Tag Archives: predictive analytics

What Predictive Analytics Is Reshaping

Insurance is a business sector where predictive analytics software has some of the most straightforward applications, also with a high return on investment (ROI). Predictive analytics is already offering companies significant savings, and it is expected to grow exponentially in the next few years. Most likely, it will become the standard practice for insurance and risk management.

The advantage is that the data lake used for predictive analytics can collect both internal and external data and correlate it to identify patterns and create almost real-time reports. In turn, this would prevent fraud and help to analyze behaviors.

The results can be used in various areas of the business, which include risk assessment, pricing policies, claim processing, fraud management and trend analysis. Here are a few of the ways predictive analytics is reshaping the insurance sector:

Pricing

This is one of the first applications of predictive analytics in the insurance sector because it offers a high ROI. As sources become more diverse and precise, results will be more actionable. Although there are relevant security and privacy issues involved, insurance companies are collecting and analyzing data from sources that, for example, 10 years ago were not available or considered relevant, like social media.

The good news is that now data is no longer an average of a cluster, but, after the general profile is created, the machine can measure how each person scores against the grid.

Market Trends and Risk Assessment

Identifying market trends is all about detecting the right patterns in data and anticipating their further development. Fortunately, AI is perfect for doing just that, regardless of the volume or complexity of the input data.

Recently, both the U.S. government and the E.U. ruling organs have adopted an “open data” policy, making available lots of census data related to population statistics, education, safety and more. These new sets offer insurance companies new opportunities regarding macro risk assessment.

See also: 3 Ways to Optimize Predictive Analytics  

Correlating these sets of data within the right algorithm can help insurance companies to create clusters of customers grouped according to their profitability. For example, such analysis can provide the answers to questions like the probability of a person being involved in a car accident in a certain town, or the likelihood of default for a mortgage for a specific educational profile.

The next step is to extrapolate the results and make predictions for the following periods to stay ahead of the market.

Fraud Detection and Prevention

Insurance is a very vulnerable sector for fraud. People are tempted to pay for an insurance policy and “make it look like an accident” to collect the value of the insurance. Although over the years insurance inspectors have become well aware of classic schemes, new tools are needed because the insured risks become more diverse and linked to digital activity.

The Coalition of Insurance Fraud estimates that over $80 billion is lost due to fraud. The same studies show that one in 10 claims is fraudulent. Therefore, insurers are ready to go to any lengths necessary to prevent such actions.

The advantage of predictive analytics is that it can signal potential fraud before it happens. The machine would identify specific patterns associated with fraud, usually by means of dots that don’t connect.

Tailor-Made Services

Most companies, from utilities to retail and especially e-commerce, strive to offer customers a very personalized experience. The insurance sector needs to be at the forefront of this practice, too, as products have few real differentiators apart from the price.

In this business, predictive analytics can look at customers’ profiles and predict needs, create bundles of services and help these customers meet their personal goals. Depending on a customer’s profile, such purposes can include increased safety, budget management, saved time or significant risk hedging.

These systems also offer the opportunity to prioritize claims and serve customers not only in their arriving order but also by evaluating their lifetime value, to avoid losing important ones who need their cases sorted faster.

Customer Retention

Learning from the world of retail and even HR, the insurance business can benefit significantly from identifying those customers who are about to cancel their policy. Usually, by giving these some extra attention, they can be kept onboard for another year or more.

In this case, data insights and customer behavior analysis can help insurance companies identify those who are already looking for solutions from competitors.

Focus on the Extraordinary

Not all odd claims are frauds, but unexpected and expensive claims can hurt an insurance company’s profit margins. In this case, the role of predictive analysis is to identify potential risks and warn the customer to take all necessary preventive measures. Although such outliers are harder to detect due to the lack of previous relevant data for training, the advantage of using machine learning is that it can put together several distinct pieces of information to identify potential risk.

