Insurance providers are rich with data far beyond what they once had at their disposal for traditional historical analysis. The quantity, variety, and complexity of that data enhance the ability of insurers to gain greater insights into consumers, market trends, and strategies to improve their bottom line. But which projects offer you the best return on your investment? Here’s a glimpse at some of the most common insurance analytics project use cases that can transform the capabilities of your business.
Use your historical data to predict when a customer is most likely to buy a new policy.
Both traditional insurance providers and digital newcomers are competing for the same customer base. As a result, acquiring new customers requires targeted outreach with the right message at the moment a buyer is ready to purchase a specific type of insurance.
Predictive analytics allows insurance companies to evaluate the demographics of the target audience, their buying signals, preferences, buying patterns, pricing sensitivity, and a variety of other data points that forecast buyer readiness. This real-time data empowers insurers to reach policyholders with customized messaging that makes them more likely to convert.
Quoting Accurate Premiums
Provide instant access to correct quotes and speed up the time to purchase.
Consumers want the best value when shopping for insurance coverage, but if their quote fails to match their premium, they’ll take their business elsewhere. Insurers hoping to acquire and retain policyholders need to ensure their quotes are precise – no matter how complex the policy.
For example, one of our clients wanted to provide ride-share drivers with four-hour customized micro policies on-demand. Using real-time analytical functionality, we enabled them to quickly and accurately underwrite policies on the spot.
Improving Customer Experience
Better understand your customer’s preferences and optimize future interactions.
A positive customer experience means strong customer retention, a better brand reputation, and a reduced likelihood that a customer will leave you for the competition. In an interview with CMSWire, the CEO of John Hancock Insurance said many customers see the whole process as “cumbersome, invasive, and long.” A key solution is reaching out to customers in a way that balances automation and human interaction.
For example, the right analytics platform can help your agents engage policyholders at a deeper level. It can combine the customer story and their preferences from across customer channels to provide more personalized interactions that make customers feel valued.
Stop fraud before it happens.
You want to provide all of your customers with the most economical coverage, but unnecessary costs inflate your overall expenses. Enterprise analytics platforms enable claims analysis to evaluate petabytes of data to detect trends that indicate fraud, waste, and abuse.
See for yourself how a tool like Tableau can help you quickly spot suspicious behavior with visual insurance fraud analysis.
Now, high-powered analytics has the potential to provide insurers with a real-time understanding of loss ratios, using a wide range of data points to evaluate which of your customers are underpaying or overpaying.
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.
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.
Are you ready to take your data science initiatives to the next level? Partner with 2nd Watch, the industry leader in data management, analytics, and data science consulting. Our team of experts will guide you through the entire process, from building the business case to data preparation and model building. Schedule a data science readiness whiteboard session with us today and unlock the full potential of data science for your business. Don’t miss out on the opportunity to enhance fraud detection, uncover emerging criminal strategies, and remain compliant with data privacy regulations. Get started now and experience the transformative power of insurance fraud analytics with 2nd Watch by your side.
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.
Real-time analytics is revolutionizing the way businesses make decisions and gain insights. With streaming analytics, organizations can analyze complex data as it is ingested, enabling faster and more informed decision-making. Whether it’s detecting anomalies in manufacturing processes, optimizing supply chain operations, or personalizing customer experiences in real-time, streaming analytics is transforming various industries. By leveraging advanced technologies and powerful analytics platforms, businesses can unlock the full potential of real-time data to drive growth, improve operational efficiency, and stay ahead in today’s fast-paced business landscape.
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.
Real-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.
Real-time analytics is revolutionizing the way businesses operate, providing valuable insights and enabling faster decision-making. With its ability to analyze complex data in real-time, organizations can stay ahead of the competition and make data-driven decisions. At 2nd Watch, we understand the importance of real-time analytics and its impact on business success.
Get Started with Real-Time Analytics
If you’re ready to leverage the power of real-time analytics for your business, partner with 2nd Watch. Our team of experts can help you develop a comprehensive analytics strategy, implement the right tools and technologies, and guide you through the process of unlocking the full potential of real-time analytics. Contact us today to get started on your real-time analytics journey and drive meaningful business outcomes.
It’s difficult to achieve your objectives when the goalposts are always in motion. Yet that’s often the reality for the healthcare industry. Ongoing changes in competition, innovation, regulation, and care standards demand real-time insight. Otherwise, it’s all too easy to miss watershed moments to change, evolve, and thrive.
Advanced or modernized analytics are often presented as the answer to reveal these hidden patterns, trends, or predictive insights. Yet when spoken about in an abstract or technical way, it’s hard to imagine the tangible impact that unspecified data can have on your organization. Here are some of the real-world use cases of big data analytics in healthcare, showing the valuable and actionable intelligence within your reach.
Improve Preventative Care
It’s been reported that six in ten Americans suffer from chronic diseases that impact their quality of life – many of which are preventable. Early identification and mediation reduce risk of long-term health problems, but only if organizations can accurately identify vulnerable patients or members. The success of risk scoring depends on a tightrope walk exploring populace overviews and individual specifics – a feat that depends on a holistic view of each patient or member.
