Apart from the banking industry, insurance is perhaps the most critical sector in an economy. It provides businesses and individuals protection against financial risks and helps the government finance development projects without borrowing high-interest loans from local and foreign banks.
It is no surprise then, that governments encourage citizens to invest in insurance policies, to help secure themselves against future medical emergencies, business losses, and retirement.
That said, like all other sectors in the global economy, the insurance industry was also impacted by the devastating consequences of the COVID-19 pandemic.
The insurance market is experiencing unprecedented levels of asset risks, volatility in capital markets, and a lack of growth in the market.
In combination with the declining returns on equity in mature markets, these challenges have pushed insurance providers into investing in digital tools, like predictive data analytics, to improve operational efficiency.
In this post, we explore how predictive analytics tools are helping insurance companies improve their operations.
Insurance predictive analytics is an invaluable tool because it allows firms to operate smarter. Data analytics in the insurance industry helps agents anticipate future trends by analysing data, and these insights help them become more precise and accurate in their service offerings. For example, by assessing transactions in real-time, insurers can determine if certain requests or operations are safe or fraudulent.
In essence, insurance firms can transition from firms that expend plenty of resources to remediate damage to firms that prevent damage.
Before we dive into how predictive analytics helps insurance companies optimise their processes, it’s important to understand what different options are available for them. These include:
Now that we are aware of the different models available, we can dive into how they help insurance companies.
Predictive analytics in the insurance industry is applicable across a range of different functions and operations, all of which help companies work more efficiently than before. Here are a few applications of data analytics for insurance.
No two insurance policyholders are the same. Depending on their level of risk, they will be paying different premiums even if they are subscribed to the same policy. That’s why risk assessment is perhaps the most important step in the insurance policy application process.
Insurance providers have always used data analytics in this process, but the recent changes in the industry have compounded the need for more robust data analytics tools like predictive analytics.
Unlike traditional data analytics tools, predictive analytics utilises advanced machine learning and artificial intelligence algorithms. These collect, process, and analyse different types of data such as social media behaviour, criminal records, and credit reports to create a comprehensive and accurate risk assessment for each client.
Based on the predictive insights from these assessments reports, insurance providers can create a personalised pricing plan for individual customers.
In a traditional claims process, investigating each insurance claim takes weeks or even months, as investigation units use skills and experience to conduct their due diligence process.
Predictive analytics, however, helps insurers streamline this process by giving insurers the ability to anticipate events before they occur and prioritise claims—shortening the claims investigation time and increasing customer satisfaction in the process.
Predictive analytics tools can also reduce the costs associated with the claims process.
Researchers found out that more than 66% of insurers who used predictive analytics tools in their claims processes experienced a considerable reduction in processing and underwriting costs.
Using predictive analytics can also enable insurance providers to identify potentially fraudulent claims and take measures to prevent these claims from entering the investigation process, thereby reducing time and resource wastage.
Finally, predictive data analytics can help outlier claims—large unexpected claims that lead to losses for the insurance provider.
During the height of the pandemic, many businesses had to shut down and file for insurance payouts to keep their employees on the payroll. With predictive data modelling, insurance providers can predict these kinds of events in advance and handle the claims accordingly.
As we’ve established above, traditional insurance markets are maturing, and insurance providers are experiencing low levels of policy buy-ins.
Insurance providers need to stay profitable in this market and for that, they need to look to newer markets or even niche customer segments in the existing markets; predictive analytics can help insurers do this.
Using the available data, predictive analytics can identify behavioural patterns of target demographics and produce actionable insights, which insurance companies can use to create new insurance products that suit those demographics.
The insurance industry has always been one of the slowest to adapt to changes and utilise new technologies.
In the current business environment, however, this needs to change and the industry has to take a proactive stance, investing in tools like predictive analytics to improve their operational efficiency and profitability.
The insurance industry is one of the most important industries in an economy-not only does it provide a respite for individuals when they face financial losses, medical expenses, or retirement, but it also helps governments finance development initiatives to keep the economy stable.
In addition, insurance payments prevent businesses from bankruptcy and help keep essential employees at work. The insurance industry even has a hand in bringing food to our tables, as agri businesses rely on insurance payments to keep their fields and farms open despite floods, droughts and other natural disasters.
All of this means that insurance companies collect, process, and store sensitive information of millions of policyholders, making processing insurance claims an arduous and time-consuming task.
These companies receive thousands of insurance claims each year, and the amount of data to be processed and reviewed has created major bottlenecks in their due diligence process.
Sensing this vulnerability, offenders are filing an increasing number of fraudulent claims each year, with relative success, resulting in insurance fraud close to $80 billion annually.
That said, insurance providers are now looking for better, more holistic ways of investigating insurance claims. SAS Detection and Investigation for the insurance industry might be the tool they need.
When people think of insurance fraud, they assume that only the insurance companies are affected, though in reality, the repercussions affect every single policyholder paying premiums.
Successful fraudulent claims increase the premium for policyholders across all different insurance schemes. In the USA, for example, insurance fraud costs an average family between $400–$700 a year.
They also lengthen the review process of all insurance claims, increasing the waiting period for deserving customers, who require swift insurance payouts—leading to loss of customer loyalty, and ultimately, revenue.
Across the world, insurance fraudsters use different methods to manipulate the insurance claim due diligence process. In Europe, insurance fraud is an organised crime by gangs.
