Emerging megatrends in our fast-changing world, which is increasingly affected by many global concerns, provide the insurance business with difficult challenges.
Successful insurers will be those that communicate openly and change their focus to a new business model, technology solutions, and people, rather than anyone working in isolation, as clients want an enhanced digital experience.
In today’s competitive world, business intelligence for insurance is a critical opportunity for key executives and decision-makers across the organization to use and make well-informed business decisions in order to sustain and expand their businesses.
Here are a few areas where if BI is leveraged will result in business growth.
Insurance firms must store and analyze massive volumes of data on a daily basis. A healthcare insurance company often gathers the following information about a customer:
And this is only a small sample of the information that an insurance company must review before and after enrolling a consumer in a plan.
To manage all of these process processes, insurance companies may use a popular ERP and CRM system. There is a lack of a complete platform that aggregates all of the data and provides important insights for senior executives to make critical decisions.
To obtain the correct conclusions, all of the data sets must be correctly processed and examined.
In the insurance industry, business intelligence aligns the data storage process in the following ways:
- Creating a data warehouse that stores all of your data in an efficient cloud platform, as well as using data warehouse virtualization techniques to keep your data safe on a remote server.
- BI portals can manage access permissions by limiting access to different types of data based on the user’s authorization level.
- Only claims adjusters and financial reporters have access to their assigned claims, and only the financial data they need to make strategic judgments.
Insurance Companies Use BI to Generate Accurate Reports
For insurers, business intelligence streamlines the report generation process. Even small businesses and startups must provide a variety of reports, including:
Internal expenditures and premium profits are tracked in financial reports.
To develop these reports, companies must undertake exploratory data analysis, predictive modeling, statistical analysis, and other complicated calculations.
BI Facilitates Predictive Modeling
Some businesses are embracing BI in more revolutionary ways than just storing data and generating reports. One of the most unique advantages is predictive analysis, which will most likely become the new industry standard in the coming years.
Past insurance claims are used by machine-learning algorithms to forecast client behavior and fraud situations. These prediction models are superior to the actuarial models that are currently in use.
In order to produce the best projections, insurance companies must collect this data on a regular basis. Furthermore, especially if you’re basing estimates on self-reported customer surveys, the data may be old or even erroneous.
For each claim, artificial intelligence (AI) and machine-learning models provide a unique predicted “score.” The more claims input to a machine learning algorithm, the more accurate the results become over time.
Benefits of Business Intelligence in the Insurance Industry
Businesses are adopting business intelligence at a faster rate around the world. Almost every industry and business function benefited from it, and the insurance industry was no exception. Companies who use business intelligence (BI) service applications and solutions have a number of advantages over those that don’t, including:
Any insurance company that wants to prosper in this highly competitive industry must have access to business intelligence and analytics. For many companies, the capacity to turn enormous amounts of raw data into actionable insights is a key value proposition. These insights can be priceless in terms of the unlimited possibilities they can uncover across the organization.
Combat Insurance Fraud
The insurance industry is rife with fraud. To be sure, it’s a sad reality, but it’s a reality nevertheless, and insurance companies must take steps to prevent fraud.
The entire cost of insurance fraud in the United States is estimated to be more than $80 billion each year, according to the Coalition Against Insurance Fraud. And insurance fraud doesn’t simply hurt insurers; it also has an impact on policyholders. Fraud can have a huge financial impact: When insurance firms lose money due to fraud, they frequently hike premiums and rates to recuperate their losses, resulting in a poor customer experience. According to the Federal Bureau of Investigation, insurance fraud costs the average American family between $400 and $700 per year in higher rates.
The good news is that insurance business intelligence software can tackle fraud in all of its manifestations, from inflating actual claims to fabricating data on application forms. Early detection is critical for effective fraud protection, and predictive analytics which blends artificial intelligence, machine learning, data mining, and predictive modeling is used by business intelligence to identify fraudulent claims earlier in the claims cycle. End-users can spot trends that lead to fraudulent activity and set up automated warnings based on these patterns by displaying the data in easy-to-understand graphics.