A wide range of data contributes to risk scoring (e.g., patient/member records, social health determinants, etc.) and implementation (e.g., service utilization, outreach results, etc.). With data contained in an accessible, centralized infrastructure, organizations can pinpoint at-risk individuals and determine how best to motivate their participation in their preventive care. This can reduce instances of diabetes, heart disease, and other preventable ailments.
Encouraging healthy choices and self-care is just one potential example. Big data analytics has also proven to be an effective solution for preventing expensive 30-day hospital readmissions. Researchers at the University of Washington Tacoma used a predictive analytics model on clinical data and demographics metrics to predict the return of congestive heart failure patients with accurate results.
From there, other organizations have repurposed the same algorithmic framework to identify other preventable health issues and reduce readmission-related costs. One Chicago-based health system implemented a data-driven nutrition risk assessment that identified those patients at risk for readmissions. With that insight, they employed programs that combated patient malnutrition, cut readmissions, and saved $4.8 million. Those are huge results from one data set.
Boost Operational Efficiency
It’s well known that healthcare administrative costs in the United States are excessive. But it’s hard to keep your jaw from hitting the floor when you learn Canadian practices spend 27% of what U.S. organization do for the same claims processing. That’s a clear sign of operational waste, yet one that doesn’t automatically illuminate the worst offenders. Organizations can shine a light on wastage with proper healthcare analytics and data visualizations.
For instance, the right analytics and BI platform is capable of accelerating improvements. It can cross-reference patient intake data, record-keeping habits, billing- and insurance-related costs, supply chain expenses, employee schedules, and other data points to extract hidden insight. With BI visualization tools, you can obtain actionable insight and make adjustments in a range of different functions and practices.
Additionally, predictive analytics solutions can help to improve the forecasting of both provider organizations. For healthcare providers, a predictive model can help anticipate fluctuations in patient flow, enabling an appropriate workforce response to patient volume. Superior forecasting at this level manages to reduce two types of waste: labor dollars from overscheduling and diminished productivity from under-scheduling.
Enhance Insurance Plan Designs
There is a distinct analytics opportunity for payers, third-party administrators, and brokers: enhancing their insurance plan designs. Whether you want to retain or acquire customers, your organization’s ability to provide a more competitive and customized plan than the competition will be a game-changer.
All of the complicated factors that contribute to the design of an effective insurance plan can be streamlined. Though most organizations have lots of data, it can be difficult to discern the big picture. But machine learning programs have the ability to take integrated data sources such as demographics, existing benefit plans, medical and prescription claims, risk scoring, and other attributes to build an ideal individualized program. The result? Organizations are better at catering to members and controlling costs.
Plenty of Other Use Cases Exist
And these are just a sample of what’s possible. Though there are still new and exciting ways you can analyze your data, there are also plenty of pre-existing roadmaps to elicit incredible results for your business. To get the greatest ROI, your organization needs guidance through the full potential of these groundbreaking capabilities.
Data is one of the insurance industry’s greatest assets, which is why data analytics is so important. Before digital transformations swept the business world, underwriters and claims adjusters were the original data-driven decision makers, gathering information to assess a customer’s risk score or evaluate potential fraud. Algorithms have accelerated the speed and complexity of analytics in insurance, but some insurers have struggled to implement the framework necessary to keep their underwriting, fraud detection, and operations competitive.
The good news is that we have a clear road map for how to implement data analytics in insurance that garners the best ROI for your organization. Here are the four steps you need to unlock even more potential from your data.
Step 1: Let your business goals, not your data, define your strategy
As masters of data gathering, insurers have no shortage of valuable and illuminating data to analyze. Yet the abundance of complex data flowing into their organizations creates an equally vexing problem: conducting meaningful analysis rather than spur-of-the-moment reporting.
It’s all too easy for agents working on the front lines to allow the data flowing into their department to govern the direction of their reporting. Though ad hoc reporting can generate some insight, it rarely offers the deep, game-changing perspective businesses need to remain competitive.
Instead, your analytics strategy should align with your business goals if you want to yield the greatest ROI. Consider this scenario. A P&C insurer wants to increase the accuracy of their policy pricing in a way that retains customers without incurring additional expenses from undervalued risk. By using this goal to define their data strategy, it’s a matter of identifying the data necessary to complete that objective.
If, for example, they lack complex assessments of the potential risks in the immediate radius of a commercial property (e.g., a history of flood damage, tornado warnings, etc.), the insurer can seek out that data from an external source to complete the analysis, rather than restricting the scope of their analysis to what they have.
Step 2: Get a handle on all of your data
The insurance industry is rife with data silos. Numerous verticals, LoBs, and M&A activity have created a disorganized collection of platforms and data storage, often with their own incompatible source systems. In some cases, each unit or function has its own specialized data warehouse or activities that are not consistent or coordinated. This not only creates a barrier to cohesive data analysis but can result in a hidden stockpile of information as LoBs make rogue implementations off the radar of key decision-makers.
