Artificial intelligence (AI) is a big term in technology. It refers to computer programs that can do tasks that typically require human thinking. One type of AI that is getting lots of buzz is generative AI. This class of smart software can create brand new content like text stories, artwork, music or videos from scratch.
Understanding how generative models work today can help us imagine the future ahead for industries like insurance.
A Peek Inside Generative AI
Let’s peek inside one of the most advanced forms of generative AI called transformers. These systems break content down word by word. Then they predict what words should come next based on patterns.
For example, a system trained on millions of online articles would learn that the word “hero” is often followed by words like “rescued”, “defeated” or “vanquished”. So if asked to generate a new story about a hero, it can pull together strings of related words in a way that flows logically.
The more quality data they train on, the more realistic their imaginary outputs become!
What Are Transformers Used For?
Many areas of digital content creation, such as:
- Smart replies in chatbots
- Tailored marketing emails
- Predictive text on phones
- Automated reporting in business
And now…insurance!
Streamlining Insurance Claims
One major opportunity for generative AI is modernizing insurance claims processing.
Today, people file claims by mailing and faxing piles of paper documents to their insurer. Staff must manually read all these forms, letters, receipts and more to verify details and determine payout amounts.
This mountain of paperwork causes delays getting checks to policyholders. It also costs insurance companies tons in labor and overhead.
How Can Generative AI Help?
Smart models could automate many of the repetitive tasks humans now handle:
- Scanning – Instantly turn stacks of paper documents into digital files using optical character recognition.
- Extraction & validation – Identify and compile details needed to proceed, pulling them across various forms. Flag missing info.
- Anomaly detection – Analyze historical claims data to catch new claims that seem suspicious or fraudulent.
- Estimation – Suggest payout amounts based on expected costs for similar past incidents.
This automation means faster help for policyholders and major cost savings for insurers!
Personalized Risk Assessment
Calculating how risky a customer is allows insurers to set fair premium prices. Those at higher risk of accidents or disasters pay somewhat more to balance the costs.
In the past, risk tiers were very basic. But AI lets insurers harness vastly more data to predict risk levels personalized almost down to the individual.
What Data Can Improve Risk Prediction?
With smart models, tons of nuanced data can feed into personalized profiles:
- Specific medical conditions raise or reduce various health risks
- Certain driving patterns reveal safer vs riskier behaviors
- Exact home locations pinpoint exposure to weather events or disasters
- Integrating workplace safety plans provides more context
How Does This Help Consumers and Insurers?
More customized risk assessment allows insurers to price policies tailored to fit people’s unique situations. This gives policyholders coverage better aligned with their personal needs and budget.
Meanwhile, insurers enjoy more predictable models reducing surprises. This prevents unexpected losses downstream that drive up everyone’s premiums.
Hyper-Personalized Premiums
In the past, insurance companies could only look at basic categories when pricing policies. For example, pooling 40-year old women in one group and teens in another.
But today generative algorithms support ultra-specific personalization. Two neighbors with different health or driving can now pay customized premiums aligned to their unique risk profiles.
Examples of Personalized Premium Factors
Someone who..
- Owns a smart home with automated water shut-off valves pays a lower rate.
- Regularly texts while driving based on cell data analysis sees their auto premium rise.
- Has chronic illness gets life insurance costing 10% more than their illness-free friend.
Insurance is finally becoming truly personalized for policyholders!
Challenges With Adopting Generative AI
Despite lots of benefits, adopting bleeding-edge technology like generative AI has some challenges to thoughtfully address:
- Trust – Handing decisions over to mysterious “black box” algorithms makes some hesitant. Insurers need extreme transparency on how AI reaches conclusions.
- Bias – Models can unintentionally discriminate if they are poorly designed or trained on biased data. Continual bias testing is a must.
- Talent gaps – To build and run AI systems requires new technical skills like data engineering and machine learning. Retraining or hiring is key.
- Data risks – As AI relies on more customer data, safeguarding privacy and security becomes even more critical.
The Outlook Ahead
While generative AI for insurance remains in early stages, it’s potential is immense. As models keep advancing, they will reshape insurer operations and offerings for decades to come.
Rather than AI eliminating jobs, the future likely involves valuable melding of human strengths like emotional intelligence with machine capabilities like data processing speed. Together, this hybrid approach can take customer experience to new heights!
Conclusion
The future looks very bright for AI in insurance! Responsibly applied, generative models can greatly benefit both policyholders and insurers over the next decade. Streamlining claims and hyper-personalization are just the start. We’re on the cusp of an exciting new era of technological partnership between people and machines!
Ready to Bring Generative AI Into Your Insurance Operations?
From faster claims to smarter, personalized premiums generative AI isn’t just a future idea, it’s a competitive advantage happening now.
FAQ’s
How accurate are generative insurance models today?
Accuracy varies widely across different algorithms. In general, insurers should compare AI recommendations to human expert judgments until large-scale testing proves reliable performance over time.
Will AI take away many insurance jobs?
Low-skill clerical roles will likely decline first. But net job creation in technical and creative roles should rise substantially in the long run.
Can AI unfairly discriminate against protected groups?
Yes, if biases exist in the training data or algorithms themselves. Responsible development demands extensive bias testing and diverse tech teams building solutions, plus external oversight.
Is customers’ personal data safe when using AI?
Maintaining strict privacy and cybersecurity defenses remains extremely important, as AI expands risks of data breaches or misuse. Keeping customer data anonymous wherever possible is key.
