Vinod Singh, Chief Technology Officer at Concirrus, takes a look at the power of AI to transform insurance, but also the ethical questions regarding data privacy and more.
Much like the advent of the internet 25 years ago, the introduction of AI is changing everything about how we do business in insurance. Artificial intelligence has revolutionised the traditional claims settlement process by introducing efficiency, accuracy, and speed.
Just as we’ve seen business models that are entirely dependent on the internet, so shall we see those that are entirely dependent on AI, whether that is enabling improved risk assessment, fraud detection, claims processing, or customer service. It will make insurance more accessible and affordable, with instant quotes and multiple distribution channels.
However, the use of AI in insurance can be complex and bring both benefits and several pitfalls that must be addressed.
Bringing innovation to insurance
An overarching benefit of using AI within claims settlement is the reduction of human error. AI systems are trained on vast datasets, enabling them to analyse and interpret information with unparalleled precision. This accuracy minimises the chances of errors that might arise from human oversight, leading to fairer and more consistent outcomes.
AI’s ability to process and analyse a plethora of data in real-time is particularly valuable in complex claims cases. Rather than employing hundreds of man hours to check and index reams of information presented within a case, natural language processing algorithms can sift through policy documents, medical records, and legal precedents to determine the appropriate compensation accurately. This depth of analysis ensures that settlements are tailored to the specifics of each case, promoting fairness and transparency.
The speed of overall settlement can be increased significantly using AI. Automating the processing of data, coupled with machine learning to produce rulings based on all relevant information from a myriad of sources means that what could previously have taken weeks or months to conclude, can now be done in a matter of hours.
In tandem, the ability to utilise AI in claims settlement enhances customer satisfaction. The reduced waiting time and increased transparency foster a positive relationship between the insured and the insurance provider. The availability of online portals or chatbots also enables claimants to track the progress of their claim, providing a sense of control over the process for claimants.
Pitfalls of artificial intelligence
AI, whilst developing at an incredible rate, is still artificial at its core. Whilst emulating human intelligence, it lacks human empathy and contextual understanding. These are critical elements in claim settlements; being able to understand the context of a situation and the impact it is potentially having on the claimants is vital to insurance companies and their teams being able to deliver a positive service to their customers.
Claims often involve personal and sensitive matters, such as accidents, injuries, or property damage. Humans possess the capacity to understand and empathise with the emotional turmoil that claimants might be experiencing. AI, on the other hand, lacks this emotional intelligence, potentially leaving claimants feeling unheard or disregarded. Non-human decision making can miss vital factors, often hidden within context that experienced staff can identify, and the insurance industry needs to work out how this can be resolved.
The “black box” problem is another challenge. AI algorithms can be highly complex, making it difficult to decipher how a specific decision was reached. This lack of transparency can erode trust, as claimants and even insurers might struggle to comprehend the basis for a particular settlement. Ensuring algorithmic transparency is crucial to maintaining the credibility of AI-driven decisions.
AI can also unintentionally promote bias. Drawing from reams of historical data, machine learning models may inadvertently incorporate existing bias present in society which, left unaddressed, can continue through AI models, leading to unfair or discriminatory outcomes. For instance, an algorithm might systematically undervalue claims from certain demographics or geographic areas, perpetuating inequalities.
The use of AI within the insurance industry, and beyond, is currently unregulated which is also a huge challenge that needs to be addressed. Individual businesses building their own AI models can either factor in or exclude elements that are fundamental to accurate and fair settlement of claims, posing ethical questions over how AI can be regulated now and in the future.
Positive steps forward
Changes are afoot, however. The EU and USA are currently developing AI regulations, with the European Union developing the AI Act; a comprehensive regulation that would govern both the development and use of AI in the EU.
As part of its National AI Strategy, the UK Government also published a white paper on AI regulation which was updated in August this year, setting out a number of proposals for how to regulate the development and use of AI in the UK, prioritising safety, fairness, transparency and accountability as its core pillars.
Undocumented AI decision-making leaves the entire insurance industry potentially exposed. Regulators are discussing to what extent they may require companies to document and disclose their procedures used by their own AI systems. Insurance companies must strive for transparency in AI-driven decision making. Providing clear explanations of how decisions were reached can alleviate the “black box” problem, enhancing trust among both claimants and stakeholders.
Regulatory bodies could also implement audits as a means of ensuring companies are working in accordance with their documented procedures, reducing the risk of errors and bias in AI decision-making.
Central to positively moving forward, and reassuring customers, is to use AI to augment human expertise, not replace it. Drawing in additional intelligence, quickly and efficiently using AI benefits the process, but it must work in tandem with human employees checking the process and outcome.
Incorporating human oversight at critical stages can also address empathy and contextual understanding gaps. Humans can review complex or contested cases, ensuring that the decision aligns with the spirit of the policy and the unique circumstances of the claimant.
When it comes to bias, efforts should be made to identify and rectify prejudice in AI models. Regular audits of algorithms and continuous monitoring for fairness can help ensure that decisions are free from discriminatory outcomes.
Overall, implementing a hybrid model that combines the strengths of AI with human judgment can provide the best of both worlds until the AI evolution reaches a level where this can be removed. Complex cases can be routed to human experts while routine claims can be efficiently handled by AI – striking the balance between innovation and ethical safeguards.
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