Before you can extract meaningful insights, your organization needs to establish a single source of truth, creating a unified view of your disparate data sources. One of our industry-leading insurance clients provides a perfect example of the benefits of data integration. The organization had grown over the years through numerous acquisitions, and each LoB brought their own unique policy and claims applications into the fold. This piecemeal growth created inconsistency across their comprehensive insight.
For example, the operational reports conducted by each LoB reported a different amount of paid losses on claims for the current year, calling into question their enterprise-wide decision-making process. As one of their established partners, 2nd Watch provided a solution. Our team conducted a current state assessment, interviewing a number of stakeholders to determine the questions each group wanted answered and the full spectrum of data sources that were essential to reporting.
We then built data pipelines (using SSIS for ETL and SQL Server) to integrate the 25 disparate sources we identified as crucial to our client’s business. We unified the meta-data, security, and governance practices across their organizations to provide a holistic view that also remained compliant with federal regulation. Now, their monthly P&L and operational reporting are simplified in a way that creates agreements across LoBs – and helps them make informed decisions.
Step 3: Create the perfect dashboard(s)
You’ve consolidated and standardized your data. You’ve aligned your analytics strategy with your goals. But can your business users quickly obtain meaning from your efforts? The large data sets analyzed by insurance organizations can be difficult to parse without a way to visualize trends and takeaways. For that very reason, building a customized dashboard is an essential part of the data analytics process.
Your insurance analytics dashboard is not a one-size-fits-all interface. Similarly, to how business goals should drive your strategy, they should also drive your dashboards. If you want people to derive quick insights from your data, the dashboard they’re using should evaluate KPIs and trends that are relevant to their specific roles and LoBs.
Claims adjusters might need a dashboard that compares policy type by frequency of utilization and cost, regional hotspots for claims submissions, or fraud priority scores for insurance fraud analytics. C-suite executives might be more concerned with revenue comparisons across LoBs, loss ratios per policy, and customer retention by vertical. All of those needs are valid. Each insurance dashboard should be designed and customized to satisfy the most common challenges of the target users in an interactive and low-effort way.
Much like the data integration process, you’ll find ideal use cases by conducting thorough stakeholder interviews. Before developers begin to build the interface, you should know the current analysis process of your end users, their pain points, and their KPIs. That way, you can encourage them to adopt the dashboards you create, running regular reports that maximize the ROI of your efforts.
Step 4: Prepare for ongoing change
A refined data strategy, consolidated data architecture, and intuitive dashboards are the foundation for robust data analytics in insurance. Yet the benchmark is always moving. There’s an unending stream of new data entering insurance organizations. Business goals are adjusting to better align with new regulations, global trends, and consumer needs. Insurers need their data analytics to remain as fluid and dynamic as their own organizations. That requires your business to have the answers to a number of questions.
How often should your dashboard update? Do you need real-time analytics to make up-to-the-minute assessments on premiums and policies? How can you translate the best practices from profitable use cases into different LoBs or roles? Though these questions (and many others) are not always intuitive, insurers can make the right preparations by working with a partner that understands their industry.
Here’s an example: One of our clients had a vision to implement a mobile application that enabled rideshare drivers to obtain commercial micro-policies based on the distance traveled and prevailing conditions. After we consolidated and standardized disparate data systems into a star schema data warehouse, we automated the ETL processes to simplify ongoing processes.
From there, we provided our client with guidance on how to build upon their existing real-time analytics to deepen the understanding of their data and explore cutting-edge analytical solutions. Creating this essential groundwork has enabled our team to direct them as we expand big data analytics capabilities throughout the organization, implementing a roadmap that yields greater effectiveness across their analytics.
P&C insurance is an incredibly data-driven industry. Your company’s core assets are data, your business revolves around collecting data, and your staff is focused on using data in their day-to-day workstreams. Although data is collected and used in normal operations, oftentimes the downstream analytics process is painful (think of those month-end reports). This is for any number of reasons:
Large, slow data flows
Unmodeled data that takes manual intervention to integrate
Legacy software that has a confusing backend and user interface
Creating an analytics ecosystem that is fast and accessible is not a simple task, but today we’ll take you through the four key steps 2nd Watch follows to solve business problems with an insurance analytics solution. We’ll also provide recommendations for how best to implement each step to make these steps as actionable as possible.
Step 1: Determine your scope.
What are your company’s priorities?
Trying to improve profit margin on your products?
Improving your loss ratio?
Planning for next year?
Increasing customer satisfaction?
To realize your strategic goals, you need to determine where you want to focus your resources. Work with your team to find out which initiative has the best ROI and the best chance of success.
First, identify your business problems.
There are so many ways to improve your KPIs that trying to identify the best approach can very quickly become overwhelming. To give yourself the best chance, be deliberate about how you go about solving this challenge.
