Tag Archives: underwriting

3 Key Enablers for Better Underwriting

Within the commercial property and casualty sector, there has been a lot of attention on the underwriting process, which is inherently manual and time-consuming. Carriers and insurtechs are primarily trying to accomplish two things: make it easier for the end user to buy insurance and improve the accuracy and efficiency of underwriting.

These two goals complement each other. By tapping into third-party data sources, carriers can get more accurate information. Carriers also increase ease of doing business, for both underwriters and customers.

To date, carriers have leveraged in-house tools and partnered with technology providers to gain information for underwriting. Property sensors, public records, telematics and drones are just a few of the sources underwriters are using to access risk. But to stay competitive against other carriers and to continue to cut down on the number of questions that will need to be answered by the applicant, carriers need to continue to innovate.

See also: Winning in Small Commercial Lines  

An Accenture report centered on the rise of insurtech found that 86% of insurers believe innovation must happen faster. So, what can carriers do to boost their underwriting game?

  1. Start with areas that are aligned with strategic objectives: Insurance carriers need to think more strategically about innovation. Areas of investment in innovation need to tie directly to where you want to grow or improve from a business perspective. It is easier to gain internal buy-in and traction on topics that everybody already agrees need the most attention.
  2. Understand operational readiness for prioritized uses: Before any investment takes place, business leaders must understand what it would take to put an idea into production. In many cases, a clear path to test a new capability is identified–e.g.. limited proof of concept (POC)–but the actual requirements, timeline and costs (rough order of magnitude) in a production scenario are not analyzed or understood. Early focus on the desired end state can set the proper vision and avoid stalls and misdirection later on. 
  3. Make it easy to collaborate. The thing that insurance carriers that have strong innovation programs have in common is that they are not afraid to collaborate with insurtech partners. Today’s insurtechs are building niche businesses that can be tapped to enhance specific parts of the insurance value chain, often far faster than a carrier’s internal capabilities could allow on their own. Leading-edge carriers are collaborative and oriented toward feedback. The approach by insurtechs creates a healthy ecosystem and promotes effective product development.  

Once a carrier implements new technologies in the underwriting process, the organization should ensure that it is measuring the improvements in accuracy and efficiency. One way is to look at the organization’s overall profitability. Another is to look at team productivity–if measures have been put in place to boost efficiency and accuracy, the same underwriting team should be able to quote more policies than before, and more quotes should be “bindable.”

Agents will find it easier to do business with carriers that operate more efficiently and effectively.

While the insurance industry has fallen into the late adopter category, carriers and insurtechs have the opportunity to take advantage of a market that is ripe for change. 

See also: The New IoT Wave: Small Commercial  

DataCubes focuses on powering commercial underwriting using decision science. The organization was built on the idea that there is a more productive way to underwrite.

How Machine Learning Transforms Insurance

We like our insurance carriers to be risk-averse. So it should come as no surprise they are often last to innovate. Insurers need to feel very comfortable with their risk predictions before making a change. Well, machine learning is writing a new chapter in the old insurance book. There are three key reasons why this is happening now:

  1. New insurtech players are grabbing market share and setting new standards. Traditional carriers have no choice but to follow suit.
  2. Customers are expecting Netflix/Spotify-like personalization, and have no problem changing providers — this trend is expected to grow as we see more millennials maturing out of their parents’ policies.
  3. Getting started with machine learning is becoming VERY easy because of open source frameworks, accelerated hardware, pre-trained models available via APIs, validated algorithms and an explosion of online training.

As with any innovation, it only takes two things for widespread adoption:

  1. Potential to improve business goals.
  2. Ease of establishing pilots.

With time, we see that successful pilots become products. Teams are hired/trained, resources are allocated, business goals gain more “appetite” and models are tweaked.

For P&C carriers. we see the opportunity for improving business goals and easily pilot machine learning in the following areas:

Risk Modeling

Given the complex and behavioral nature of risk factors, machine learning is ideal for predicting risk. The challenge lies in regulatory oversight and the fact that most historic data is still unstructured. This is why we often see machine learning applied to new products such as those using data from IoT sensors in cars (telematics) and home (connected home). But innovative carriers are not limited. They use pre-trained machine learning models to structure their piles of unstructured data: APIs to transcribe coupled with natural language understanding (NLU) extract features from recorded call center calls, handwriting and NLP/NLU tools for written records, leading toward identifying new risk factors using unsupervised learning models.

See also: 4 Ways Machine Learning Can Help  


Carriers can get an actuarial lift even without designing and filing new actuarial models. Using machine learning to better predict risk factors in existing (filed) models. For example, a carrier may have already filed a mileage-based rate-plan for auto insurance but rely on user-reported or less accurate estimates to determine mileage. Machine learning can help predict mileage driven, in a less biased and more accurate way. Similarly, APIs to pre-trained chatbots using lifelike speech and translators can turn website underwriting forms into more engaging and personalized chats that have a good chance to reduce soft fraud.

Claims Handling

Claims handling is a time-intensive task often involving manual labor by claims adjusters onsite. Innovative carriers already have policy holders take pictures and videos of their damaged assets (home, car…) and compare with baseline or similar assets. Carriers could easily leverage existing APIs for image processing, coupled with bot APIs to build a high-precision model, even at the expense of low recall. Compared with having 100% of the book handled manually, a triage bot that automates even a mere 20% of the claims (with high precision) can enable carriers to start with a low-risk service that’s on par with new insurtech players and improve ratios over time. Such a tool can even be leveraged by adjusters, reducing their time and cost.