See also: 3 Key Steps for Predictive Analytics  

Privacy Concerns

As in all matters related to the use of personal data, some people could have three categories of concerns, as stated by the report of the Geneva Association. To wrap up this discussion of data-centric insurance, let’s look at them:

  • Privacy and data protection concerns. These are mostly related to the fear of discrimination based on profiling. The other problem in this category is intrusiveness in the right of self-determination, especially when customers can’t afford the prime for their risk class, thus restricting their lifestyle options.
  • The individualism of insurance problems. The problem of exclusion should be at the forefront of insurance companies’ internal regulations. Excluding certain high-risk categories can lead to social pressure and the need to find alternative solutions such as state funding.
  • Implications of big data and AI for competition. The fourth technological revolution is already causing disruption and changing markets. By implementing these tools, we can expect that some jobs will disappear or reorganize. This will also happen to companies that will not adopt the new standards.

It’s Time to End Appeals Based on Fear

Consumer attitudes toward the insurance industry are changing faster than ever. Millennials make up the most populous generation today, and with many of them entering their mid- and late thirties, they are shopping for insurance in higher numbers. This tech-savvy generation expects personalized services and demands greater control over their experiences and decisions. Millennial consumers are calling the shots in almost every B2C industry – and insurance is no exception.

The insurance industry traditionally relied on the fear of the unknown as its most powerful sales enabler, but with millennials making decisions based on brand experience, insurers need to turn to emerging technologies to transform and customize the way they reach customers. The status quo is simply unsustainable if they want growth. Forward-looking insurers know that the key to attracting and retaining clients is to leverage predictive technology and provide them with the seamless, smart, digital-first experience they need.

But for this future to become a reality, companies need to implement and use predictive analytics in a way that truly enhances the customer experience. Here are the steps every insurer needs to know before embarking on that journey:

Collect the Right – Not the Most – Data

Knowing the ins and outs of customer needs and behaviors is essential in operating an insurance business, but it is not enough to know the general needs of a customer base. In fact, the majority of consumers are willing to share personal information in exchange for added benefits like enhanced risk protection, risk avoidance or bundled pricing. To deliver personalized service, insurers must collect data at the individual level – and quantity does not always mean quality. The accuracy of predictive analytics relies on the certainty and relevancy of the data those systems are fed. Before doing anything else, insurers must determine exactly what information drives business decisions and collect that data on both individual and grand scale as efficiently as possible.

See also: 3 Ways to Optimize Predictive Analytics  

This is where the Internet of Things (IoT) steps in. As one of the most ground-breaking technologies on the market today, IoT has only just begun to realize its potential in the insurance industry. IoT sensors attached to infrastructure, cars, homes and other insurable items, can feed real-time data back to providers with unprecedented accuracy. Not only does this live feed of data prevent emergencies by identifying potential problems before they arise, the highly precise information acts as a foundation for analytics at a customer-specific level in the next phase of the process.

Get Personal With Predictions

Once insurers are collecting relevant, accurate and individualized data, the next step on the road to customer satisfaction is applying machine learning and AI to that information. The outcomes of this analysis not only determine truths about the current status of an asset or situation but reveal patterns that enable insurance companies to predict what is in store down the road. For an insurer, this predictive knowledge means more accurately being able to evaluate, price and plan for risk – whether evaluating individual portfolios or aggregating data to foresee larger trends in the marketplace.

But as predictive technology becomes more mainstream, the true value of digital foresight will be its ability to offer the millennial customers the deep personalization and hyper-relevance they crave and expect from all their services. By transforming the industry into a predictive and even preventative experience, insurance companies are changing the status quo of fear-based customer relationships and instead leverage technology to make insurance feel tailored and assuring.

Engage With Emerging Technology

The insurance industry is not and never will be based on static, one-time decisions. As risk is calculated on various constantly changing variables, it is essential to continue evolving customer predictions, recommendations and prices based on incoming information. Analyzing both existing and new data from IoT sensors allows companies to pivot strategies in the face of new predictions, enhance underwriting, reduce claim ratio and remain agile to meet the needs of their customers today and tomorrow.

