The impact of COVID-19 has disrupted the trade and supply chain across the world and brought the world economy into a tizzy. Small and Medium Enterprises are especially in a difficult situation. They are facing huge business loss, cash crunch, and some even bankruptcy. Insurance will have a crucial role to play within SMEs in the post-pandemic world.
To understand the importance of Insurance for SMEs and how various industries should pivot their mitigation strategies towards long term sustainability, we have Mr. Steve Tunstall, CEO and Co-founder of Inzsure.com, Singapore.
The Inzsure platform is designed to transform the global commercial insurance industry by providing SME customers, initially in Singapore, Malaysia, and The Philippines.
Steve has over thirty years of experience in owning, running, and future-proofing companies. He has been CEO, Managing Director, or equivalent in seven companies in four countries managing teams of up to 500 employees and based in Asia for over 20 years. Steve is also a contributing author to The InsurtechBook and author of “RISK and the Asian CEO” published on Amazon Kindle in 2016. He has deep domain knowledge in Insurtech, Fintech, commercial insurance, compliance, risk, and crisis management. He has been featured in the Top Global Influencer lists of Rising, InsurtechNews, Richtopia, and Onalytica in the areas of Insurtech, Fintech, and Blockchain.
Connect with Mr. Steve Tunstall – LinkedIn
Here’s the excerpt from the interview:
The Impact of COVID-19 on SMEs in Asia
What’s the magnitude of the impact of COVID-19 in small and medium scale businesses — both globally and Asia specifically?
Steve: The entire world is facing the consequences of the current pandemic which is affecting everybody with no exceptions.
Some sectors like the hospitality and travel industry have been hit the most. Along with these, service providers and manufacturers have also been affected. Oil industry unexpectedly also saw an all-time low in this crisis. The Global Supply Chain was an obvious sector to get disrupted. The supply chains have become shorter and duplicate. The whole concept of Just-in-time has gone for a toss. SMEs in those affected industries need to rethink their business and close down if necessary.
The time is tricky and if there’s a short lock-down period, then it will have grave consequences to humankind. Massive spikes in infections will lead to a huge overload on the healthcare system. It’ll create a painful situation for medical professionals where they’ll have to make difficult life-death decisions based on the facilities available.
However, on the other hand, longer periods of lockdowns will suffocate GDP and damage businesses. Most SMEs can survive if they are in the hot sectors. Once the lock-downs extend more than 2-3 months, it’ll be traumatic to the global GDP. We have already lost 25% of the global GDP. A lot of businesses might go bankrupt and the government cannot bail out everyone.
These are gloomy times, but business managers and business owners need to think about how to pivot their business and find some sort of viable solution for this.
The Rise of Digital Insurance Models
Insurers are taking the distribution process online. How are the Insurers adjusting to this new model and how has the customer response been in Singapore?
Steve: Transition towards online sales is a moving target. It has been happening for quite some time now and will accelerate all the more. In the UK around 70-90% of insurance policies are sold online before COVID-19 outbreak. If we split Life and Non-life and further split non-life into Personal and Commercial Insurance, there are three broad buckets-
Life Insurance- This line of insurance is not bought but rather sold. It’s a process to educate people and has a long sales gestation period. It involves a lot of interaction between insurance sales agents and an individual. Even big Life insurance companies are dependent on agents for sales.
Personal Insurance- This line includes health, travel, motor, property insurance which is mostly sold online. Wealthier economics tend to have more online stuff than developing ones. It’s a bit patchy. But in personal insurance lines, many policies can be bought online in many countries.
Commercial Insurance- This line is the slowest of the three to adopt technology particularly the intermediaries. SMEs should be a good target for online but we have seen very little traction in Asia. Large companies are much slower in the adoption of digital technology and rely on face-to-face interaction with brokers.
Covid-19 has become an accelerator for online especially for Life and Personal Insurances. Broadly speaking, 80% of the personal and life insurance are standardized. Only 20% need underwriting input. In Commercial lines, 20% is commoditized and 80% is bespoke. It is still a long journey. We have already seen insurance being sold online in the US and Europe and seem to go ahead in Asia.
Many Insurers have been resisting online and commoditization for years. But giving customers choice, trust and transparency is the way to improve overall penetration in Insurance.
