In this latest Insights piece, we take a look at how AI is transforming insurance; from quote to claim.
Machine learning (ML) is one of the many fields under artificial intelligence (AI) that allows computers to learn and adopt human behavior. Instead of being completely programmed, ML uses algorithms to constantly improve a system through learned experiences. These algorithms create mathematical models, enabling systems to make predictions and recommendations.
The explosion of data from various sources, such as online and social media activities, has overwhelmed insurance companies, as it has most businesses. Insurers are compelled to look for ways to use these data to their advantage. That’s why employing predictive modeling and machine learning are eyed as solutions to keep a competitive edge, advance business processes, and improve customer satisfaction.
If you want to explore the different ML models, you can visit this page online for an in-depth discussion regarding it. Meanwhile, you can read the list below and contemplate how machine learning applications can be beneficial to your insurance company.
- Better Customer Service
Customers often expect customized services that are tailored to their specific needs, preferences, and lifestyles. If your insurance company wants to boost its marketing strategy, using machine learning to create personalized insurance experiences is now a viable option for you.
Data on personal preferences, behaviors, perspectives, lifestyle, and interests can be used to generate customized products such as policies, loyalty programs, and recommendations. With machine learning, advanced selection and matching procedures can now analyze large data sets and produce specific suggestions for every client.
Moreover, you can use chatbots on messaging apps to resolve claim queries and answer simple questions on the front end. Likewise, you can also provide machine-generated insurance advice by using machine learning technology.
- Faster Claim Processing
Policy and legal regulations drive the insurance. A claim must meet certain criteria throughout the process cycle. Furthermore, claims processing also involves multiple phases such as review, inquiry, adjustment, and remittance. It’s a demanding and time-consuming task to deal with thousands of claims and client inquiries. consequently, speed is crucial to both your business and your client’s experience.
Machine learning has the potential to dramatically improve these procedures. From registration to settlement, you can utilize machine learning to improve operational efficiency by automating their claims processes and reducing claim settlement time.
For instance, document capture technologies use a machine-learning algorithm to verify the authenticity of documents. This way, businesses can speed up the management of huge amounts of documents required for claims processing duties, detection of fraudulent claims, and checking of legal compliance.
- Optimized Risk Assessment And Pricing Policy
Companies use the underwriting process to make application choices and price insurance products accordingly. With the growth of technology and advances in data analytics, the underwriting process can be automated to speed up application processing.
Client data may be analyzed by smart underwriting algorithms to develop more reliable customer profiles and discover risk more quickly. For instance, property and automobile insurance can use photo analytics to check the damage. Geospatial imaging for property risk analysis, as well as risk comparison systems based on deep learning and data science.
Accordingly, insurance businesses can lower risk exposure while also improving policy prices. Effective underwriting will pass the benefit to the customers by charging the appropriate rates for each application.
- More Robust Security
Fraudulent claims cost insurance companies billions of dollars each year. Nowadays, insurance companies have the technology to prevent this damage. With machine learning, you can flag probable fraudulent claims and investigate them as soon as possible.
An ML-based software can construct models of typical insurance claims by examining massive data sets of true claims. Any variation from standard claims will be highlighted as possible fraud, allowing insurance companies to reduce fraud risks and costs while achieving improved results. For this application, machine learning algorithms are more flexible compared to traditional predictive models because they can identify possible fraud for structured, unstructured, and semi-structured data.
For example, cluster analysis can uncover possible fraud in claims notes and documents, as well as database records. Similarly, deep anomaly detection or outlier detection gathers data patterns and identifies unusual events that are statistically distinct from the rest of the observations.
- Easier Compliance Of Data Privacy Regulations
The insurance industry’s regulatory framework is complicated and ever-changing. Keeping up with regulatory changes such as data privacy and putting them into policies is one of the most difficult tasks facing insurance businesses.
Machine learning systems can supplement human intelligence to assist companies to avoid noncompliance that may cost them millions in fines. In this situation, ML can help you by giving the best advice, asking required questions, monitoring actions, and doing assessments. Machine learning approaches, for example, may be useful in:
- Adherence to checklists and codes of behavior
- Assisting with risk evaluations
- Adherence to new restrictions governing the use of technology that does automatic profiling
- Adherence to the new regulations governing the detection and reporting of security breaches
With the proliferation of technology, the insurance landscape is expected to experience digital transformation as well. Therefore, you need to accept machine learning as an integral solution in boosting growth and improving operational efficiency. As in any investment, machine learning technologies may be risky but you have the power to turn those risks into benefits.