What isn’t going right? Answer this question by talking to people, looking at existing operational and financial reporting, performing critical thinking exercises, and using other qualitative or quantitative data (or both).
Then, prioritize a problem to address.
Once you identify the problems that are impacting metrics, choose one to address, taking these questions into account:
What is the potential reward (opportunity)?
What are the risks associated with trying to address this problem?
How hard is it to get all the inputs you need?
Taking on a scope that is too large, too complex, or unclear will make it very difficult to achieve success. Clearly set boundaries and decide what is relevant to determine which pain point you’re trying to solve. A defined critical path makes it harder to go off course and helps you keep your goal achievable.
Step 2: Identify and prioritize your KPIs.
Next, it’s time to get more technical. You’ve determined your pain points, but now you must identify the numeric KPIs that can act as the proxies for these business problems.
Maybe your business goal is to improve policyholder satisfaction. That’s great! But what does that mean in terms of metrics? What inputs do you actually need to calculate the KPI? Do you have the data to perform the calculations?
Back to the example, here are your top three options:
Based on this information, even though the TTC metric may be your third-favorite KPI for measuring customer satisfaction, the required inputs are identified and the data is available. This makes it the best option for the data engineering effort at this point in time. It also helps you identify a roadmap for the future if you want to start collecting richer information.
As you identify the processes you’re trying to optimize, create a data dictionary of all the measures you want to use in your reporting. Appreciate that a single KPI might:
Have more and higher quality data
Be easier to calculate
Be used to solve multiple problems
Be a higher priority to the executive team
Use this list to prioritize your data engineering effort and create the most high-value reports first. Don’t engineer in a vacuum (i.e., generate KPIs because they “seem right”). Always have an end business question in mind.
Step 3: Design your solution.
Now that you have your list of prioritized KPIs, it’s time to build the data warehouse. This will allow your business analysts to slice your metrics by any number of dimensions (e.g., TTC by product, TTC by policy, TTC by region, etc.).
2nd Watch’s approach usually involves a star schema reporting layer and a customer-facing presentation layer for analysis. A star schema has two main components: facts and dimensions. Fact tables contain the measurable metrics that can be summarized. In the TTC example, the fact-claim tables might contain a numeric value containing the number of days to close a claim. A dimension table would then provide context for how you pivot the measure. For example, you might have a dimension-policyholder table that contains attributes to “slice” the KPI value (e.g., policyholder age, gender, tenure, etc.).
Once you design the structure of your database design, you can build it. This involves transforming the data from your source system to the target database. You’ll want to consider the ETL (extract-transform-load) tool that will automate this transformation, and you’ll also need to consider the type of database that will be used to store your data. 2nd Watch can help with all these technology decisions.
You may also want to take a particular set of data standards into account, such as the ACORD Standards, to ensure more efficient and effective flow of data across lines of business, for example. 2nd Watch can take these standards into account when implementing an insurance analytics solution, giving you confidence that your organization can use enterprise-wide data for a competitive advantage.
Finally, when your data warehouse is up and running, you want to make sure your investment pays off by managing the data quality of your data sources. This can all be part of a data governance plan, which includes data consistency, data security, and data accountability.
Don’t feel like you need to implement the entire data warehouse at once. Be sure to prioritize your data sources and realize you can gain many benefits by just implementing some of your data sources.
Step 4: Put your insurance analytics solution into practice.
After spending the time to integrate your disparate data sources and model an efficient data warehouse, what do you actually get out of it? As an end business user, this effort can bubble up as flat file exports, dashboards, reports, or even data science models.
I’ve outlined three levels of data maturity below:
The most basic product would be a flat file. Often, mid-to-large-sized organizations working with multiple source systems work in analytical silos. They connect directly to the back end of a source system to build analytics. As a result, intersystem analysis becomes complex with non-standard data definitions, metrics, and KPIs.
With all of that source data integrated in the data warehouse, the simplest way to begin to analyze the data is off of a single flat extract. The tabular nature of a flat file will also help business users answer basic questions about their data at an organizational level.
Organizations farther along the data maturity curve will begin to build dashboards and reports off of the data warehouse. Regardless of your analytical capabilities, dashboards allow your users to glean information at a glance. More advanced users can apply slicers and filters to better understand what drives their KPIs.
By compiling and aggregating your data into a visual format, you make the breadth of information at your organization much more accessible to your business users and decision-makers.
The most mature product of data integration would be data science models. Machine learning algorithms can detect trends and patterns in your data that traditional analytics would take a long time to uncover, if ever. Such models can help insurers more efficiently screen cases and predict costs with greater precision. When writing policies, a model can identify and manage risk based on demographic or historic factors to determine ROI.
Start simple. As flashy and appealing as data science can be to stakeholders and executives, the bulk of the value of a data integration platform lies in making the data accessible to your entire organization. Synthesize your data across your source systems to produce file extracts and KPI scorecards for your business users to analyze. As users begin to adopt and understand the data, think about slowly scaling up the complexity of analysis.
