Artificial Intelligence Within Insurance

Throughout the ages, the insurance industry has proven its resilience and adaptability to changes, from the scribe to the digital age.

With the dawn of advanced machine learning algorithms, cognitive technologies, collection of data (≥40 trillion gigabytes of data to date). Insurers are bringing in more information to better gauge risk and offer “tailor-made” premium pricing. On the back end, AI is streamlining the insurance process to connect applicants with carriers more efficiently and with fewer errors.

It is predicted that the AI industry is due to increase by 14.5%, across the financial industry by 2030, which will have a direct impact on retail & commercial consumers, as well as those within the insurance industry.

This brings in several lines of questioning which I shall go over in further detail.

1.   What Is AI?

2.     What Is AI In Insurance, & How It Can Affect The Industry?

3.     How AI Can Help Insurance Companies

4.     AI In Insurance Claims

5.     Does AI In The Insurance Industry Benefit The Consumer

6.     Will AI Replace Insurance Brokers?

7.     Conclusion

1) So, What Is AI?

First and foremost, what is defined as AI?

Oxford dictionary 1) the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Alan Turing’s definition would have fallen under the category of “systems that act like humans”.

In essence, the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

These can then be breakdown into further categories;

Artificial Intelligence (AI)

AI is a subfield within computer science associated with constructing machines that can simulate human intelligence. AI research deals with the question of how to create computers that are capable of intelligent behaviour. 

Machine Learning (ML)

ML is a subset within AI associated with providing machines the ability to learn from experience without the need to be programmed explicitly. Or in simple words, ML or machine learning is a part of AI. So, while all ML models are, by default, AI models, the opposite may not always be true.

Deep Learning (DL)

DL is a subset of machine learning, which is a subset of artificial intelligence. Deep learning is concerned with algorithms that can learn to recognize patterns in data, whereas machine learning is more general and deals with algorithms that can learn any kind of task.

Artificial neural networks (ANNs)

ANNs a branch of machine learning models that are built using principles of neuronal organization discovered by connection, in the biological neural networks. These connected units or nodes, called artificial neurons. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some, non-linear function of the sum of its inputs. An example will be explained within section twos final paragraph

2) What Is AI In Insurance & How Can It Affect The Industry?

Historically, insurance underwriters have relied on applicant-provided information to assess clients’ insurance risks. The trouble, of course, is that applicants could be dishonest or make mistakes, rendering these risk assessments inaccurate.

There are three principal categories of change initiated by AI systems within the insurance industry

The first is the way in which insurance, can be front facing, such as customer services & sales, replacing brokers or support teams with chat bots, in order to either streamline services, or unburden staff with medial tasks such as issuing documents. The benefit the insured may find is the quicker response time from such chat bots and policy documents being issued.

Further along the chain, digital technologies, such as apps, offer assistance and support claims reporting. This is seeing a shift in the insurance being a reactive to proactive, or preventative industry. Engaging insurers and insureds to evolve into a  “detect and repair” to a “predict and prevent”. This could furth shift to a entirely new model of the future, becoming risk management solution, opposed to the historic compensating of losses to third parties.

The second change is the automation of business processes (processing of contracts, reporting of claims) and decisions (underwriting, claim settlement, product offerings). While personal lines have widely adopted these methods for some years, specifically health insurance, using large data pools and applying AI, will stimulate a further wave of automation, enabling insurers to potential provide cost saving methods to individuals.

Lastly, whilst the first to focus on the related impact of AI, the third category revolves around integrated AI. As an example, allowing “X” companies to communicate simultaneously and reporting information directly. A simple example of this, would be in the event of a collision of two vehicles, automated reports and images, would be issued to all (relevant) parties and allowing the AI to “assume” responsibility of either party. Moreover, to this, with automated vehicles being more prevalent, who would be deemed to be liable for “X” circumstances? Perhaps a fundamental shift will arise due to this?

3) How AI Can Help Insurance Companies

Machine learning, specifically natural language understanding (NLU), enables insurers to pore through more abstract sources of information, such as social media, online reviews, public accounts information, and or other information from data brokers. With more information readily available, to insurers to review, can allow a more accurate risk assessments mean more appropriate premiums.

Aside from scope of rating factors, AI data can also investigate.