Build Efficiency Into Claims Management
Nothing irritates an insurance client more than a long and drawn-out claims process, and it’s easy to see why: For most clients, that claim is much-needed recompense for a stressful incident such as a car accident, property damage, or surgical procedure. A smooth claims process is critical to any insurance company’s performance since it improves client happiness while also reducing losses. A more efficient claims procedure also allows agents to address open claims faster, allowing them to focus on a larger number of consumers.
Claims handlers may get a holistic perspective of important business operations and performance, including open claims, with insurance business intelligence tools. Insurance companies can give their claims handlers access to extensive customer profiles by integrating business intelligence software with customer relationship management (CRM) systems. Handlers can use this capacity to review customers’ previous claims and other important information, allowing them to provide faster service and a more personalized experience.
Identify Profitable Opportunities
Insurance firms, like any other business, need to be on the lookout for methods to increase their profits. Executives must be able to see all aspects of the business from a single location and in a convenient format in order to do so.
Business intelligence software, as previously said, employs data analytics to build detailed representations from which users can glean actionable insights. Insurance businesses can utilize this skill to analyze market trends and make more strategic business decisions in addition to detecting fraud. Companies can also use these visualizations to track the performance of their numerous partner agencies, as well as products in their own catalog, and determine where a little extra effort and attention could result in increased profit.
Better Enable Your Sales Team
The odds are stacked against insurance business sales staff, who are tasked with handling a massive portfolio of insurance products that are often scattered throughout the globe. Sales managers must closely analyze the performance of each sales representative, product, and territory to see whether they’re fulfilling expectations in order to continuously achieve and surpass their team’s quarterly targets.
Insurance business intelligence solutions provide real-time reporting and detailed visualizations of individual product and agency performance, allowing sales managers and representatives to quickly identify which areas of the business are performing well and which ones require attention, as well as potential growth opportunities.
Optimize the Underwriting and Sales Processes
In the same way that BI solutions may help with claims management, they can also help with underwriting and sales.
In the form of data visualizations, insurance business intelligence may provide a full perspective of the stages of the underwriting process and the numerous actions that take place inside each stage. BI enables close monitoring of the process, the display of critical facts about each stage, the identification of potential bottlenecks, and the identification of chances for improvement.
Insurance business intelligence can help with upselling and cross-selling activities in the sales department. If you collect submissions like quote requests, BI can show you the total number of quote requests and segment them by product, region, and even firm size or industry (for commercial insurers). Through reports like the white space report, this data can assist your sales staff in identifying possibilities to sell more policies.
By recording support lines for each policy, BI provides a more full picture of sales potential. If a policyholder were to renew their business auto policy, for example, a sales person may look at and recommend associated policies like as commercial property insurance and workers compensation insurance to that policyholder.
Finally, BI allows both underwriting and sales teams to evaluate past submissions for underwriting comments, such as whether the policyholder’s premium was different than expected, the cause for the difference, and so on.
Discover New Opportunities Using External Data Sources
One of the most appealing features of insurance business intelligence is the ability to draw data from other sources in order to improve existing processes and uncover new opportunities.
You could, for example, combine data from the National Weather Service with policyholder data to construct weather prediction models. You might use these visualizations to track weather trends by region and send messages to policyholders in certain areas advising them to take action, such as advising them to park their car in their garage to avoid hail damage.
These types of clever reminders not only improve the customer experience, but they also provide opportunities for upselling. Using the same example, if a specific region is regularly subjected to specific weather patterns hurricanes, tornadoes, snowstorms, and so on you might proactively target policyholders in that region with offers for additional coverage, increased limits, or higher premiums to mitigate potential damage.
Weather pattern data is just one example of how insurance businesses can use BI to tap into external data sources; other examples include using financial reporting services to underwrite commercial policies or estimate risk and exposure.
How is business analytics used in insurance?
In the insurance industry, predictive analytics can assist insurers in identifying and targeting potential markets. Data can indicate behavioural trends as well as common demographics and features, allowing insurers to focus their marketing efforts where they are most effective.