See also: What Comes After Predictive Analytics  

Just as predictions do not stand still, neither should an insurance company’s methods for determining them. In an era of hyper customer relevance, with disruptive players like Uber, Venmo and Mint, millennials have come to expect services that are not only predictive but get deeply personalized in accuracy and usability overtime. The insurance industry has traditionally lagged behind other B2C industries in terms of adoption, however, due to its changing customer base it will have no other choice than to evolve rapidly over the next few years. Placing emerging technologies like AI, machine learning, automation and IoT at the core of business operations now will be key in setting insurance up for continued progression in the future.

Appealing to the new generation of insurance customer is all about offering tailored experiences that cater to their needs and expectations. The insurance industry is in for an acceleration of change to accommodate their new millennial consumer – a change fueled by technology that creates bonds of loyalty and trust via personalization, not fear.

3 Ways to Optimize Predictive Analytics

A few years ago, simply applying predictive analytics to insurers’ underwriting practice was enough to gain a competitive edge against the large portion of the market that was still operating with traditional methods. That ship has sailed with increased adoption of analytics, raising the stakes for companies that once enjoyed a first mover advantage. Currently, 60% of insurers have welcomed predictive analytics into decision-making and underwriting processes, and research continues to show correlation between predictive analytics integration in the property & casualty industry and improvement to top and bottom lines. Companies that view analytics as a necessary commodity for modern underwriting instead of the centerpiece to their decision making will find themselves falling short of their competition. The biggest differences between the winners and losers in analytics today is equal parts ideological and technical.

In its recently published ROI study, Valen Analytics observed 20 insurance companies, representing $1.8 billion in premium, and compared their loss ratios and premium growth against the industry. The study showed that data-driven insurers consistently outperformed the market on both metrics.

  • Between 2012 and 2017, the industry saw its loss ratios improve by 18 points, whereas these 20 carriers averaged improvements that were nearly twice that (loss ratios improved by 35 points).
  • Between 2012 and 2017, industry-wide premium grew 18% on average, while the carriers studied grew by 53%.

For the first time since its inception, the ROI study isolated the impact of applied analytics on insurers with concerning loss ratios: those whose loss ratio were greater than 60%. This group of insurers saw loss ratios improve to market average within 12 months, and then outperform the market with each subsequent year. These results underscore the value of predictive analytics in insurance.

See also: 3-Step Approach to Big Data Analytics  

Below are three best practices that the insurers studied have implemented to draw the most value from their predictive analytics programs:

Empower underwriters

The considerably positive findings of Valen’s study do not imply that predictive analytics should replace traditional underwriters. Instead, research suggests that predictive analytics tools should aid traditional insurance writers. This year’s study found that underwriter performance improves 3x when they combine predictive analytics with expertise. A well-implemented analytics solution helps underwriters leverage powerful data that they wouldn’t be able to otherwise, and underwriters provide the expertise to make the final decision. In other words, an insurance underwriter’s wealth of knowledge and contextual expertise is a largely irreplaceable asset. Underwriters know the critical variances between the price suggested by the analytics model and the historical habits of a policyholder and can incorporate this information into their decisions. Thus, predictive analytics usage augments an underwriter’s decision-making process rather than supplements it.

Streamline the workflow

Predictive analytics enable insurers to accurately align price to risk exposure, helping underwriters price policies within the context of an insurer’s risk appetite, and oftentimes allowing insurers to implement straight-through-processing (STP) for specific types of risk. In doing so, insurers can eliminate the need for underwriters to be heavily involved in certain decisions and allow them to focus on the decisions that will have the greatest impact to a book of business. This, again, leverages the expertise of an underwriter.

Incorporate the right data

Insurers that have incorporated a consortium of anonymized data into their model-building initiatives tend to be better-positioned for growth. This additional information can be crucial to initiatives like expansion across states or business classes, often by identifying risks that might fall in a blind spot of institutional knowledge. In other cases, the incorporation of consortium data will eliminate sample bias in an existing book of business. For instance, an insurer that’s relied heavily on its expertise in knowing how to underwrite low-risk construction accounts in one state to build a data set that determines good risks in a new state will risk overfitting the model, essentially giving it too high a standard. This will leave an insurer vulnerable to underpricing risky accounts without third party data to balance the scales. Consortium data increases the predictive power of models and helped the group in our ROI study of analytically inclined insurers grow premium last year, even as the market declined.

