Digital Transformation in the Insurance Industry
This post was written by Dylan Kohlstadt
The rapid advancement of Artificial Intelligence (AI) and digital learning models has resulted in concern for job security across various industries, including transport, retail and digital art. This is understandable. With AI on its current trajectory, the global industry is expected to be earning $126 billion a year by 2025. Is this cause for concern, or is it a much-needed change?
Despite the advent of AI technology and its proven reliability and versatility over time, it also brought about much controversy. Most of this controversy and distrust comes from a widespread fear of the unknown and the potential cause for disruption.
However, rather than being disruptive, AI and digital learning software increasingly proved they are advantageous without threatening existing job roles. These advantages extended to the life insurance industry and proved the essential nature of insurance agents despite the numerous capabilities of machine learning and AI technology.
AI and human capabilities are nowhere near the same level; individuals must train themselves diligently to perform with the amount of perfection that comes naturally to AI; however, AI lacks the compassion and relatability humans can provide. Only 27% of global consumers believe that AI can deliver service that is the same or better than humans, which is less than a third of the population. Therefore, insurance agents and Machine Learning (ML) technology could synergise well in the insurance industry and compensate for each other’s weaknesses.
With revolutionary advancements in ML and AI occurring within the last few years, digital transformation could completely alter how insurance companies operate in the future – and for the better.
The Disadvantage of Using Legacy Systems
‘Legacy systems’ typically refer to aged systems, software and hardware that tend to hold businesses back from success and can also hinder long-term company growth.
Dated technology generally is not user-friendly and can result in frustrated employees and customers; if left unchecked, this discontentment can become widespread and damage the brand’s reputation. In addition, companies that fail to facilitate regular system updates and upgrades often face more difficulties down the line when legacy systems become incompatible with new tech and impossible to update.
Continuously updating systems and securing the latest technology is the best way for an organisation to become future-proof. Therefore companies must keep a close watch on emerging technology and big data trends. Unfortunately, many companies still use legacy systems and remain unaware that this can cause long-term damage.
Legacy systems include the following:
- Technology or software that no longer receives maintenance or support
- Systems that run on outdated technology
- Products that are no longer in circulation or available for purchase
Often many businesses are unaware of the importance of conducting security and stem performance audits to identify areas of weakness that require improvement. Here are some examples of legacy hardware and software to look out for:
Hardware:
- Mainframe computers
- Old personal computers with obsolete processing systems
- Network devices, such as routers and modems that are no longer supported
Software:
- Discontinued oracle software such as Peoplesoft
- Operating systems such as Microsoft Windows 7 (or earlier system models)
Companies must remain cautious and discontinue using software that does not comply with current standards, has no available security patches, or is incompatible with new systems and drivers. Hardware that is slower, less efficient and frustrating for employees should be phased out as well. Insurance companies can ensure their longevity by gradually removing legacy software and hardware from any business processes and replacing them with current technology.
Tech Advancements
Revolutionising Data Analysis
AI and ML technology are on the rise and gradually becoming integrated into data analysis processes as digital transformation takes hold. ML models can use historical data to identify data sets patterns and quickly produce actionable insights. Business decision-makers can use these actionable insights to improve and maintain the functioning of a company.
This approach to data analysis can help businesses navigate internal and external challenges, such as market shifts and management issues, in real-time.
An AI-based analytics system is significantly more reliable than a manual one and offers more than just pattern analysis and trend forecasting. As trends in the current market become apparent, machine learning technology allows companies to implement dynamic pricing accordingly; this means that pricing fluctuations within a company can keep up with real-time shifts in the economic climate.
Furthermore, companies can use AI for diagnostic and prescriptive purposes, allowing them to determine why something happened and what approach to take moving forward. Because the feedback from machine learning technology is so reliable, companies can quickly and confidently make informed decisions and stay afloat.
Middleware
Advancements in middleware mean that different departments of an insurance company can integrate and allow for the easy sharing of data and information. Middleware functions as a bridge that connects operating systems and the applications running on it.
Hybrid cloud services are an excellent example of middleware which is becoming increasingly popular. Cloud computation allows various applications to interact with each other to assist with dynamic workloads and to process big data. Hybrid clouds are easily scalable, less expensive than private clouds, and more secure than public ones.
Examples of hybrid cloud services include the following:
- AWS Outposts
- Azure Stack
- Azure Arc
- Google Anthos
- VMware Cloud on AWS
More automation means easier streamlining of company processes, increasing the speed and efficiency with which the company can complete tasks and improving business overall.
Improving Customer Experience
AI can provide interactive advisory services, health assessments and personalised product suggestions. Chatbots allow for easy navigation regarding FAQs and other customer interactions. Today’s AI-based chatbots have evolved significantly, becoming less scripted, rule-based, and better targeted. Bots can now respond better to uncommon questions with personalised answers. The language of chatbots has also become more nuanced, giving customers the experience of feeling heard and understood.
