Getting to grips with China's digital-only insurer...

The Rise

Well, in simple terms, Zhong An graduated from being just a thought way back in 2010 to a reality in 2013, when 3 powerhouses decided to shake hands – Ping An Insurance, Tencent Holdings and Ant Financial, Affiliate of Alibaba group.  Since then, this InsurTech start-up has grown fast and created lot of attention among the insurance community.

Current Highlights

  • Annual GWP: $1.1 billion
  • Policies sold: 8.2 billion
  • Policyholders: 543m
  • Distribution partnerships: Alibaba, Taobao, Ctrip, Wechat

Zhong An has definitely seen its early success based on 3 factors:

1.      Shareholders being the largest consumer companies

2.      Product Innovation

3.      Technology

 

How can we describe Zhong An?

It's difficult to put Zhong An into one bucket. Its a Next Y Insurance Company, a Digital Insurer, an Online Insurer, InsurTech Startup – a start-up which has grown from nothing to $13B valuation and ranks among the Top 15 start-ups in the world from a valuation perspective and is probably one of the quickest to reach $1B valuation.

Product Innovation

You will be surprised to know that Zhong An's portfolio boasts of 200+ products - for a company which is less than 4 years into its existence! But, what differentiates it the most is its “product innovation group”. The products which are designed and developed are relatively niche, easy to understand from customer perspective and are relatively different. Why is it different? Well, the products are structured by using artificial intelligence, consumer data and analytics for product pricing, underwriting rules and technology use for risk management.

“Machine learning can optimise the quality of customer service, so the development of AI in the insurance industry will certainly be a big trend,” said Wayne Xu, the company’s Chief Operating Officer.

It’s called the gig in the product innovation space. Let’s take few examples –

  • Insuring against cracked mobile screens using image recognition technology
  • Insuring for e-commerce products using big-data technology
  • Insuring against shipping return products using machine-learning technology
  • Insuring against flight delays using big data and real-time weather forecasts

Products are classified into following categories:

  • Lifestyle 48% of GWP
  • Travel 32% of GWP
  •  Finance 9% of GWP
  • Health 7% of GWP
  • Motor / Others 4% of GWP

The use of big data and machine learning helped them to assess the risk of short-term-maturity products with days, weeks and months of term. Zhong An is packaging the product using a “consumption risk” model rather than the “traditional risk” model, which helps them price the product dynamically or in some cases on a “real-time” basis. Example, when they are insuring against the flight delays, it maps the weather forecast 2 hours before the flight departure and can then dynamically price the product with lower or higher premiums.

Business Model

Simple – when you have the 3 biggest and most successful names in the market as your shareholders, you don’t have to “think about business models”. Zhong An brings best-of-breed of business models from all 3 shareholders and partners with all 3 of them.

Ping An – One of the largest Insurance companies in China, and brings loads of distribution and product-building experience

Ant Financial – All about Alibaba, Consumer network and e-commerce. It can be easily applied in Insurance

Tencent Holdings – Consumer network and ability to bring the payment eco-system embedded

But, there are drawbacks also. Bringing the powerhouses means costs are inflated from partners to payment networks, as well as cost ratios not coming down in the near to short term, which can hit the profitability of its growth trajectory. Well, it doesn’t need capital infusion in the mid term since the operating income can suffice for growth. 

Financials

Relatively strong Financials - their FY2016 GWP was $500M during the time when Zhong An had filed for an IPO. It has grown at the rate of 95% CAGR for last 3 years, bringing them closer to the valuation of $15B by end of FY 2017. Though they are barely scratching the surface of profitability, topline has been impressive. Compared to other insurers, they are relatively on the higher side when it comes to loss ratio (~ 40%) and expense ratio (~ 62%).

The distribution cost is also a little on the higher side since it distributes mostly through its partners like Alibaba and Tencent, and average distribution cost moves around 40%.

In the first 3 quarters, it saw a jump of 130% in premium income to 6.4 billion yuan ($960M) and posted loss of 400 million yuan.

Understanding customers

Products are not created based on traditional models, they are created using varied data sets of consumers. It receives vast amount of data and insights from its partner ecosystems i.e. customer demographics, buying patterns, social listening, browsing history, personal data, transactional data, credit report, payment history, etc. Now – that can run into zillions of datapoints, all of which needs to run through the big data cloud so that it can be processed and relevant insights generated, to create, price and distribute products. It is important that the products are positioned “after” knowing the customer – this way distribution and positioning becomes relevant.

60% of customers are aged between 25-30 years, who are truly internet-connected millennials. Top 70% GWP driven from top 40% customers who are aged less than 40 years.

Moving Forward

Zhong An will definitely shine in terms of selling consumer-centric, light-weight products which will attract consumer attention. There is no reason that the company can’t grow at 80%+ CAGR for next 3 years, but they would have to keep combined ratio in mind. Since it has the backing from largest consumer internet companies, it can enrich the huge consumer base to cross-sell and distribute the products. Technology will be the key pillar to their success, which they will continue to leverage based their R&D capabilities around big data, machine learning and artificial intelligence.

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