Detecting Fraud:

Fraud poses a significant concern for insurance companies, and artificial intelligence (AI) plays a pivotal role in combating fraudulent claims. Cognitive Machine Learning (CML) algorithms have achieved an impressive accuracy rate of approximately 75% in detecting fraudulent insurance claims. These algorithms furnish comprehensive information on suspicious claims, including potential liability and repair cost assessments, and recommend procedures to enhance fraud protection.

Reducing Human Error:

The process of securing insurance coverage is continually evolving. As a wholesale broker, we may serve as one of several intermediaries in the chain, introducing the possibility of errors. AI is increasingly addressing this potential issue. Algorithms can minimize errors and reduce processing time as information transitions from one source to the next. By utilizing a portal and uploading a PDF, insurers can diminish data entry requirements. Those immersed in AI aspire to not only decrease errors but also bridge the gap between the insured and the insurer, recognizing the importance of accurate data for both customers and insurers. The hypothesis is that, with better data, insurers can develop more precise products, and customers can acquire precisely what they need. CML proponents believe AI can significantly improve consumer advice automatically.

Customer Service:

In an industry as traditionally resistant to change as insurance, excellent customer service remains paramount. Poor customer service often prompts people to discontinue using companies. This is why many insurance company websites now feature chat bots, AI tools capable of guiding customers through various inquiries without human intervention. Moreover, these chat bots are accessible 24/7, providing continuous support unlike many human teams.

4) AI In Insurance Claims:

Insurers primarily exist to process claims and assist customers in covering them. However, claims assessment is a complex task, involving the review of multiple policies and meticulous scrutiny of every detail. AI can expedite this process. Machine learning tools swiftly analyze the elements of a claim and predict potential costs. They can assess images, sensor data, and historical insurer data. The insurer then reviews the AI's results to verify and settle the claim, resulting in mutual benefits for both the insurer and the customer.

5) Does AI In The Insurance Industry Benefit The Consumer?

The widespread adoption of a particular technology in an industry often indicates benefits for companies, sometimes with no apparent impact on the consumer. However, this is not the case with AI in the insurance industry, where clear advantages accrue to the customer. AI-assisted risk assessment facilitates insurers in tailoring plans to the specific needs of customers, ensuring they pay only for what they require. It also reduces human errors in the application process, increasing the likelihood that customers receive plans that suit their needs. Additionally, AI expands customer service options and streamlines the claims approval process, ultimately ensuring that customers obtain precisely what they need.

6) Will AI Replace Insurance Brokers?

The insurance industry has only begun its foray into AI, and companies are already experimenting with new ways to incorporate it into their day-to-day operations in anticipation of further technological development. At present, it is used for menial, repetitive task, & agents are still arguably “in charge”, within a commercial setting.

Within the personal lines setting, excluding those on a “non-standard” basis, we may see AI slowly replacing brokers, and other supporting staff. However, I strongly believe that we will always want to speak to a human opposed to a machine.

7) Conclusion

As detailed throughout this article the needs & demands for AI to provide aid in analyzing an array information is certainly required in an ever developing / advancing world. Risks & advancements in technologies do require the need for AI in allowing insurers to examine & underwrite policies.

However, what is undeniable is the need for human interaction. Commercial brooking is a craft which will required for years to come. I do not believe that the art of negotiation, asking the “right questions”, understanding the needs and demands for the insureds coverage & the ability to deliver this information. Moreover, thinking “outside the box” to provide solutions cannot be writing in AI coding / algorithms.

Another consequence to the above, sometimes insureds does not always understand their own needs. We all know, that as a species we think we know best, we know our industry and our flaws. Of course, this is not always the case. The job of a broker is always to advise the client of their needs, especially when the insured is unaware of what losses they could potential have in the future. Our aim is always to predict such events to ensure the coverage is well suited.

Of course, we have seen those personal lines insurance has been moving in this direction for several decades. Naturally the lack of human interaction, does have its flaws. The most common demand, of trying to save money, tends to leave insureds focused on saving money, and consequently being under insured (not what you want when claiming).  Therefore, the need to speak to a professional is still paramount for insureds.

The second flaw, is the inability to communicate effectively, leading to frustration with an already begrudging & compulsory purchase. Though the use of chatbots can be of great benefit for small queries and insured may have, ultimately the personal touch can never be replaced. As we are all commonly aware that in the event of claims there is always an essence of “sensitivity” which must be adopted which, at present, cannot be replicated with AI.

Until the above mentioned is inherent within AI, then I feel that our jobs are secure for now….

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