second block then compiles all of its own data and, including the hash of the previous block, creates a new hash and sends it to the next block in the chain. In this way, a blockchain is created by “chaining” together blocks by means of a block’s unique hash. In other words, the hash of one block is reliant on the hash of the previous block, which is reliant on that of the one before it, ad infinitum.

And there you go, you have a blockchain! Before we move on to the next step (which will really blow your mind), let’s recap:

Blockchain: The Basics ChartYou have Block-0. Information is packed into Block-0 and hashed, giving you Hash-0. Hash-0 is passed to Block-1, which is combined with the data in Block-1. So, Block-1’s data now includes its own information and Hash-0. This is now hashed to release Hash-1, and it’s passed to the next block.

The second major aspect of blockchain is that it is distributed. This means that the entire protocol is operated across a network of nodes at the same time. All of the nodes in the network store the entire chain, along with all new blocks, at the same time and in real time.

Secure Data Is Good Data

Remember earlier when we said a blockchain is immutable? Let’s go back to that.

Suppose you have a chain 100 blocks long and running on 100 nodes at once. Now let’s say you want to stage an attack on this blockchain to change Block-75. Because the chain is run and stored across 100 nodes simultaneously, you have to instantaneously change Block-75 in all 100 nodes at the same time. Let’s imagine somehow you are able to hack into those other nodes to do this; now you have to rehash everything from Block-75 to Block-100 (which, remember, rehashing is extremely computationally difficult). So while you (the singular malicious node) are trying to rehash all of those blocks, the other 99 nodes in the network are working to hash new blocks, thereby extending the chain. This makes it impossible for a compromised chain to become valid because it will never reach the same length of the original chain.

About That Bitcoin Thing…

Now, there are two types of blockchains. Most popular blockchains are public, in which anybody in the world is able to join and contribute to the network. This requires some incentive, as without it nobody would join the network, and this comes in the form of “tokens” or “coins” (i.e., Bitcoin). In other words, Bitcoin is an incentive for people to participate and ensure the integrity of the chain. Then there are permissioned chains, which are run by individuals, organizations, or conglomerates for their own reasons and internal uses. In permissioned chains, only nodes with certain permissions are able to join and be involved in the network.

And there you go, you have the basics of blockchain. At a fundamental level, it’s an extremely simple yet ingenious idea with applications for supply chains, smart contracts, auditing, and many more to come. However, like any promising new technology, there are still questions, pitfalls, and risks to be explored. If you have any questions about this topic or want to discuss the potential for blockchain in your organization, contact us here.


What Real-Time Analytics Looks Like for Real-World Businesses

Real-time analytics. Streaming analytics. Predictive analytics. These buzzwords are thrown around in the business world without a clear-cut explanation of their full significance. Each approach to analytics presents its own distinct value (and challenges), but it’s tough for stakeholders to make the right call when the buzz borders on white noise.

Which data analytics solution fits your current needs? In this post, we aim to help businesses cut through the static and clarify modern analytics solutions by defining real-time analytics, sharing use cases, and providing an overview of the players in the space.


  • Real-time or streaming analytics allows businesses to analyze complex data as it’s ingested and gain insights while it’s still fresh and relevant.
  • Real-time analytics has a wide variety of uses, from preventative maintenance and real-time insurance underwriting to improving preventive medicine and detecting sepsis faster.
  • To get the full benefits of real-time analytics, you need the right tools and a solid data strategy foundation.

What is Real-Time Analytics?

In a nutshell, real-time or streaming analysis allows businesses to access data within seconds or minutes of ingestion to encourage faster and better decision-making. Unlike batch analysis, data points are fresh and findings remain topical. Your users can respond to the latest insight without delay.

Yet speed isn’t the sole advantage of real-time analytics. The right solution is equipped to handle high volumes of complex data and still yield insight at blistering speeds. In short, you can conduct big data analysis at faster rates, mobilizing terabytes of information to allow you to strike while the iron is hot and extract the best insight from your reports. Best of all, you can combine real-time needs with scheduled batch loads to deliver a top-tier hybrid solution.

What Is Real-Time Analytics

Stream Analytics Overview Courtesy of Microsoft

Streaming Analytics in Action

How does the hype translate into real-world results? Depending on your industry, there is a wide variety of examples you can pursue. Here are just a few that we’ve seen in action:

Next-Level Preventative Maintenance

Factories hinge on a complex web of equipment and machinery working for hours on end to meet the demand for their products. Through defects or standard wear and tear, a breakdown can occur and bring production to a screeching halt. Connected devices and IoT sensors now provide technicians and plant managers with warnings – but only if they have the real-time analytics tools to sound the alarm.

Azure Stream Analytics is one such example. You can use Microsoft’s analytics engine to monitor multiple IoT devices and gather near-real-time analytical intelligence. When a part needs a replacement or it’s time for routine preventative maintenance, your organization can schedule upkeep with minimal disruption. Historical results can be saved and integrated with other line-of-business data to cast a wider net on the value of this telemetry data.

Key Benefits of Stream AnalyticsReal-Time Insurance Underwriting

Insurance underwriting is undergoing major changes thanks to the gig economy. Rideshare drivers need flexibility from their auto insurance provider in the form of modified commercial coverage for short-term driving periods. Insurance agencies prepared to offer flexible micro policies that reflect real-time customer usage have the opportunity to increase revenue and customer satisfaction.

In fact, one of our clients saw the value of harnessing real-time big data analysis but lacked the ability to consolidate and evaluate their high-volume data. By partnering with our team, they were able to create real-time reports that pulled from a variety of sources ranging from driving conditions to driver ride-sharing scores. With that knowledge, they’ve been able to tailor their micro policies and enhance their predictive analytics.

Healthcare Analytics

How about this? Real-time analytics saves lives. Death by sepsis, an excessive immune response to infection that threatens the lives of 1.7 million Americans each year, is preventable when diagnosed in time. The majority of sepsis cases are not detected until manual chart reviews conducted during shift changes – at which point, the infection has often already compromised the bloodstream and/or vital tissues. However, if healthcare providers identified warning signs and alerted clinicians in real time, they could save multitudes of people before infections spread beyond treatment.

HCA Healthcare, a Nashville-based healthcare provider, undertook a real-time healthcare analytics project with that exact goal in mind. They created a platform that collects and analyzes clinical data from a unified data infrastructure to enable up-to-the-minute sepsis diagnoses. Gathering and analyzing petabytes of unstructured data in a flash, they are now able to get a 20-hour early warning sign that a patient is at risk of sepsis. Faster diagnoses results in faster and more effective treatment.

That’s only the tip of the iceberg. For organizations in the healthcare payer space, real-time analytics has the potential to improve member preventive healthcare. Once again, real-time data from smart wearables, combined with patient medical history, can provide healthcare payers with information about their members’ health metrics. Some industry leaders even propose that payers incentivize members to make measurable healthy lifestyle choices, lowering costs for both parties at the same time.

Getting Started with Real-Time Analysis

There’s clear value produced by real-time analytics but only with the proper tools and strategy in place. Otherwise, powerful insight is left to rot on the vine and your overall performance is hampered in the process. If you’re interested in exploring real-time analytics for your organization, contact us for an analytics strategy session. In this session lasting 2-4 hours, we’ll review your current state and goals before outlining the tools and strategy needed to help you achieve those goals.