Insurance companies rely on data to make price policies. They collect money from customers and keep that to themselves for rainy days. Big data can have a significant effect on finance policing. This can turn into 30% better insurance services, 50-60% cost savings, and 70% fraud detection rates, which benefits both parties.
However, these benefits can only be acquired if the core insurance systems are modernized enough to have fresh data. Moreover, their integration with machine learning and AI would be much more cost-effective for insurance companies. Like any other industry, keeping this sector up-to-date with technology would help in better tracking, monitoring, and measuring risks.
Insurance core systems handle all the vital aspects of the business. Insurance companies must modernize core systems to improve customer satisfaction. This system is responsible for policy administration, claims management, underwriting management, billing and accounting management, reporting and analytics, and document management.
Why is Big Data in Core System Evolution Important?
If we see collectively, big data would raise 3 trillion dollars in multiple industries. This much revenue comes from basic adjustments made in the insurance industry and other industries that can only be done with data. From effective marketing to controlled risk management, data would be inevitable in one way or another.
Traditionally, insurance companies have leveraged other sectors for a long time. But with the use of technology and modern AI-based software, companies have seen positive feedback from customers and in the finances of companies.
Use of Data Analytics in Insurance
With the advancement in data technology, insurance companies can now better understand customer behavior, fraudulent patterns, risk prediction, and establish financial theories. Data analytics would create a better product chain in the insurance industry, with data-based decision-making and predicting clients’ trustworthiness.
This can help not only the industry but also the customers, who can better understand different companies in this competitive era. Algorithms that rank the best insurance offers specialized for customers would help in insurance pricing. Big data can automate claim development and claim payments in the evolution of insurance core systems.
Following are some usages of big data in insurance companies:
Customer Acquisition:
In this era, when behavioral psychology is used to target potential customers in marketing, the insurance sector can also maximize its effectiveness by retaining existing customers and attracting new ones. Big data from companies’ social media handles can be analyzed for customer profiling. For example, Twitter API accounts can be used to scrape data and track customers’ online behavior.
Effective Internal Processes:
Data-driven algorithms for customer personalized profiling are effective uses of data. A study on automation shows that it can save 43% of the time of employees in the insurance sector. They can quickly review the customer profile, billing history, risk class, and suitable pricing tag for enhanced customer service.
Risk Assessment:
Insurers’ whole business model revolves around assessing risk under specific conditions. If they can personalize the risk diversification portfolio, which can be done with big data, they can predict possible outcomes of different scenarios.
Insurers can debar deals with a higher risk percentage than positive outcomes. Customer data can be used for predictive modeling and handling such situations.
Cost Reductions:
Leveraging technology for cost reduction is not a new norm in the industry. Automating repetitive tasks can be cost-effective if companies replace them with AI chatbots. These chatbots can be trained using big data and run for different functions, like claim management and customer support.
Fraud Prevention and Detection:
Detecting fraudulent activities in this model has always been challenging for this business. According to an estimate, US insurance companies lose more than $80 billion yearly due to false claims. This can be detected using big data and AI predictive models.
Conclusion:
As the technology sector advances and integrates itself into every business and industry sector, it is becoming inevitable for businesses like insurance not to avail themselves of this opportunity. Running a business otherwise would result in a huge blow from its competitors. Hence, insurance companies have to evolve their core systems with big data to remain intact in the competitive environment. Integrating big data into insurance software can be a game changer for the insurance business.