This was a lot of information to absorb, so let’s summarize the roadmap to solving your business problems with insurance analytics:
Step 1: Determine your scope.
Step 2: Identify and prioritize your KPIs.
Step 3: Design your solution.
Step 4: Put your insurance analytics solution into practice.
2nd Watch’s data and analytics consultants have extensive experience with roadmaps like this one, from outlining data strategy to implementing advanced analytics. If you think your organization could benefit from an insurance analytics solution, feel free to get in touch to discuss how we can help.
Data sharing has become more complex, both in its application and our relationship to it. There is a tension between the need for personalization and the need for privacy. Businesses must share data to be effective and ultimately provide tailored customer experiences. However, legislation and practices regarding data privacy have tightened, and data sharing is tougher and fraught with greater compliance constraints than ever before. The challenge for enterprises is reconciling the increased demand for data with increased data protection.
The modern world runs on data. Companies share data to facilitate their daily operations. Data distribution occurs between business departments and external third parties. Even something as innocuous as exchanging Microsoft Excel and Google Sheets spreadsheets is data sharing!
Data collaboration is entrenched in our business processes. Therefore, rather than avoiding it, we must find the tools and frameworks to support secure and privacy-compliant data sharing. So how do we govern the flow of sensitive information from our data platforms to other parties?
The answer: data clean rooms. Data clean rooms are the modern vehicle for various data sharing and data governance workflows. Across industries – including media and entertainment, advertising, insurance, private equity, and more – a data clean room can be the difference-maker in your data insights.
There is a classic thought experiment wherein two millionaires want to find out who is richer without actually sharing how much money they are individually worth. The data clean room solves this issue by allowing parties to ask approved questions, which require external data to answer, without actually sharing the sensitive information itself!
In other words, a data clean room is a framework that allows two parties to securely share and analyze data by granting both parties control over when, where, and how said data is used. The parties involved can pool together data in a secure environment that protects private details. With data clean rooms, brands can access crucial and much-needed information while maintaining compliance with data privacy policies.
Data clean rooms have been around for about five years with Google being the first company to launch a data clean room solution (Google Ads Data Hub) in 2017. The era of user privacy kicked off in 2018 when data protection and privacy became law, most notably with the General Data Protection Regulation (GDPR).
This was a huge shake-up for most brands. Businesses had to adapt their data collection and sharing models to operate within the scope of the new legislation and the walled gardens that became popular amongst all tech giants. With user privacy becoming a priority, data sharing has become stricter and more scrutinized, which makes marketing campaign measurements and optimizations in the customer journey more difficult than ever before.
Data clean rooms are crucial for brands navigating the era of consumer protection and privacy. Brands can still gain meaningful marketing insights and operate within data privacy laws in a data clean room.
Data clean rooms work because the parties involved have full control over their data. Each party agrees upon access, availability, and data usage, while a trusted data clean room offering oversees data governance. This yields the secure framework needed to ensure that one party cannot access the other’s data and upholds the foundational rule that individual, or user-level data cannot be shared between different parties without consent.
Personally, identifying information (PII) remains anonymized and is processed and stored in a way that is not exposed to any parties involved. Thus, data sharing within a data clean room complies with privacy policies, such as GDPR and California Consumer Privacy Act (CCPA).
How does a data clean room work?
Let’s take a deeper dive into the functionality of a data clean room. Four components are involved with a data clean room:
#1 – Data ingestion
Data is funneled into the data clean room. This can be first-party data (generated from websites, applications, CRMs, etc.) or second-party data from collaborating parties (such as ad networks, partners, publishers, etc.)
#2 – Connection and enrichment
The ingested data sets are matched at the user level. Tools like third-party data enrichment complement the data sets.
#3 – Analytics
The data is analyzed to determine if there are intersections/overlaps, measurement/attribution, and propensity scoring. Data will only be shared where the data points intersect between the two parties.
#4 – Application
Once the data has finished its data clean room journey, each party will have aggregated data outputs. It creates the necessary business insights to accomplish crucial tasks such as optimizing the customer experience, performing reach and frequency measurements, building effective cross-platform journeys, and conducting deep marketing campaign analyses.
What are the benefits of a data clean room?
Data clean rooms can benefit businesses in any industry, including media, retail, and advertising. In summary, data clean rooms are beneficial for the following reasons:
You can enrich your partner’s data set.
With data clean rooms, you can collaborate with your partners to produce and consume data regarding overlapping customers. You can pool common customer data with your partners, find the intersection between your business and your partners, and share the data upstream without sharing sensitive information with competitors. An example would be sharing demand and sales information with an advertising partner for better-targeted marketing campaigns.
You can create governance within your enterprise.
Data clean rooms provide the framework to achieve the elusive “single source of truth.” You can create a golden record encompassing all the data in every system of records within your organization. This includes sensitive PII such as social security numbers, passport numbers, financial account numbers, transactional data, etc.
You can remain policy compliant.
