Introduction: Artificial Intelligence as the Latest Step in a Long Evolution
The topic of artificial intelligence and its role in changing the insurance industry, including underwriting, claims management, fraud detection and customer service is hotly debated today. However, it would appear to those observers with an understanding of the past that insurance is by nature already at its third or fourth wave of technological evolution which altered its perception of risk and how it is managed. So, to argue that we are entering a new phase of insurance in its pure data-driven sense, that will differ vastly from the past is flawed. Insurance has always been about predictive capabilities, probability and systematic decision-making long before computers, where insurers used statistical tables, actuarial formulas and historical information to establish risk parameters and pricing. So, if you define AI, its building blocks and core functionalities as what has historically been established within the industry’s principles then the essence has always been in existence for centuries. This time we’re just enhancing the existing practices through a higher capability in computing power, machine learning and in analyzing data on a bigger scale.
The Historical Roots of Data and Prediction in Insurance
To truly grasp the potential role of AI in the insurance sector it is necessary to first acknowledge that the industry has always had a heavy reliance on data analysis. Insurers had to rely on mortality tables, human underwriting and statistical inference to calculate risk exposures. Early forms of insurance had to cope with human limitations, but it was in essence that insurers predicted future occurrences based on prior evidence. This remains the fundamental basis for insurance today.
As societies became more advanced and data became more readily available, insurers further adapted. Early computing in the twentieth century became the key milestone where insurers gained the ability to manage large datasets and make faster, more accurate predictions at a faster pace than human limitations would allow. While still being rule based and non-adaptive, these early forms of computers laid the foundations for AI in insurance today, by proving that the better the data can be handled, the better the risk can be managed.
The Rise of Digital Automation in Insurance Operations
The next big change for the insurance industry was the onset of digitization and automation. During this period, insurers started to implement systems that were capable of performing repetitive processes with limited human input. Claims processing, policy administration and customer services all were digitized as the insurers wanted to increase operational efficiency. Early digital systems were largely based on a rule set. They were not capable of ‘learning’, but these systems automated processes that were slow and involved a high degree of manual input, therefore, making them quicker and more efficient. Fraud detection systems also grew in sophistication and could detect unusual claim patterns using the power of technology. Although the processes described can be more accurately described as system updates than intelligent systems they signal the move away from a purely human decision making system.
The Emergence of Artificial Intelligence in Insurance
The advent of artificial intelligence created a significant shift in how insurance companies manage data and make decisions. Unlike automation technologies used in the past, AI technologies can “learn” from large data sets, identify patterns and relationships and subsequently, in an adaptive and learning capacity, update their results and improve accuracy without being specifically programmed.
Today, AI is already being applied in various insurance lines of business. In terms of underwriting, AI algorithms can evaluate large amounts of both structured and unstructured data to provide a more accurate and personalized risk evaluation than would be possible using conventional underwriting rules. Insurance companies will then be able to make more profitable decisions regarding pricing. In claims, an AI system may process submitted documents, confirm claims, and may be able to approve relatively simple claims autonomously. This may have significant time savings and impact customer satisfaction favorably. Fraud detection has also undergone a dramatic shift with AI and machine learning. Instead of solely operating under fixed rules, machines can identify patterns of potentially fraudulent behavior that might go unnoticed by a human. This process continues to improve the more data it is fed. Customer service has also evolved in response to these changing technologies, as virtual agents and chatbot technologies can respond to policyholders’ questions and issues instantly.
Lessons from Previous Technological Shifts in Insurance
History is instructive for the way that technology has been and continues to interact with the insurance sector. All the way from early computing to today’s digital automation we’ve seen very similar reactions over three periods of innovation: distrust, then tentative embrace, and then gradual implementation. We’ve consistently seen that uptake varies: larger and older institutions will lag behind in implementing newer tech because of existing systems and regulatory requirements whereas newer, smaller and/or more nimble organizations will embrace new technology more quickly in pursuit of an advantage. Also, the reality of tech implementation has rarely been one of job displacement but rather one of added productivity to the decision-making process. This is because tech usually supports human decision-making rather than immediately replacing it in areas that need nuance and judgment. Regulatory influence will certainly be a factor as, with every new technology, comes new regulation for which insurers must comply. Finally, the importance of the underlying quality of the data that will be fed into the systems outweighs the importance of the sophistication of the systems themselves.
Challenges Facing AI Adoption in the Insurance Sector
In addition to the benefits there are also a few challenges to implementing AI in the insurance sector. Data privacy is one of the biggest concerns when adopting AI as large quantities of private data are involved which raises questions surrounding the methods of data collection, storage and usage. Another hurdle is regulatory uncertainty, as jurisdictions vary in rules for the usage of automated decision-making systems; this creates a problem for global insurers trying to conform to both local and global laws whilst also ensuring that their AI models are consistent. Algorithmic bias can also occur, meaning that AI models could yield unfair and wrong results if trained on unfair or incomplete datasets. This could result in damage to reputation and investigation by regulators. Many insurance companies are still running on legacy infrastructure which cannot be easily integrated with modern AI. Integrating new systems could be both complex and costly. The most important challenge is that over-reliance on automation may occur.
The Future of Insurance: A Hybrid Human-AI Model
Moving forward, the most probable outcome for the insurance industry will not be complete automation but rather a hybrid system where AI and human intuition co-exist and complement each other. AI systems will be employed for processing large quantities of data, automate redundant processes and recognize patterns while human professionals will be used for judgment, monitoring and customer interaction.
This collaboration allows the strengths of both the systems to be exploited efficiently by the insurance providers. While the former offers speed, accuracy and computational power the latter provides context, ethical considerations and emotional quotient to make the insurance processes efficient.
Over the long term, this hybrid system will become prevalent in the industry and ultimately evolve and integrate to not just augment insurance professionals, but redefine their roles and responsibilities within the insurance organizations.
Conclusion
Looking at the history of insurance technology, there is an undeniable and undeniable trend: change has been constant but incremental, not disruptive. From actuarial tables, to the move towards digital automation, and now artificial intelligence-every piece of technology has built upon what came before, not simply rendered it obsolete.
Artificial intelligence is the cutting-edge of the current iteration of this evolution, and offers abilities far beyond any previous innovation; however, its impact needs to be tempered. AI is not destroying the concept of insurance, but building upon it.
At its heart, technology is only one part of insurance; human expertise will always remain. If history has taught us anything, it is that even if the tools with which insurance is practiced will be different, the fundamental nature of insurance- managing risk through prediction – will remain just as consistent.