The Importance of Insurance for SMEs
Since the pandemic started, fewer businesses (especially SMEs) are seeking insurance because of the loss of cash flow. How do you think your platform could help SMEs in this current situation?
Steve: It’s a common human tendency that you don’t need an umbrella during a light shower so you don’t buy one. But when the rain is hammering down, you go buy one only to find out that shops have run out of them. There are gaps in the knowledge about insurance. Not only within SMEs but also many businesses.
In Asia, there’s less insurance required by the law and hence insurance does not tend to sell much. It’s the discerning and more naive one who gets sold insurance. The issue is that people do not know why insurance is a good thing and should be made a priority. Not all types of insurance perhaps, but businesses need to look at appropriate insurance which is tied to risks holding on their balance sheet. For example, fire is a big risk. Maybe not for a co-working space where data is on the cloud but for traditional businesses, you need to have insurance.
Insurance in the New Normal
What are some new business models that Insurance Carriers are considering to meet the expectations of life in ‘The New Normal’? More specifically, where is the new business going to come from, for Insurance, over the next two years?
Steve: Around 30 years ago, businesses had their own properties for which they would need a cover, their machinery, they would operate out of a premise. But these days, most businesses do not own property, they are working in rented premises and have data on the cloud.
There’s been a shift away from physical assets towards liabilities like loss of data, hacking, legal and regulatory obligations. All these different liability types are growing exponentially which creates a lower demand for property insurance.
The traditional property and casualty insurance relies on historical data for calculating premiums. But for these emerging liabilities, it is difficult for insurers to get their head around its implications. Taking Cyber insurance policy for example. If businesses are not able to link the loss incurred due to cyber hacking, then insurers won’t payout. If an amazon web service goes down for the entire building, other businesses also have faced losses that accumulate losses to other companies as well. This accumulation of loss is worrying the CEOs now. This could be a huge opportunity for insurers to address these emerging liabilities in a meaningful way.
Speeding-up Claims during COVID-9 crisis
The pandemic has put a lot of pressure on health claims due to the increase in the volume of claims. What do Insurers need to do to speed-up their claims processes?
Steve: Out of all the processes in the insurance, claims appear to be the most painful and complained about. Surely, there will be an increase in claims related to COVID-19. In the US a typical COVID claim is looking somewhere between $20,000 to $100,000 but in Asia, it is much more bottom of that range.
But on the other hand, another effect of COVID-19 is that since so many medical facilities around the world have seen a massive decline in regular doctor visits and elective surgeries. Therefore, there has been a reduction in the claims for other health ailments. We will see some of it coming in the upcoming months, probably in Q3 and Q4. For now, it has brought a balance in the number of claims.
Technology trends post COVID-19
How can technology help in sustaining the Insurance business and what are upcoming technology trends? Also, what industry will expect from technology service providers?
Steve: I believe that all the technology that is needed for insurers to work efficiently and perfectly online is already available. What is most needed is a huge change in mindset amongst the insurers. As an industry, people who build the products should not be separated from people who sell the products.
On the customer side, insurance is not a product where you get instant gratification. Knowing the importance of insurance for SMEs, appropriate education about risk management can help. The change in mindset will impede the implementation of technology.
Digitizing Insurance Processes
COVID-19 will propel insurers to increase the digitization of their operations and interactions with clients. We may also see insurers scaling back on their physical office networks and moving more people to remote working. More focus will fall on the automation of processes for greater cost efficiencies and resilience. What, according to you, are the crucial insurance processes where automation will disrupt first?
Steve: It depends upon where you are in the supply chain. The more insurers can automate their internal processes, the better. Underwriting is an area where AI plays a crucial role in making this process easy and cost-efficient.
For insurers, when it comes to back-office functionality, cost-cutting will be a high priority due to the COVID-19 crisis. Technology can bring more efficiency to the intermediary processes making adoption of insurance for SMEs easier.
AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out FlowMagic— an AI-driven platform for Insurer workflows and Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at email@example.com.