See also: Global Trend Map No. 5: Analytics and AI  

For the third consecutive year, Valen’s ROI study has identified just how much value applied analytics can add to insurers. The carriers that have leveraged analytics and consortium data and empowered their underwriters have realized significant advantages over competitors to improve both profitability and growth.

Insurance and the Internet of Things

For many, the concept of a “smart home” is a futuristic, and perhaps even frivolous, offering where lights shut off automatically once we fall asleep, thermostats are controlled from your phone and security cameras can show you what’s going on in your home from thousands of miles away. However, as I have written in many previous posts, we are only at the start of the Internet of Things (IoT). Significantly more sophisticated devices are already entering the market and soon consumers will see the benefits of both enhanced personal safety and home protection. Forward-thinking insurance companies are not only recognizing the potential for reduction in non-catastrophic loses, they are embracing the potential by filing smart home discounts to create incentives for consumers who use these technologies.

Let’s look at a few of these enabling technologies and their potential for loss reduction/avoidance around the core perils of water, fire and theft:

1) Advanced home security products — The professionally monitored home security market has limited penetration in the U.S. — a significant number of home owners don’t feel the need to have their homes monitored for theft. However, many IoT devices enable basic self-monitoring features as a secondary benefit. From video cameras with 24×7 recording, to controllable door locks, to lights that are triggered on with motion, home owners are now getting home security features included with IoT products that might otherwise be purchased primarily for convenience.

2) Leak detection — Traditionally, these products focus on single points of failure, providing coverage in specific locations, such as below a dishwasher or a hot water tank. While providing a lot of utility relative to their cost, it’s been hard to programmatically prove loss reduction with these devices as the location of the sensor has so much to do with catching the leak. That said, more ambitious forms of leak detectors are entering the market, enabling whole-home monitoring, from flow sensors installed on mains, to lightweight stripping that can be installed in floor boards. Additionally, a series of whole-home shut-off valves are also being introduced into the market. Most of these valves require professional installation; however, they are capable of automatically closing the water main with the slightest detection of a leak or abnormal usage patterns. Water losses may be greatly reduced if a home could automatically respond to a burst pipe or an overflowed toilet.

3) Connected smoke alarms and “listeners” — Fire alarms have saved many lives, but the original design was intended to notify occupants of a fire so they could quickly exit. Unfortunately, if no one is home to hear a smoke alarm, there isn’t much that can be done by way of stopping a fire before a total loss. But the new generation of connected smoke alarms and “listeners” (an add-on that hears an alarm and sends a signal) can message not only the home owner but also a third party who can dispatch emergency crews on a homeowner’s behalf. It’s not hard to imagine how dramatic loss reductions will be when all homes have connected fire safety devices.

An exciting aspect of all of these enhancements is that they are incremental improvements on already approved safety devices, enabling a fast track of the actuarial analysis/regulatory acceptance of additional discounts. But these improvements are just the start…

See also: Global Trend Map No. 7: Internet of Things  

Connected devices are particularly special because the “intelligence” doesn’t necessarily need to reside on the device itself, but could also live in the cloud, where processing is getting more powerful and less expensive by the day. As such, there is a tremendous amount of innovation in the data analytics space — and here are a few technologies that will almost certainly result in greater loss reduction:

1) Real-time analytics — the more information that can be analyzed in real time, be it from multiple sensors or devices or historical data, the higher the accuracy in early detection of a potential loss situation. For instance, a sharp rise on a temperature sensor might indicate a fire, but it also could be caused from sunlight striking the device. Long-term tracking of that temperature data might quickly indicate what is normal, what is not. Or perhaps a flow sensor might detect a flow of water similar to shower running, but when paired with alarm system that shows the home is unoccupied and the alarm has been in “away” mode for several days could be a clear indication of a burst pipe.