Diversified Products can now meet the needs of customers. In addition, AI systems can tailor and target market content to ensure that insurance companies reach potential clients and hold their attention. As new data collection and analysis avenues open up, companies can learn more about their clients personally, and marketing strategies can become more refined.
Digital transformation in the insurance industry means companies can challenge and change the typical associations of stress, paperwork, and time wastage. Insurance claim processes are now more straightforward and user-friendly, leading to better customer feedback and more business. This method is perhaps one of the most significant ways ML and AI can transform the insurance industry, as it is an industry with a reputation for being difficult to navigate.
Customers can now file claims, browse insurance policies, and pay bills via apps. The more user-friendly the process is, the more likely the company will appeal to new customers. Apps are yet another way insurance companies can future-proof themselves and avoid crumbling under the weight of accumulating legacy systems.
Fraudulent Claims
AI can take on other executive responsibilities, such as fraud detection and estimate and processing claims. Identifying patterns in large data sets allow companies to halt fraudulent claims before they reach approval. This kind of task can prove especially challenging without the aid of AI.
Visual image recognition is revolutionising the insurance claim process. Regarding car insurance, logistic regression models can isolate fraudulent claims such as stolen vehicles, faked car accidents and fabricated auto repair costs. Prior to this digital transformation, insurance agents would visit sites in person to verify damages claimed. Now remote visual image recognition can simplify the process. This remote access applies to other insurance types as well.
Virtual medical examinations that employ AI models are becoming increasingly popular concerning life and health insurance. While controversial, facial recognition software has the potential to prevent many fraudulent claims by verifying an individual’s identity. The latest advancements in facial recognition software can identify common health issues and how susceptible an individual is to certain diseases based on a selfie. At least one insurance company in China already uses this software.
Improved Data Security
An AI-operated system leaves less room for human error and reduces the likelihood of financial security breaches. As AI advances, there are increasing concerns around emerging security risks, such as adversaries tampering with machine learning systems to influence their output negatively. However, AI is now more secure than any of its predecessors.
AI-based cybersecurity systems can detect malware, identify risky behaviour and identify vulnerabilities that might lead to a phishing attack. This software can adapt over time to identify new attacks that could potentially put a company at risk. Another benefit of using AI-based cybersecurity systems is predictive and prescriptive analytics to project how and where a company is most open to a breach. These analytics remain helpful not just for insurance companies but for businesses everywhere.
The Changing Role of Employees
In essence, AI is efficient, agile, personalised and scalable. But does this leave room for insurance agents to continue to have as much value within their companies?
The answer is yes.
While direct-to-consumer models are appealing, there is nothing that can replace the reassurance and comfort of having somebody else’s input.
Despite the digital transformation, customers will continue to benefit from interacting with insurance agents. As AI becomes more present in the industry, the role of employees will shift. They will be invaluable in guiding clients through unfamiliar processes by developing and maintaining an expert understanding of increasingly complex AI-based technology. Agents will continue to be a critical component of the customer experience.
Employees will focus on other tasks that are arguably more ‘human’ while leaving AI in charge of high-pressure, complex processes like crunching numbers. Selling insurance, especially life insurance involves building relationships, actively listening and being sympathetic. Insurance needs also vary over time as personal circumstances change, and insurance agents could be significantly better at identifying what customers need in individual situations than AI.
These qualities are unlikely to be replaced by machines any time soon, and in all probability, the role of insurance agents will shift more towards customer support than administration.
Customers are becoming more accustomed to using automated processes across the board. By integrating AI into their existing systems and moving away from legacy technology, insurance companies can begin to future-proof themselves. By moving forward with machine learning technology and AI-integrated processes, insurance companies are not jeopardising their employees but rather assisting them.
AI vs Humanity
The transition from physical to digital had a largely positive impact on the sector.
AI is still immensely intimidating – its potential is seemingly limitless. But rather than being purely parasitic, it seems to be taking on a symbiotic role in many areas, with life insurance being one of them. Although there is no telling what the future holds, digital transformation and the current use of AI is such they it could make all facets of the insurance world more accessible to navigate for everyone involved.
The insurance sector benefits immensely from Artificial Intelligence and Machine Learning technology advancements, particularly in data analysis, data security, preventing fraudulent claims and ensuring customer satisfaction. By revolutionising how the insurance process works, new technology can improve the functioning and longevity of businesses everywhere. In harnessing the potential of these resources and moving swiftly away from legacy software and hardware, the insurance sector can challenge all its negative associations and revolutionise itself.
Yooma Life Insurance is the latest Life Policy administration system designed to assist your insurance company with client management and vital account information. We offer you simplified payment portals and channels, claims administration tools and sales systems, with an integrated Client Self-Service portal.
Contact Yooma Life Insurance via our website and future-proof your company today.
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