Interviews with InsurTech thought leaders:
Australian FinTech company profile #122 – Unhedged
1. Company Name: Unhedged
2. Website: www.unhedged.com.au
3. Key Staff & Titles: Peter Bakker – Founder & CEO, Mike Cohen – Co-Founder & COO, Glen VanBavinckhove – CTO, Jeremy Beasley – Growth, Jeremy Machet – Growth, and 6 others who are building like crazy
4. Location(s): Melbourne and Sydney
5. In one sentence, what does your fintech do?: Unhedged uses AI to deliver algorithmic returns to the everyday investor
6. How / why did you start your fintech company?: Being an Algotrader and working with rich people I got annoyed that these advanced tools were not available to my friends. When I looked up the returns of robo-investors I got really annoyed and thought: there must be a better way
7. What is the best thing your company has achieved or learnt along the way (this can include awards, capital raising etc)?: Raised 500K in 3 days which was faster then I ever raised before.
8. What’s some advice you’d give to an aspiring start-up?: Watch your cashflow: companies die of lack of cash, not lack of ideas
9. What’s next for your company? And are you looking to expand overseas or stay focussed on Australia?: Lauchinh the fund in April/May, a crowd fund raise in June and launching the retail product in July….
10. What other fintechs or companies do you admire?: Finserv (most stable earnings and growth), Blackrock: amazing money machine. Ellevest: a narrow target markets that works. CacheInvest: fundmanager as a service
11. What’s the most interesting or funniest moment that’s happened in your company’s lifetime?:
An investor transferring 100K without any documentation nor live fund (we returned the cash). We are still wondering how he knew where to transfer to.
Flawed data is putting people with disabilities at risk
Data isn’t abstract — it has a direct impact on people’s lives.
In 2019, an AI-powered delivery robot momentarily blocked a wheelchair user from safely accessing the curb when crossing a busy road. Speaking about the incident, the person noted, “It’s important that the development of technologies [doesn’t put] disabled people on the line as collateral.”
Alongside other minority groups, people with disabilities have long been harmed by flawed data and data tools. Disabilities are diverse, nuanced and dynamic; they don’t fit within the formulaic structure of AI, which is programmed to find patterns and form groups. Because AI treats any outlier data as “noise” and disregards it, too often people with disabilities are excluded from its conclusions.
Disabilities are diverse, nuanced and dynamic; they don’t fit within the formulaic structure of AI, which is programmed to find patterns and form groups.
Take for example the case of Elaine Herzberg, who was struck and killed by a self-driving Uber SUV in 2018. At the time of the collision, Herzberg was pushing a bicycle, which meant Uber’s system struggled to categorize her and flitted between labeling her as a “vehicle,” “bicycle,” and “other.” The tragedy raised many questions for people with disabilities; would a person in a wheelchair or a scooter be at risk of the same fatal misclassification?
We need a new way of collecting and processing data. “Data” ranges from personal information, user feedback, resumes, multimedia, user metrics and much more, and it’s constantly being used to optimize our software. However, it’s not done so with the understanding of the spectrum of nefarious ways that it can and is used in the wrong hands, or when principles are not applied to each touchpoint of building.
Our products are long overdue for a new, fairer data framework to ensure that data is managed with people with disabilities in mind. If it isn’t, people with disabilities will face more friction, and dangers, in a day-to-day life that is increasingly dependent on digital tools.
Misinformed data hampers the building of good tools
Products that lack accessibility might not stop people with disabilities from leaving their homes, but they can stop them from accessing pivot points of life like quality healthcare, education and on-demand deliveries.
Our tools are a product of their environment. They reflect their creators’ worldview and subjective lens. For too long, the same groups of people have been overseeing faulty data systems. It’s a closed loop, where underlying biases are perpetuated and groups that were already invisible remain unseen. But as data progresses, that loop becomes a snowball. We’re dealing with machine-learning models — if they’re taught long enough that “not being X” (read: white, able-bodied, cisgendered) means not being “normal,” they will evolve by building on that foundation.
Data is interlinked in ways that are invisible to us. It’s not enough to say that your algorithm won’t exclude people with registered disabilities. Biases are present in other sets of data. For example, in the United States it’s illegal to refuse someone a mortgage loan because they’re Black. But by basing the process heavily on credit scores — which have inherent biases detrimental to people of color — banks indirectly exclude that segment of society.
For people with disabilities, indirectly biased data could potentially be frequency of physical activity or number of hours commuted per week. Here’s a concrete example of how indirect bias translates to software: If a hiring algorithm studies candidates’ facial movements during a video interview, a person with a cognitive disability or mobility impairment will experience different barriers than a fully able-bodied applicant.