Because there are 3.2 billion people on social media worldwide, these channels have become increasingly significant in discovering potential consumers. It’s also had an impact on customer service: almost 60% of Americans believe that social media has made finding answers and resolving problems easier.
How is business intelligence used in business?
Corporate intelligence, or BI, is an important part of an organization’s strategic planning process. It’s used for a variety of things, including tracking performance against business goals, performing quantitative analysis, reporting and data sharing, and identifying customer insights.
What is Artificial Intelligence in insurance?
Artificial intelligence (AI) is a technology that allows computers to perform tasks that would normally require intelligent human behavior. Gathering data, interpreting data using a model, and making judgments are some examples.
The usage of artificial intelligence (AI) has exploded across all industries. The availability of more data, more processing power, and shifting customer expectations have all accelerated AI development. We now use AI in almost every aspect of our livesoften without even realizing it. As a result, AI is fast expanding and opening up new commercial options. Across many industries, including insurance, it is disrupting and improving organizations. Customers, partners, and workers benefit from AI platforms and solutions used by IBM, Apple, Google, Facebook, and Amazon. AI is revolutionizing areas including underwriting, customer service, claims, marketing, and fraud detection in the insurance industry.
Background: AI technology has advanced tremendously in recent years and continues to evolve and improve all the time. It has improved its ability to recognize images, detect spoken phrases, and use unstructured data, all of which were previously difficult for computers to accomplish. Outstanding technology advancements, as well as a significant shift in customer expectations, are propelling AI forward. Higher computer power, memory capacity, cloud computing, big data technologies, and human and machine global connectivity have enabled machines to perform complex algorithms faster and handle more input data than humans. Furthermore, insurance customers, particularly millennials, have come to expect speedy on-demand services as a result of their previous experiences in other industries.
AI’s progress is also aided by the vast volumes of data available nowadays. The amount of data we currently generate is astounding, and the velocity at which it is generated has only increased the importance of data management systems like AI. AI is assisting insurers in making sense of huge data, whether it is structured or unstructured data (e.g., social media, wearables, telematics, sensors, news, weather, and traffic reports). Driverless test cars produced around 30 terabytes of data each day as of 2018, thanks to on-board sensors and digital maps. The vast amount of data is too much for statistical models to handle. AI delivers faster insights because it can perform complicated analyses and computations at a rate that humans cannot.
AI has the potential to have a wide range of effects on the insurance industry. Claims processing, underwriting, fraud detection, and customer service are all areas where it is now used. Many insurers, for example, are investing in virtual assistants such as chatbots to improve client experience. A chatbot is a computer program that can hold natural-sounding conversations with humans in order to complete specific tasks, such as answering inquiries. Chatbots are accessible 24 hours a day, 7 days a week to provide simple advise, check billing information, and handle frequent enquiries and transactions, in addition to answering questions. Startup Lemonade, Geico, Allstate, and Lincoln Financial are among the insurers that are currently deploying chatbots. Furthermore, machine learning approaches can be used to enhance claims management at various phases of the claim processing process. Machine learning models can use previous data, sensors, and photographs to swiftly assess the degree of damages and anticipate repair costs. Lemonade, for example, claims that their chatbot Jim settled a claim in 3 seconds as early as 2016.
Furthermore, insurers have access to a vast amount of big data, which is a key component of AI’s success. AI may be used to boost customer interaction, generate more personalized service and meaningful marketing messages, sell the correct product to customers, and target the right customer, thanks to the quantity of unstructured data.
According to a PwC report from 2021, insurance firms’ most effective use of AI is in the customer experience arena, while insurers’ top AI-related fear is the risk of cybersecurity breaches with AI technology. In the future, AI will allow insurers to shift from a “detect and repair” to a “predict and prevent” framework, allowing them to assist their customers in risk management and avoiding claims altogether.
Status: The insurance business is only getting started with artificial intelligence, with many traditional insurers experimenting with new methods to incorporate it into their day-to-day operations in anticipation of future technological advancements. AI is also being used by insurtech businesses to build solutions to expedite operations, improve underwriting models, and improve customer experience. While AI provides traditional insurers with possibilities to modernize, putting AI into practice is not simple. Due to constraints such as data quality, privacy, and infrastructure compatibility, traditional insurers may have difficulty integrating AI into their present technologies.
