Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

4 AI Applications That Are Transforming the Insurance Industry Now

The Asia Pacific (APAC) region is experiencing an insurance boom, as well as significant digital disruption in this sector, according to a recent report by Bain & Company. As competition grows and new players enter the market, insurance companies are implementing artificial intelligence (AI) powered tools to stay ahead. 

Asia Pacific currently has the fastest growing insurance market in the world. This boom is linked to two main factors: an expanding middle class and the removal of barriers to entry in markets such as China and India.

At the same time, digital disruption has created a situation where customers expect to access insurance services digitally, can easily compare policies online, and will receive more personalized services and products.

To get ahead, insurers are using advanced analytics, machine learning and other AI-driven tools to compete with agile new players and elevate the customer experience. According to a study by PwC, more than 80 percent of insurance CEOs said AI was already a part of their business model or would be within the next three years.

Here are four major areas where insurers can implement AI to improve customer engagement, combat fraud and streamline business processes.

 

1. Fraud Detection & Credit Analysis

The General Insurance Association of Singapore estimates that around one in five claims the industry receives are either false or inflated, costing the industry around S$140 million (US$101 million) a year.

To combat fraud, insurers are using AI-driven predictive analytics software to process thousands of claims each month. By analyzing the claims in milliseconds based on set rules and indicators, AI is able to identify which may not be legitimate, reducing the number of fraudulent claims slipping through. These indicators include things such as frequency of claims, past behavior and credit score.

By leveraging machine learning, Chinese Insurer Ping An saved itself US$302 million from fraudulent claims in one year. It also achieved a 57 percent increase in accuracy in fraud detection from the previous year.

 

2. Customer Profiling & Segmentation

By automating and applying cognitive learning to their data collection processes, forward-thinking insurance companies, including AIA Singapore, are also advancing their customer profiling capabilities.

Equipped with the power to unify and derive insights from their internal and external customer data, insurers are able to build a more comprehensive picture of their customers, such as their insurance needs, interests and life stages, for more effective targeting. Insurers can segment their audience based on these attributes, and use deep learning to predict the conversion rate of these segments. With such insight, insurers can then decide the relevant product recommendations for each customer segment.   

Insurance companies are also enhancing customer profiling with AI-enabled voice and facial recognition, which helps create biological customer profiles for fast and accurate verification, as well as the tracking of behaviors and attributes.

 

3. Product & Policy Design

Another area insurance companies are using AI is to inform their product and policy design,

By streamlining and speeding up the collection and analysis of massive data from owned channels, third-party sources and agents, insurers can use machine learning to discover customer trends and interests in real time. These insights are then being used to develop and improve product and policy design.

Chinese online-only insurance company, ZhongAn, is a company that continually releases innovative products and policies, many of which are developed with the help of advanced AI techniques such as machine learning and image recognition. For example, they came up with niche policies to insure against cracked mobile screens and shipping return products.

 

4. Underwriting & Claims Assessment

The process of underwriting is often viewed as an art based on personal judgment, but AI technologies have also worked their way into this area of insurance, making the process increasingly scientific.

Insurers are now using advanced analytics and machine learning, as well as additional sources such as satellites and the Internet of Things devices, to help get a more holistic view of risk, as well as to determine which submissions to review in the first place.

Japanese insurance firm Fukuoka Mutual, for instance, has been using a cognitive machine learning based system to scan medical records and data on surgeries and hospital stays to calculate payout. Meanwhile, Indian company ICICI Lombard has created an AI-based cashless claims settlement process, which can be completed in just a minute.

From fraud detection to underwriting, AI technologies are reimagining every facet of APAC’s booming insurance industry. By reducing the risks and streamlining processes, it can help companies drive efficiencies and deliver more personalized products and services – the key to future success.

 

* Do you also want to know more about how AI can help financial services companies analyze data, make precise predictions and offer insights that enable them to create a more effective marketing strategy and campaigns? Download our latest white paper ‘Predict Customer Behavior in Financial Services: How Artificial Intelligence and Data Science Enable Better Marketing and Higher ROI’ now. 

WE ARE HERE TO HELP

Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.

YOU MIGHT ALSO LIKE

How to Minimize Booking Abandonment in Online Travel

Cart abandonment remains a major issue in online travel business. Proactive customer engagement platforms powered by artificial intelligence (AI), however, are making it possible to catch and convert cart abandoners, effectively and efficiently, through brands’ owned media channels. According to a recent research, 92 percent of all travel bookings in Asia Pacific were abandoned at checkout, with online travel agencies suffering from the highest rate at 95.5 percent, followed by hotels (90.4 percent), airlines (91.6 percent) and vehicle hire companies (88.9 percent). There are many reasons why shoppers abandon cart. Some are just browsing, still comparing prices or simply distracted. Others become frustrated with slow-to-load pages, or are put off by high credit card fees. While some cases of abandonment can be avoided by improving your website or app UX, many can be overcome by remarketing via paid media channels, such as Facebook and Google Ads. While such remarketing can be effective in winning back lost carts and boosting sales, it is also costly, and relies on strong retargeting tech and infrastructure that many companies lack internally.   AI, Owned Media and Cart Abandonment Can travel brands tap into the potential of owned media and leverage data to tackle cart

Boost High-Quality App User Acquisition With Artificial Intelligence

In the early days of app marketing, brands defined success as the number of times their apps were downloaded and installed. With a deeper understanding of customer behavior in the app world has come the realization that the continued engagement and retention of customers is a much more valuable metric. With one in five users abandoning apps after just one use, only the customers who continue to engage with the app will fulfil valuable in-app events such as subscription and purchase, which ensures higher return on investment and revenue, and lower churn rate. Brands have thus turned their attention to high-quality user acquisition (UA), throwing their money behind growing the customer lifetime value (CLV/LTV). But it all starts with the app install. The key to optimizing in-app events is to drive quality installs by identifying users who will engage with your app prior to the initial download – at the right volumes and the right price. UA requires that marketers analyze data points to identify customer behavior trends and arrive at insights around which of them will demonstrate stickiness. With the sheer volume of data that marketers are dealing with today, manual analysis is not feasible, and they are turning

A Guide on How to Maximize Value From Third-Party Data

Data privacy concerns are on the rise in a post-GDPR (General Data Protection Regulation) era. Four in five marketers worldwide were worried about the potential risk of violating the EU’s GDPR due to their tech vendors. However, data is critical in today’s engagement-focused marketing battlefield. As the consumer journey becomes increasingly complex, it is getting more and more difficult to achieve effective campaign results by relying on first-party data alone. That is why third-party data remains a vital tool for marketers to better target their customers with personalized offers and find lookalikes. In fact, when leveraged in the right way, third-party data can significantly impact your campaign success and put you one step ahead. Here are four ways how you can maximize value from third-party data.   1. Discover Customers’ External Interests to Expand Share of Wallet You already know your customers’ interactions with your brand based on first-party data collected from your websites, apps and other owned channels. It provides you with basic demographic information, such as gender, age and geo-location, and insights into individual purchasing patterns and interests – information that can help guide your campaign targeting decisions. But how do you know what they are doing beyond