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Artificial intelligence (AI) has been gaining popularity among businesses, and for good reason. As our society develops more and more advanced algorithms and models, innovative companies have been advancing revolutionizing their industries with the help of AI. In many cases, the technology has been helping these businesses to gain an edge over their competitors, and make significant strides in growing their market share.
SEE ALSO: The AI Revolution in Insurance: Risks & Solutions
In highly competitive industries, AI adoption is occurring more quickly than ever. Machine learning (ML) models can, in many cases, help to significantly increase employee productivity and accuracy. In this article, we’ll discuss how AI in the insurance industry can help to increase an insurer’s bottom line, as long as the accompanying risk is appropriately managed.
Benefits of AI for Insurance Companies
When deployed thoughtfully, Responsible AI can save a business significant amounts of money in reduced expenses. Increased accuracy in risk management and fraud detection can improve cash flow for an organization, and decreased expenses (such as labor costs) allow the company to re-invest in growth and expansion.
Enhanced Risk Assessment
Traditional risk assessment methods rely on historical data and manual analysis, which makes them time-consuming and prone to human error. Conversely, when supported by AI models, more innovative methods deliver more accurate results more quickly by analyzing vast datasets in the blink of an eye. More accurate assessment tools allow insurers to offer more competitive rates to low-risk customers, expanding their client base, and appropriately charge higher-risk customers.
Improved Fraud Detection
In the United States alone, insurance fraud costs the industry $308 billion annually. While AI likely won’t eradicate fraud completely, it does have the potential to detect a higher amount of fraud cases than human analysis alone. It continuously learns from new fraud patterns, and can assist in protecting insurers from financial losses due to false claims.
Claims Processing & Automation
Beyond predictive analytics, there’s another area where AI tends to excel when compared with its human counterparts: repetitive tasks within the claims processing procedure. Document verification, claim triage, and initial assessments are just a few tasks which can be accelerated with the use of AI, reducing expensive labor costs and providing a smoother customer experience.
AI Risk Management
While AI offers significant advantages for the insurance industry, it also introduces challenges that insurers must carefully navigate. From biased decision-making which can lead to major losses in customer loyalty to regulatory uncertainty and legal consequences, any one of these risks has the potential to ruin a once-thriving business.
Bias in AI Models
An AI model is only as good as the data on which it’s trained. If training data is flawed or contains historical biases, such as discrimination in underwriting or claims approvals, those biases will most likely be reinforced by the model. This can lead to unfair policy pricing, discriminatory claim denials, and potential legal repercussions. To mitigate the risk of AI bias, insurers must use diverse training datasets, conduct regular bias audits, and adopt explainable AI (XAI) methods to ensure transparency in decision-making.
Regulatory and Compliance Challenges
As AI adoption grows, so does the demand for AI governance. Insurance companies are naturally in a position to be trusted by their consumers, and regulatory agencies are increasingly aware of potential threats that accompany AI use. As they respond, many governments have passed legislation to oversee and regulate AI in the insurance industry. A few of these proposed regulations include:
- The Texas Responsible AI Governance Act,
- Canada’s AI and Data Act,
- The EU AI Act,
- Multiple pieces of legislation from Colorado, including one specifically regarding AI Life Insurance Regulation,
- And many more across the world.
Model Drift
Machine learning (ML) models evolve over time, and without proper maintenance, they can degrade in accuracy. This is called model drift, and left unchecked, it can cause irresponsible decision-making leading to a significant loss of trust in the model.
Balancing AI Risk and Benefits
To fully leverage the potential of AI while minimizing risk, insurers should protect themselves from all angles with a multi-prong strategy, including each of the following considerations.
Implementing AI Governance Frameworks
A governance framework is a set of standards and policies that act to protect an organization from both known risks and those that may not yet be understood. In the case of AI, these policies apply to the development of the AI model itself. These standards and policies are typically agreed-upon best practices within the industry to ensure that AI operates ethically and responsibly.
Transparent AI Models
Trust in AI-driven decisions is crucial, especially in high-stakes areas like underwriting and claims assessment. Explainable AI (XAI) techniques provide insights into how AI models reach conclusions, allowing insurers to:
- Detect and correct potential biases in decision-making.
- Provide clear explanations to customers and regulators.
- Strengthen overall trust and adoption of AI technologies.
By prioritizing transparency, insurers can ensure fairness and accountability while meeting compliance requirements.
Continuous Model Monitoring & Improvement
Model drift can be particularly dangerous in insurance. It can lead to inaccurate risk assessments and unfair policy pricing, negatively impacting both customers and the business’s bottom line. To combat model drift, insurers should implement a few precautions:
- Ongoing performance monitoring to detect shifts in model accuracy.
- Regular retraining and updates to reflect changing market conditions.
- Human-in-the-loop oversight to validate AI-driven decisions.
Book Your Demo
If you’ve implemented AI processes for your insurance company - or, you’re considering adding AI in the near future - and you’re concerned about risk management, book a demo today. The Lumenova platform can help you to deploy Responsible AI confidently.