Machine Learning in Insurance


In today’s time, where the massive technological advances redesign the insurance background, insurance companies must become more customer-centric, improvise customer service, develop better solutions for operational efficiency and generate more accurate underwriting models. The insurance industry always had data as their base to calculate risk and come up with personalization. Data has always played a central and critical role in the insurance industry, and today, insurance carriers have access to more of it than ever before. Today, the insurance sector is experiencing an intense digital transformation thanks to technologies such as machine learning. We all like our insurance carriers to completely be risk-averse. This is where machine learning is writing a brand new chapter in the books of old insurance methods. Insurance companies are using machine learning to upsurge their operational efficiency, improve customer service, and even focus on detecting fraud. 

As customers become increasingly selective about personalizing their insurance purchases to their specific needs, leading insurance companies are exploring as to how machine learning can expand business operations and improve customer satisfaction. Machines will play an important role in customer service, starting from managing the initial interaction to determining which cover a customer requires as per their inputs. The applications of machine learning in the insurance sector are various: starting from understanding the customer’s risk appetite and premium payment capacity to their expense management, litigation, and even fraud identification.

With increased awareness and resources available amongst the masses about the game-changing influence of Artificial Intelligence in the Insurance Industry, the initial resistance and slight discomfort around its implementation are now diminishing rapidly as now people have begun to trust in the caliber and several opportunities brought forward by Artificial Intelligence and Machine Learning. Artificial Intelligence within the Insurance industry has overhauled the claims management process by making it faster, better, and with minimal errors. 

From smart Chatbot’s that offer quick customer service round the clock to the group of machine learning technologies that spruce up the functioning of any workplace through its automation power, the increasing potential of Artificial Intelligence in Insurance is already being used in numerous ways. Many examples of insurance companies using machine language include names like Allianz, Allstate, Progressive, Aetna, Transamerica, QBE and many more.

Benefits of Machine Learning in Insurance

  • Better claim processing: Insurers are using machine learning to improvise the operational efficiency, from claims registration to claims settlement. Many companies have already started to automate their claims processes, thus enhancing the customer experience while diminishing the claims settlement time. Machine learning and predictive models can also prepare insurers with an improved understanding of claims costs. Claims handling is a very time-intensive task often involving manual labor by claims adjusters on site. Innovative companies already have manifested policyholders to take pictures and videos of their damaged assets (home, car, etc.) and compare it to baseline or alike assets. Companies could convincingly leverage existing API’s for image processing, coupled with chatbot APIs to build a high-precision model, even at the expense of low-recall. Such an effortless process will have clients filing their claims without much hassle.
  • Fraud detection and prevention: Modern machine learning is far more effective and efficient than static rules in detecting ever-evolving methods of fraud. Machine learning helps them identify possible fraudulent claims quicker and more precisely, and flag them for further investigation. Machine learning algorithms are way more superior to the traditional predictive models for this application because they can tap into unstructured and semi-structured data such as claims notes and documents as well as structured data, to identify potential fraud.
  • Risk Modeling: Given the multifaceted and behavioral nature of risk factors, Machine Learning is perfect for predicting risk. Insurance companies can use machine learning to predict the premiums and losses for their policies. Detecting risks early in the process would enable these companies to make better provisioning and would facilitate better use of underwriters’ time and gives them a huge competitive advantage over other companies.
  • Claim underwriting: Using Artificial Intelligence and Machine Learning, insurers can save a lot of time and resources involved in the underwriting process and tedious questions and surveys, and automate the entire process. Insurance bots using artificial intelligence can automatically explore a customer’s general economic condition and social profile to determine their living patterns, lifestyle, risk factors, and financial stability. Customers who are more consistent in their financial patterns are fit to feel safe by paying lower premiums. Since artificial intelligence is capable of rigorous scrutiny of gathered data, it can predict the amount of risk involved, protect companies from frauds and give justified insurance amounts to customers. So this makes a win-win situation for both the insurance companies and its clients.
  • Better marketing and personalization: The old methods of marketing are on the edge of extinction since digital disruption has already quaked the grounds of the insurance field. Customers are expecting personalized services that match their needs, preferences, and lifestyles. Creating personalized experiences with the help of advanced analytics and machine learning is now an option that companies can use to improve their marketing. They can chalk details from data gathered about individual taste and preferences, behaviors, lifestyle details, and hobbies to create personalized products such as policies, loyalty programs, and recommendations. Marketing is another weapon for insurance companies that are in a look out to enhance their reach and obtain higher customer acquisition. Being a part of the competitive market, insurance companies need to capitalize on a vital marketing strategy that goes beyond the traditional approach.

Challenges in implementing Machine learning in the Insurance sector

According to a survey conducted by Accenture Company, 74% of the customers of the insurance sector would like to interact with modern day technology and would appreciate the results provided by such computer software. Companies who have been swift to adopt the automation of some aspects of their claims process can experience a noteworthy fall in processing time and cost, and a good increase in service quality, thus benefiting it more as compared to its competitors who are yet to adopt machine learning into their systems. 

Some of the challenges insurance companies confront when adopting machine learning are:

Lack of skilled data requirement: AI-powered intellectual systems must be instructed in a domain, e.g., claims or billing for an insurer. This requires a separate training system, which insurers find hard to provide for training the artificial intelligence model. Models need to be trained with enormous volumes of documents/transactions to cover all possible scenarios.

Data security: The enormous amount of data used for machine learning algorithms has fostered an additional security risk for insurance companies. With such an upsurge in collected data and connectivity among applications, there is a risk of data leaks and security breaches. Any security breach could lead to personal information of customers and the company falling into the wrong hands. This creates fear in the minds of insurance companies.

Resistance to change: Rapid advances in technologies over the last 10 years have led to disruptive changes in the insurance industry. There is an inherent fear amongst the industry about the outcomes of Artificial Intelligence models. The human race still fears to be reliant on technology and still doubts on the results produced by machines.

Regulatory barriers: Insurance is a heavily regulated industry in most countries, which limits many opportunities to use machine learning; especially it’s black box approach. 

Concluding words

Recently more companies are continuously in a look out for new methods and approaches of implementation of Machine Learning algorithms into their business processes. Insurance industry with its thousands of claims, customer queries and huge amounts of data is not an exception. The uses of artificial intelligence and machine learning algorithms have great potential in the insurance domain. Insurance companies and organizations all across the globe are leveraging and reaping benefits of these technologies as it can provide better customer service and gain a competitive edge.

Since inception, insurance companies have always worked with data, thus, it only makes more sense that they enjoy the journey of this digital transformation wave and implement machine-learning solutions that give them an additional and in-depth look into this data to discover new insights. The time for machine learning is on the rise, so we’re bound to see these applications mature and new ones appear on the insurance scenario to accelerate the sector’s digital transformation.

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