The NAIC established the Innovation and Technology (EX) Task Force to study technology advancements in the insurance industry. The Task Force provides a venue for state insurance regulators to debate innovation and technological advances in order to educate them on how these developments will effect consumer protection, insurer and producer oversight, and the state insurance regulatory framework. The Task Force will also address any new issues that arise as a result of insurers or licensees utilizing new technology such as artificial intelligence. Furthermore, the NAIC Center for Insurance Policy and Research has organized a number of AI-related events and webinars, the most recent of which was held at the 2019 Summer National Meeting.
In 2019, the Task Force formed an Artificial Intelligence Working Group to investigate the advancement of artificial intelligence, its use in the insurance industry, and its implications for consumer protection and privacy, market dynamics, and the state-based insurance regulatory system.
The Working Group established artificial intelligence regulatory guidelines, which were endorsed by the entire NAIC membership during the 2020 Summer National Meeting. The Big Data and Artificial Intelligence (EX) Working Group created a private passenger auto survey in 2021 to explore how insurers are employing AI and machine learning in this field. In the fall of 2021, the poll will be sent to insurers in nine states. In early 2022, the working group will receive a report summarizing the replies.
How does the insurance industry use analytics to manage risk?
Leading insurance companies are reimagining risk evaluation, improving client experience, and increasing efficiency and decision-making throughout the underwriting process by leveraging data and sophisticated analytics. They’ve developed agile capabilities for obtaining, testing, maintaining, using, and reusing data in their models.
Why is data analytics important in the insurance industry?
Modern insurance professionals are being empowered by data analytics, which provides them with the business insight they need to better understand their clients and design better products and services to satisfy their demands. Business executives will need to make smart data investments and have an agile IT infrastructure to respond rapidly as market demands change. Insurers will be able to use every byte of data to acquire deeper insights and generate profitable business results if they do so.
What is business intelligence with example?
This is the first in a series of articles called the Starter Guide. The Starter Guide to Dashboards, the Starter Guide to Dashboard Design, and the Starter Guide to Data Visualizations are all worth checking out.
So you have data, but what are you going to do with it? How can you turn raw data into something useful? This is a question that we’ve all questioned at some point in our lives.
Data-curiousness has been aroused by the influx of available data. How can you leverage the plethora of data at your disposal to influence decisions?
You’ve probably heard of Business Intelligence, or BI as it’s more generally known. Business intelligence literally translates to “becoming more intelligent about your business.” And the tools you use to do business intelligence define your approach. Data warehouses, dashboards, reports, data discovery tools, and cloud data services are examples of BI technologies. These tools allow you to extract information from your data.
There’s no denying that the Internet of Things (IoT) has altered how the general public accesses information. At the touch of a button, data is available. There is data everywhere, whether it’s fitness numbers from your smartwatch or monthly recurring revenue for a major enterprise corporation.
Do you or someone you know own a fitness tracker or smartwatch? Simple performance indicators such as daily steps, standing hours, and activity minutes are tracked by these devices. Consider the following insights from that data: Perhaps you need to raise your step count in order to fulfill a target? This information can assist you in making fitness-related decisions.
It’s up to you to select how you want to utilize and interpret data (consumer or corporate), analyze it, and make data-driven decisions when it comes to data. That’s business intelligence in action.
What are the benefits of business intelligence?
BI solutions are built to handle large amounts of data processing in the cloud or on your company’s infrastructure. BI tools collect data from many sources and store it in a data warehouse, where it is then analyzed using user queries, drag-and-drop reports, and dashboards. Lenovo was able to boost reporting efficiency by 95 percent across many divisions thanks to business intelligence. Several monthly reports were reduced into a single snapshot dashboard by their HR department. Through the use of BI, PepsiCo was able to reduce analysis time by up to 90%. Business intelligence dashboards make data analysis easier and more intuitive for non-technical people, allowing them to build stories with data without having to learn code.