2) Automated response logic — connected devices lend themselves well for automated responses. Homeowners will be able to create steps that are enacted when emergencies are detected. For instance, when a fire alarm rings, the sequence might be something like: a) snap a picture from each camera and take a temperature read from each sensor in the house, b) email/text all of the family that lives there with the data to confirm or override an emergency call, c) if no response within 60 seconds, forward the notifications to a third party for emergency dispatch. Automation combined with human intervention allows for a more accurate and effective response.

3) Predictive analytics — ultimately the best way to lower losses is to prevent problems before they start. This is where heavy processing power is required — as well as buy-in from consumers on the use of their data. Connected homes provide streams of output data and, with it, anticipated performance. Variances in this data might indicate early stages of problems. For instance, a packaged HVAC system might be showing degradation of airflow in the summer, which could mean trouble for gas heating as temperatures drop. It might be in the best interest of the insurance company to ensure performance is restored as the winter comes, prior to the risk of freezing pipes. Additionally, as we are seeing in telematics and auto insurance, you can bet that consumer behaviors will also have the potential to be analyzed, no doubt showing correlation between “safe” homeowners and reduced loss.

While more forward-facing than the device enhancements listed in the first section of this article, it’s these enhanced intelligence features that will truly revolutionize loss models. The more advanced the technology becomes, the less dependent the loss prevention becomes on human behaviors.

See also: Insurance and the Internet of Things  

Imagine a world where the main perils for homeowners insurance carriers such as water, fire and theft are dramatically reduced through the IoT and smart homes. Yes, consumer mistakes/negligence, even moral hazard, will always be an issue, but at some point it’s very possible the home will become smart enough to compensate even for these factors in a substantial way. We are already seeing rapid advancements in these areas in both telematics for auto insurance and wearables for life and health insurance. Similarly with smart homes, these IoT technologies have significant potential to lower losses from non-cat perils.

5 Key Effects From AI and Data Science

In the digital era of innovative products and services, insurtech technologies are bringing great opportunities to the insurance sector and accelerating the industry’s transformation. Advances in AI and data science are leading insurers toward the effective use of machine learning, data modeling and predictive analytics to improve back-end processes and streamlining and automation of the front-end experience for both consumers and insurance companies.

Here are five ways that insurance companies are applying AI and data to the industry:

1. Front-end sales, underwriting and policy service

Customers are acquiring insurance policies much faster and easier with the help of automated processes. These technologies differ depending on the systems that employ them and the people they serve. Integration gateways relying on data and AI are creating new customer experiences.

See also: Seriously? Artificial Intelligence?  

2. Back-end claim services

AI, IoT, predictive analytics and data modeling let insurers refocus claims so that it is easier to file, submit, adjust and reimburse claims. This means customers have their claims settled in an expedited manner. Patterns of fraud are detected, learned from and shared via modeling and the AI that combs them for key information.

3. Business intelligence and big data

Smartphones, telematics and sensors from wearables and connected homes provide a wealth of new data. In a connected world, insurers can generate insights from both external and sensor-based data sources. How this data is collected, stored and used will determine whether insurers will build or lose trust with customers. Take necessary measures to harden networks so that the threat of cybercrime is reduced.

4. Customer experience

Insurance companies need to offer their services in a way that encourages loyalty, customer retention and loss mitigation. This can be made possible by making policy acquisition easier and keeping policyholders engaged. It’s now common for insurers to monitor driving, health and home behavior through mobile apps and wearables. In exchange for the data, carriers offer lower or customized premiums to customers whose score reflects reduced risk.

5. Customized insurance

Carriers offer insurance packages and plans based on a matrix of factors. This requires their agents to possess extensive knowledge about products as well as their new and prospective clients. Through machine learning, millions of data patterns can be analyzed to identify the most appropriate customized plan or product for a particular customer. It can even be offered to them via AI.
Data modeling and artificial intelligence are advancing rapidly. They are laying the foundation of an industry equipped to quickly take clients from prospect to policyholder with minimal touch points and reduced risk.

See also: Motto for Success: ‘Me, Free, Easy’  

Where exactly these technologies will lead us next is anyone’s guess, but carriers have begun to realize the benefits. A historically slow-to-move, conservative industry is now more nimble, innovative and tech-savvy than ever before. Transformation is here!