In a data clean room environment, you can monitor where the data lives, who has access to it, and how it is used with a data clean room. Think of it as an automated middleman that validates requests for data. This allows you to share data and remain compliant with all the important acronyms: GDPR, HIPPA, CCPA, FCRA, ECPA, etc.
But you have to do it right…
With every data security and analytics initiative, there is a set of risks if the implementation is not done correctly. A truly “clean” data clean room will allow you to unlock data for your users while remaining privacy compliant. You can maintain role-based access, tokenized columns, and row-level security – which typically lock down particular data objects – and share these sensitive data sets quickly and in a governed way. Data clean rooms satisfy the need for efficient access and the need for the data producer to limit the consumer to relevant information for their use case.
Of course, there are consequences if your data clean room is actually “dirty.” Your data must be federated, and you need clarity on how your data is stored. The consequences are messy if your room is dirty. You risk:
Loss of customer trust
Fines from government agencies
Inadvertently oversharing proprietary information
Locking out valuable data requests due to a lack of process
Despite the potential risks of utilizing a data clean room, it is the most promising solution to the challenges of data-sharing in a privacy-compliant way.
To get the most out of your data, your business needs to create secure processes to share data and decentralize your analytics. This means pooling together common data with your partners and distributing the work to create value for all parties involved.
However, you must govern your data. It is imperative to treat your data like an asset, especially in the era of user privacy and data protection. With data clean rooms, you can reconcile the need for data collaboration with the need for data ownership and privacy.
2nd Watch can be your data clean room guide, helping you to establish a data mesh that enables sharing and analyzing distributed pools of data, all while maintaining centralized governance. Schedule time to get started with a data clean room.
Data governance is a broad-ranging discipline that affects everyone in an organization, whether directly or indirectly. It is most often employed to improve and consistently manage data through deduplication and standardization, among other activities, and can have a significant and sustained effect on reducing operational costs, increasing sales, or both.
Data governance can also be part of a more extensive master data management (MDM) program. The MDM program an organization chooses and how they implement it depends on the issues they face and both their short- and long-term visions.
For example, in the insurance industry, many companies sell various types of insurance policies renewing annually over a number of years, such as industrial property coverages and workers’ compensation casualty coverages. Two sets of underwriters will more than likely underwrite the business. Having two sets of underwriters using data systems specific to their lines of business is an advantage when meeting the coverage needs of their customers but often becomes a disadvantage when considering all of the data — but it doesn’t have to be.
The disadvantage arises when an agent or account executive needs to know the overall status of a client, including long-term profitability during all the years of coverage. This involves pulling data from policy systems, claims systems, and customer support systems. An analyst may be tasked with producing a client report for the agent or account executive to truly understand their client and make better decisions on both the client and company’s behalf. But the analyst may not know where the data is stored, who owns the data, or how to link clients across disparate systems.
Fifteen years ago, this task was very time-consuming and even five years ago was still quite cumbersome. Today, however, this issue can be mitigated with the correct data governance plan. We will go deeper into data governance and MDM in upcoming posts; but for this one, we want to show you how innovators like Snowflake are helping the cause.
What is data governance?
Data governance ensures that data is consistent, accurate, and reliable, which allows for informed and effective decision-making. This can be achieved by centralizing the data into one location from few or many siloed locations. Ensuring that data is accessible in one location enables data users to understand and analyze the data to make effective decisions. One way to accomplish this centralization of data is to implement the Snowflake Data Cloud.
Snowflake not only enables a company to store their data inexpensively and query the data for analytics, but it can foster data governance. Dynamic data masking and object tagging are two new features from Snowflake that can supplement a company’s data governance initiative.
What is dynamic data masking?
Dynamic data masking is a Snowflake security feature that selectively omits plain-text data in table or view columns based on predefined policies for masking. The purpose of data masking or hiding data in specific columns is to ensure that data is accessed on a need-to-know basis. This kind of data is most likely sensitive and doesn’t need to be accessed by every user.
When is dynamic data masking used?
Data masking is usually implemented to protect personally identifiable information (PII), such as a person’s social security number, phone number, home address, or date of birth. An insurance company would likely want to reduce risk by hiding data pertaining to sensitive information if they don’t believe access to the data is necessary for conducting analysis.
However, data masking can also be used for non-production environments where testing needs to be conducted on an application. The users testing the environment wouldn’t need to know specific data if their role is just to test the environment and application. Additionally, data masking may be used to adhere to compliance requirements like HIPAA.
What is object tagging?
Another resource for data governance within Snowflake is object tagging. Object tagging enables data stewards to track sensitive data for compliance and discovery, as well as grouping desired objects such as warehouses, databases, tables or views, and columns.
When a tag is created for a table, view, or column, data stewards can determine if the data should be fully masked, partially masked, or unmasked. When tags are associated with a warehouse, a user with the tag role can view the resource usage of the warehouse to determine what, when, and how this object is being utilized.
When is object tagging used?
