Big data has become especially relevant to the insurance industry since 2019. Accenture reported that 79% of insurance executives believe they will crowd out competitors that do not use big data. Let's figure out how big data analytics influence insurance and claims processing.
Insurance has always relied on statistics and data analysis. Accident statistics and personal information of the insured are used to group people according to different risk categories and optimize premiums.
The insurance industry is currently facing many challenges: competition, globalization, interconnectedness, and technological advancement. Consumers can now easily compare insurance plans and have become very cost-sensitive. There is increasing:
- Pressure on the value chain
- Lack of understanding of marketing investments
- Lack of flexible pricing models and new products
- Fraudulent activity
Thankfully, these are all areas where big data analytics works miracles.
Big data algorithms can accelerate or substitute for human analytical thinking on tasks such as customer service, processing applications, and checking the history of the insured. Automation can save employees up to 50% of their time, so they can focus on profitable tasks.
Big data can computerize manual processes, reducing administrative and application processing overhead. In a competitive, cost-conscious environment, this decreases the cost of insurance, which will attract new customers.
Despite obvious applications of big data, the insurance industry is far from using big data widely. Bearing Point’s research shows the hesitation and conservatism of the field.
As you can see in the graph above, most companies are still in the exploration stage. Around 70% of respondents to the study said their organization has a long way to go towards unleashing the full potential of big data.
Triage Analytics for Healthcare
All medical facilities deal with crowded emergency rooms. This is exactly when a doctor has to quickly determine how severe a patient’s disease, condition, or trauma is, and what should be done with the patient. In a previous article, we discussed the risks of under- and over-treatment because of the triage process, both of which lead to serious or even fatal consequences. Triage analytics based on big data can significantly reduce these risks.
Let’s consider the business perspective. The Journal of Infection and Public Health published research on the impact of analytics on health services at King Faisal Specialist Hospital and Research Center. The project analyzed two major factors: patients’ stays in the ER and the percentage of patients who left the hospital without any treatment.
Many analytic techniques were used to provide recommendations on hospital workflows. First, Fast-Track was introduced to lower-acuity patients, i.e. those who can stay vertical. This reduced the length of stay at the ER from 20 hours to 12, a 40% improvement. Consequently, the percentage of patients who left without treatment dropped from 17% to 9%, a 50% improvement.
“As insurers collect more granular data about insurance consumers, state insurance regulators need greater insight into what data is available to the industry, how it is being used, and whether it should be used by insurers. While the use of big data can aid insurers’ underwriting, rating, marketing, and claim settlement practices, the challenge for insurance regulators is to examine whether it is beneficial or harmful to consumers.”
— National Association of Insurance Commissioners
When used in a healthcare facility, triage analytics can identify weak points of performance and offer enhancements.
“This vast and growing medical information that represents increasingly complex relationships and dependencies present a seemingly impossible situation. Most of the current analytics implementations are in silos and hence fail to capture the holistic picture, which at times lead to erroneous decisions and actions. For analytics to be effective, we have to make decisions and take actions based on integrated, relevant and timely information in a holistic manner. With the right information, at the right time, for the right person, we will be able to make a dramatic positive impact on our ability to provide better healthcare outcomes with lower costs and improved patient satisfaction, with the highest levels of compliance.”
— Jivendu Biswas, Director, Digital Platforms & Enterprise Operations Transformation, Wipro Limited
Data Triage: How it Tackles Storage Issues
Data triage is critical for effective storage strategies. Modern companies generate immense volumes of data, calculated in petabytes, that need to be readily accessible from the cloud. For small companies just entering the analytics ecosystem, it is practical to offload the storage to a data management contractor. Large hospital chains, on the other hand, have more complex problems and requirements. Sorting data by the anticipated need for accessibility enables medical institutions to lower the cost of data storage.
A model-driven approach requires an immense number of rules, as well as exceptions to these rules. Naturally, it is hard to capture them all and this sometimes leads to model-driven AI being dismissed.
“Triaging incoming data based on accessibility needs helps the system store, locate, and retrieve data in an effective and streamlined manner. This is not a new phenomenon for us. We are focused on the patients and on protecting their data, so we are very conservative about moving data to the cloud.”
— Don Franklin, Assistant VP of Infrastructure and Operations, Intermountain
Data storage for health systems still relies upon conventional, manual approaches. To effectively sort and store data based on speed of retrieval, data infrastructure must be built with immediate access at the core to avoid traditional data silos. Data triage technologies are critical.
Claims processing has always been cumbersome and time-consuming because it requires seamless orchestration of many stakeholders, which inevitably leads to errors.
Proxet has vast experience building triage tools for healthcare businesses. Additionally, we have worked on cell phone data triage solutions. Triage with the help of big data analytics has been a point of focus at Proxet for years. Triage segmentation tools will be used extensively in claims management in the near future.
“Soon, the huge potential of big data will be unleashed in conservative industries. Professionals, assisted by automated intelligence powered by big data, will be freed to focus more of their time on high-value tasks. Digital claims transformation will happen in the next few years.”
— Vlad Medvedovsky at Proxet, a custom software development solutions company
In conclusion, let’s review the advantages of claims segmentation with big data. First and foremost, it will automate claims handling, estimating target costs, and triggering workflows and follows-up claims. Second, it will flag potential fraud and unlawful actions, and provide human reviewers useful data visualizations. Finally, it will compute fraud probabilities in real time; speech recognition and analytics can detect fraudulent behavior and report it immediately. Fraud detection has become increasingly important with the rise in hacking, requiring preventative over reactive action.
Build a modern data stack by following best practices from data engineering experts. Learn about data maturity, data stack components, and how to build.
Data warehouses have emerged as a viable solution for collecting, analyzing, and leveraging data. Find out if your organization needs a data warehouse.