In other parts of the world, fraudulent insurance claims involve individual claimants inflating the value of their losses or damages to receive heftier payouts. Ghost broking is another emerging type of insurance fraud, where fraudsters target a single type of insurance product.
Although the traditional claim review process identifies the common traits of insurance fraud, it takes a considerable amount of time, and some claims can eventually slip through the cracks.
Traditionally, claim investigation units hold data related to all insurance claims, including historical claims data, policy details, and specifics related to the particular claim. These units operate independently of similar teams from other insurance providers, creating a data silo.
If fraudsters target new insurance providers for their claims, the lack of integration between different claim investigation units means that these claims may get approved without information on historical claims from other providers by the insurer.
Anti-fraud systems like SAS Detection and Investigation for Insurance help improve integration between departments of insurance providers, improving information availability across the industry.
Improved data availability means investigation units can identify obvious common traits of fraudulent claims across the entire network and flag fraudulent claims—reducing the probability of a fraudulent payout.
These systems can also help enforce a holistic due diligence process and help achieve the two primary objectives of insurance claim investigation; detect and flag fraudulent claims for an in-depth review, and approve legitimate claims of customers in need.
Insurance fraud is becoming an increasingly common occurrence, with one in ten claims being fraudulent.
In this vulnerable landscape, insurance providers need to work together and open up their data silos to enforce a robust claims investigation process across the industry.
Data analytics tools like SAS Detection and Investigation can streamline this process by reducing fraudulent claims and speeding up the review process, leading to high customer satisfaction and loyalty.
Much has been said about the power of data analytics in the healthcare and insurance space. Insurance companies around the world have especially benefited from powerful data analytics technology like SAS software – enabling them to slash costs and prevent any unnecessary losses due to fraud. At the heart of the reason behind any organisation adopting technology is a core commitment to optimising growth and cutting costs.
Thanks to the vast amount of unprecedented volumes of data and insights now available to businesses, processes have been transformed and significant benefits are being enjoyed. Among these benefits is the opportunity for cost containment and growth.
How does SAS software for insurance improve cost containment
The last thing any business wants, let alone an entity in the insurance space, is to have costs and unnecessary expenditures pile up. Unfortunately, in an industry littered with fraudulent activity, many insurance companies often deal with cases of improper payments going out unnoticed for long periods of time. The result? Losses that rack up and amount to millions of dollars.
A report from 2016 identified that insurance claims rose suspiciously by 30% despite overall casualties dropping by a proportional amount in Sydney. With such staggering levels of fraud taking place in other parts of the country as well, it goes without saying that insurance companies needed a solution to address this – analytics platforms like SAS software were that answer.
On one front, SAS software, in particular, has the ability to detect and flag any suspicious/fraudulent payments before it’s too late. With built-in predictive analytics and reporting capabilities, SAS software leverages insights from previous transactions and plots them against contemporary claims in determining potentially fraudulent claims.
Another area in which unnecessary costs and fraud takes place is in the form of false Medicaid, MCOs, and state employee/traditional health plan claims. Analytics platforms like SAS software have built-in functionality to rapidly detect and determine eligibility via an array of analytics tools and data sources.
Much like any other industry, trends and insights lie deep in the data. If leveraged correctly, these hidden trends provide insurance companies with access to a wealth of information that can help them better predict effective treatment options. Through this, insurance providers can ensure that their clients have access to the best healthcare at an extremely affordable cost.
Similarly, powerful data analytics tools like SAS software can help providers easily identify and predict which clients are at the highest risk of readmission. This level of insight guarantees that providers can choose the most cost-effective and appropriate member-specific interventions – ensuring that they can aptly balance their risk without incurring heavy losses due to claims.
Opportunities for growth
In the insurance space, generalities are a luxury that most companies cannot afford. Being able to predict risk down to an individual/standalone case becomes critical in cutting costs and paving the way for greater growth.
Equipped with the many years of experience loaded into SAS software, combined with trained SAS experts and consultants to guide the way, insurance companies can maximise and protect their interests and investments by focusing on targeted populations with the greatest potential for revenue growth.
With better access to data and insights, insurance companies can provide tailored incentives to specific groups of low-risk clients for better outcomes, while significantly reducing costs and improving overall access to high-quality care. Given that organisations would have access to a centralised portal of information that comprises records from the very first recorded incident and every other subsequent one, they would be able to better collaborate with a wide variety of caregivers and ensure high-quality care, with significantly better outcomes, at much lower costs.
Why should you consider SAS?
From advanced analytics to powerful customer intelligence modules to data management and case management functionality, SAS software has provided insurance companies around the world, not just here in Australia, with access to the complete picture of their operations.
While SAS software alone may not be sufficient to drive the results you desire, merging it with the expertise of industry-renowned SAS consultants can really take things home. Unsure of this? Here’s some proof.
As a SAS partner, we had the privilege of working closely with Bupa Australia, administering and consulting on their SAS environment. As a result of our 18-month phase one engagement, we were able to help Bupa Australia save an excess of $150 million in claims – according to their Information Delivery Manager.
While it’s easy to overlook the value of data analytics – especially through a reputed platform like SAS, the value of leveraging such a platform is massive. If you’re still unsure, read this case study one more time and see for yourself!
With many years of experience as a SAS partner, in addition to countless years of shared SAS experience among our team members, our team here at Selerity have allowed many of our clients to enjoy significant savings – sometimes in the range of millions of dollars. If you would like to know more about our SAS services, our company, and our work with SAS, in addition to how you can leverage our expertise, feel free to reach out to us, or stay tuned to this feed.