There are several instances where object tagging can be useful; one use would be tagging “PII” to a column and adding extra text to describe the type of PII data located there. For example, a tag can be created for a warehouse dedicated to the sales department, enabling you to track usage and deduce why a specific warehouse is being used.
Where can data governance be applied?
Data governance applies to many industries that maintain a vast amount of data from their systems, including healthcare, supply chain and logistics, and insurance; and an effective data governance strategy may use data masking and object tagging in conjunction with each other.
As previously mentioned, one common use case for data masking is for insurance customers’ PII. Normally, analysts wouldn’t need to analyze the personal information of a customer to uncover useful information leading to key business decisions. Therefore, the administrator would be able to mask columns for the customer’s name, phone number, address, social security number, and account number without interfering with analysis.
Object tagging is also valuable within the insurance industry as there is such a vast amount of data collected and consumed. A strong percentage of that data is sensitive information. Because there is so much data and it can be difficult to track those individual pieces of information, Snowflake’s object tagging feature can help with identifying and tracking the usage of those sensitive values for the business user.
Using dynamic data masking and object tagging together, you will be able to gain insights into the locations of your sensitive data and the amount specific warehouses, tables, or columns are being used.
Think back to the situation we mentioned earlier where the property coverage sales department is on legacy system X. During that same time period, the workers’ compensation sales department is on another legacy system Y. How are you supposed to create a report to understand the profitability of these two departments?
One option is to use Snowflake to store all of the data from both legacy systems. Once the information is in the Snowflake environment, object tagging would allow you to tag the databases or tables that involve data about their respective departments. One tag can be specified for property coverage and another tag can be set for workers’ compensation data. When you’re tasked with creating a report of profitability involving these two departments, you can easily identify which information can be used. Because the tag was applied to the database, it will also be applied to all of the tables and their respective columns. You would be able to understand what columns are being used. After the data from both departments is accessible within Snowflake, data masking can then be used to ensure that the new data is only truly accessible to those who need it.
This was just a small introduction to data governance and the new features that Snowflake has available to enable this effort. Don’t forget that this data governance effort can be a part of a larger, more intricate MDM initiative. In other blog posts, we touch more on MDM and other data governance capabilities to maintain and standardize your data, helping you make the most accurate and beneficial business decisions. If you have any questions in the meantime, feel free to get in touch.
Insurers are privy to large amounts of data, including personally identifying information. Your business requires you to store information about your policyholders and your employees, putting lots of people at risk if your data isn’t well-secured.
However, data governance in insurance goes beyond insurance data security. An enterprise-wide data governance strategy ensures data is consistent, accurate, and reliable, allowing for informed and effective decision-making.
If you aren’t convinced that your insurance data standards need a second look, read on to learn about the impact data governance has on insurance, the challenges you may face, and how to develop and implement a data governance strategy for your organization.
Why Data Governance Is Critical in the Insurance Industry
As previously mentioned, insurance organizations handle a lot of data; and the amount of data you’re storing likely grows day by day. Data is often siloed as it comes in, making it difficult to use at an enterprise level. With growing regulatory compliance concerns – such as the impact of the EU’s General Data Protection Regulation (GDPR) in insurance and other regulations stateside – as well as customer demands and competitive pressure, data governance can’t be ignored.
Having quality, actionable data is a crucial competitive advantage in today’s insurance industry. If your company lacks a “single source of the truth” in your data, you’ll have trouble accurately defining key performance indicators, efficiently and confidently making business decisions, and using your data to increase profitability and lower your business risks.
Data Governance Challenges in Insurance
Data governance is critical in insurance, but it isn’t without its challenges. While these data governance challenges aren’t insurmountable, they’re important to keep in mind:
Many insurers lack the people, processes, and technology to properly manage their data in-house.
As the amount of data you collect grows and new technologies emerge, insurance data governance becomes increasingly complicated – but also increasingly critical.
New regulatory challenges require new data governance strategies or at least a fresh look at your existing plan. Data governance isn’t a “one-and-done” pursuit.
Insurance data governance efforts require cross-company collaboration. Data governance isn’t effective when data is siloed within your product lines or internal departments.
Proper data governance may require investments you didn’t budget for and red tape can be difficult to overcome, but embarking on a data governance project sooner rather than later will only benefit you.
How to Create and Implement a Data Governance Plan
Creating a data governance plan can be overwhelming, especially when you take regulatory and auditing concerns into account. Working with a company like 2nd Watch can take some of the pressure off as our expert team members have experience crafting and implementing data management strategies customized to our clients’ situations.
Regardless of if you work with a data consulting firm or go it on your own, the process should start with a review of the current state of data governance in your organization and a determination of your needs. 2nd Watch’s data consultants can help with a variety of data governance needs, including data governance strategy; master data management; data profiling, cleansing, and standardization; and data security.
The next step is to decide who will have ultimate responsibility for your data governance program. 2nd Watch can help you establish a data governance council and program, working with you to define roles and responsibilities and then create and document policies, processes, and standards.