While personalized pricing may be regulator-challenged, personalizing the insurance product offering is expected in this Netflix/Spotify age. As basic coverage is commoditizing, carriers differentiate their products based on riders and value-added services, not to mention full product offerings based on life events. Carriers can (with consent, of course) leverage social media data to tailor and personalize the offering. Similarly, marketing departments can use readily available recommendation algorithms to match and promote content about the benefits of certain riders/value-adds to relevant customers at the relevant time.


The world of insurance distribution is growing in complexity. Carriers are struggling with the growing power of intermediaries, and agents are having hard time optimizing their efforts due to lack of predictability of loss commissions. Point-of-sale and affiliation programs are growing, and with them the need for new distribution incentive models. Both traditional and new distribution channels could benefit from machine learning. Brokers, point-of-sale partners and carriers can leverage readily available machine learning models and algorithms designed for retail, to forecast channel premiums. Carriers can grow direct channels without growing headcount, using pre-trained chatbots, NLU and lifelike speech APIs.

See also: Machine Learning: A New Force  

Machine learning is part of our everyday lives. Innovative insurers are now jumping on the ML wagon with an ever-growing ease; which carriers will be left behind?

Insurers Grappling With New Risks

Warren Buffett’s caution about underwriting cyber-insurance put the spotlight on one of the big challenges facing carriers today – how to address a slew of new insurance risks.

The Oracle of Omaha told shareholders at the Berkshire Hathaway annual meeting that he didn’t want the group’s insurance business to pioneer cyber-cover because the risks were largely unknown and potentially too big. Berkshire Hathaway might write some cyber-policies to stay competitive, Buffett added, but it would not be among the top three providers in this market.

Underwriting complex new risks such as cyber-insurance, as well as meeting the rising demand for cover for other risk-heavy occurrences such as natural catastrophes and corporate fraud, promises substantial revenue for carriers. Global premium revenues for cyber-insurance, for example, could hit $7.5 billion by 2020, according to researcher Statista. Cover related to digital products and services could also yield healthy additional income. The new revenue streams are welcome news for many insurers that have watched income from traditional products plateau in the past few years.

However, as Buffett points out, venturing into uncharted territory can be hazardous — especially when we don’t know the scope of the hazards. Catastrophe cover, for example, which must now contend with uncertainty related to climate change, cost U.S. insurers dearly last year. The effects of three major hurricanes, Harvey, Irma and Maria, as well as the extensive wildfires in California, all contributed to a spike in underwriting losses. The net underwriting deficit among U.S. property and casualty insurers leaped from $4.7 billion in 2016 to $23.2 billion the following year, according to a report compiled by research firm ISO and the Property Casualty Insurers Association.

Insurers are not only being forced to make calls on new types of risk. They must also handle the growing complexity of the underwriting required for some of their established offerings. The spread of corporate ecosystems and supply chains across many varied countries, for example, has heightened the complexity of commercial risk assessment. So, too, has the rise in trade and business regulations imposed by governments around the world.

What’s more, insurers must also accommodate a flood of new data streams. While these additional sources of data provide valuable insight into commercial risks and consumer behavior, they also compound the complexity of insurers’ underwriting systems and processes.

To meet the rising challenge of new and more complex underwriting requirements, insurers need to get a lot smarter. Improving workers’ skills and hiring more talent won’t be enough. Insurers need to deploy intelligent technology. Only by using artificial intelligence (AI) will underwriters be able to manage the new, complex risks that are confronting them.

Our research shows that more than 75% of insurers plan to use AI to automate tasks in the next three years. Many of these applications are intended to improve efficiency and productivity. The big gains in AI, however, are likely to be achieved by using this technology to improve decision-making.

In my next blog post, I’ll discuss how advances in AI can help underwriters make smarter, quicker decisions. Until then, have a look at these links. I think you’ll find them useful.

Microinsurance and Insurtech

Until insurtech, insurance companies were defining microinsurance policies as social responsibility projects. With the magic touch of technology, the picture is changing rapidly!

Microinsurance is a type of micro financial activity, which protects low-income people and communities with low premiums and limited coverages against risks. The main objective is providing financial protection for all low-income members with pooling risks and financial resources. The target customer group is quite big, as well. More than 2 billion people are potential customers of micro insurance worldwide.

Microinsurance enhances financial security and peace of mind, supports social security systems in poor or developing countries and provides a high-level risk management system. For long-term investors, microinsurance stabilizes and develops financial markets in developing and poor countries and provides considerable liquidity for critical times.

See also: Microinsurance: A Huge Opportunity  

Four features are crucial for penetration by microinsurance:

  • Premiums should be affordable for low-income households
  • Products should be very basic and easy to understand and cover limited risks
  • Underwriting, claims and collection processes should be operated with high effectiveness
  • Products should be distributed effectively and with minimum distribution cost

With insurtech, the picture is changing rapidly! Insurtech is converting microinsurance into a very profitable area.

The first rule of insurance, the law of large numbers, is now valid for microinsurance business. The number of insureds is widening, and this makes claims more stable and predictable for insurance companies.

The first impact of insurtech in micro insurance is on UW processes. Because of the low premiums, operational efficiency is the key of success in projects. Insurtech allows insurers to have their own automated UW decision-making processes for fast and low-cost policy production. The key to success, management of operational risk, is reduced significantly. The products are simple, do not require any financial literacy and are very user-friendly.

Target customers purchase policies without location restrictions via digital distribution channels. Premium collections, claims notifications and all compensations activities are performed with digital tools that were developed and perfected by insurtech.

See also: A ‘Nudge’ Toward Microinsurance  

For now, microinsurance projects mainly focus on personal accident, health and agricultural activities, but new products are being developed promptly. With all its components, like artificial intelligence, machine learning, chatbots and the Internet of Things, insurtech is becoming the new leverage for microinsurance — not just diversifying and absorbing risks for individuals, but also providing very strong preconditions for other productive activities for policy owners.