Finally, through the use of technologies chosen for your particular situation, 2nd Watch can help automate your chosen processes to improve your data governance maturity level and facilitate the ongoing effectiveness of your data governance program.
If you’re interested in discussing how insurance data governance could benefit your organization, get in touch with an 2nd Watch data consultant for a no-cost, no-risk dialogue.
In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. High premium volumes show no signs of slowing down and make the American insurance industry one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence (AI) technology like machine learning (ML), insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen.
Insurance data is vast and complex. This data is comprised of many individuals with many instances and many factors used in determining the claims. Moreover, the type of insurance increases the complexity of data ingestion and processing. Life insurance is different than automobile insurance, health insurance is different than property insurance, and so forth. While some of the processes are similar, the data and multitude of flows can vary greatly.
As a result, insurance enterprises must prioritize digital initiatives to handle huge volumes of data and support vital business objectives. In the insurance industry, advanced technologies are critical for improving operational efficiency, providing excellent customer service, and, ultimately, increasing the bottom line.
ML can handle the size and complexity of insurance data. It can be implemented in multiple aspects of the insurance practice, and facilitates improvements in customer experiences, claims processing, risk management, and other general operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud, which plagues the entire industry. It is a big development in fraud detection and insurance organizations must add it to their fraud prevention toolkit.
In this article, we lay out how insurance companies are using ML to improve their insurance processes and flag insurance fraud before it affects their bottom lines. Read on to see how ML can fit within your insurance organization.
What is machine learning?
ML is a technology under the AI umbrella. ML is designed to analyze data so computers can make predictions and decisions based on the identification of patterns and historical data. All of this is without being explicitly programmed and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and are constantly learning. Ultimately, AI/ML will handle menial tasks and free human agents to perform more complex requests and analyses.
What are the benefits of ML in the insurance industry?
There are several use cases for ML within an insurance organization regardless of insurance type. Below are some top areas for ML application in the insurance industry:
For insurers and salespeople, ML can identify leads using valuable insights from data. ML can even personalize recommendations according to the buyer’s previous actions and history, which enables salespeople to have more effective conversations with buyers.
Customer Service and Retention
For a majority of customers, insurance can seem daunting, complex, and unclear. It’s important for insurance companies to assist their customers at every stage of the process in order to increase customer acquisition and retention. ML via chatbots on messaging apps can be very helpful in guiding users through claims processing and answering basic frequently asked questions. These chatbots use neural networks, which can be developed to comprehend and answer most customer inquiries via chat, email, or even phone calls. Additionally, ML can take data and determine the risk of customers. This information can be used to recommend the best offer that has the highest likelihood of retaining a customer.
ML utilizes data and algorithms to instantly detect potentially abnormal or unexpected activity, making ML a crucial tool in loss prediction and risk management. This is vital for usage-based insurance devices, which determine auto insurance rates based on specific driving behaviors and patterns.
Claims processing is notoriously arduous and time-consuming. ML technology is the perfect tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker/adjuster.
Why is ML so important for fraud detection in the insurance industry?
Below are the various stages in which insurance fraud can occur during the insurance lifecycle:
Application Fraud: This fraud occurs when false information is intentionally provided in an insurance application. It is the most common form of insurance fraud.
False Claims Fraud: This fraud occurs when insurance claims are filed under false pretenses (i.e., faking death in order to collect life insurance benefits).
Forgery and Identity Theft Fraud: This fraud occurs when an individual tries to file a claim under someone else’s insurance.
Inflation Fraud: This fraud occurs when an additional amount is tacked onto the total bill when the insurance claim is filed.
Based on the amount of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection toolkits. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. The ML approaches and algorithms that facilitate fraud detection are the following:
Deep Anomaly Detection: During claims processing, this approach will analyze real claims and identify false ones.
Supervised Learning: Using predictive data analysis, this ML algorithm is the most commonly used for fraud detection. The algorithm will label all input information as “good” or “bad.”
Semi-supervised Learning: This algorithm is used for cases where labeling information is impossible or highly complex. It stores data about critical category parameters even when the group membership of the unlabeled data is unknown.
Unsupervised Learning: This model can flag unusual actions with transactions and learns specific patterns in data to continuously update its model.
Reinforcement Learning: Collecting information about the environment, this algorithm automatically verifies and contextualizes behaviors in order to find ways to reduce risk.
Predictive Analytics: This algorithm accounts for historical data and existing external data to detect patterns and behaviors.
ML is instrumental in fraud prevention and detection. It allows companies to identify claims suspected of fraud quickly and accurately, process data efficiently, and avoid wasting valuable human resources.
Implementing digital technologies, like ML, is vital for insurance businesses to handle their data and analytics. It allows insurance companies to increase operational efficiency and mitigate the top-of-mind risk of insurance fraud.
Working with a data consulting firm can help onboard these hugely beneficial technologies. By partnering with 2nd Watch for data analytics solutions, insurance organizations have experienced improved customer acquisition, underwriting, risk management, claims analysis, and other vital parts of their operations.