The problem also stems from people with disabilities not being viewed as part of businesses’ target market. When companies are in the early stage of brainstorming their ideal users, people’s disabilities often don’t figure, especially when they’re less noticeable — like mental health illness. That means the initial user data used to iterate products or services doesn’t come from these individuals. In fact, 56% of organizations still don’t routinely test their digital products among people with disabilities.
If tech companies proactively included individuals with disabilities on their teams, it’s far more likely that their target market would be more representative. In addition, all tech workers need to be aware of and factor in the visible and invisible exclusions in their data. It’s no simple task, and we need to collaborate on this. Ideally, we’ll have more frequent conversations, forums and knowledge-sharing on how to eliminate indirect bias from the data we use daily.
We need an ethical stress test for data
We test our products all the time — on usability, engagement and even logo preferences. We know which colors perform better to convert paying customers, and the words that resonate most with people, so why aren’t we setting a bar for data ethics?
Ultimately, the responsibility of creating ethical tech does not just lie at the top. Those laying the brickwork for a product day after day are also liable. It was the Volkswagen engineer (not the company CEO) who was sent to jail for developing a device that enabled cars to evade U.S. pollution rules.
Engineers, designers, product managers; we all have to acknowledge the data in front of us and think about why we collect it and how we collect it. That means dissecting the data we’re requesting and analyzing what our motivations are. Does it always make sense to ask about someone’s disabilities, sex or race? How does having this information benefit the end user?
At Stark, we’ve developed a five-point framework to run when designing and building any kind of software, service or tech. We have to address:
- What data we’re collecting.
- Why we’re collecting it.
- How it will be used (and how it can be misused).
- Simulate IFTTT: “If this, then that.” Explain possible scenarios in which the data can be used nefariously, and alternate solutions. For instance, how users can be impacted by an at-scale data breach? What happens if this private information becomes public to their family and friends?
- Ship or trash the idea.
If we can only explain our data using vague terminology and unclear expectations, or by stretching the truth, we shouldn’t be allowed to have that data. The framework forces us to break down data in the most simple manner. If we can’t, it’s because we’re not yet equipped to handle it responsibly.
Innovation has to include people with disabilities
Complex data technology is entering new sectors all the time, from vaccine development to robotaxis. Any bias against individuals with disabilities in these sectors stops them from accessing the most cutting-edge products and services. As we become more dependent on tech in every niche of our lives, there’s greater room for exclusion in how we carry out everyday activities.
This is all about forward thinking and baking inclusion into your product at the start. Money and/or experience aren’t limiting factors here — changing your thought process and development journey is free; it’s just a conscious pivot in a better direction. While the upfront cost may be a heavy lift, the profits you’d lose from not tapping into these markets, or because you end up retrofitting your product down the line, far outweigh that initial expense. This is especially true for enterprise-level companies that won’t be able to access academia or governmental contracts without being compliant.
So early-stage companies, integrate accessibility principles into your product development and gather user data to constantly reinforce those principles. Sharing data across your onboarding, sales and design teams will give you a more complete picture of where your users are experiencing difficulties. Later-stage companies should carry out a self-assessment to determine where those principles are lacking in their product, and harness historical data and new user feedback to generate a fix.
An overhaul of AI and data isn’t just about adapting businesses’ framework. We still need the people at the helm to be more diverse. The fields remain overwhelmingly male and white, and in tech, there are numerous firsthand accounts of exclusion and bias toward people with disabilities. Until the teams curating data tools are themselves more diverse, nations’ growth will continue to be stifled, and people with disabilities will be some of the hardest-hit casualties.
giniPredict launches in ANZ for planning and forecasting in small businesses
giniPredict has recently launched as a new solution to give small businesses in Australia and New Zealand faster, more accurate and more powerful planning and forecasting capabilities.
An intuitive, no-code technology that runs on top of familiar software products, giniPredict unlocks the patterns hidden in business data. Making the power of enterprise data analytics accessible to SMBs, it helps them to assess the commercial impact of individual variables, identify the best outcomes, and prioritise investment and resources.
Fung Lim, giniPredict’s General Manager for Australia and New Zealand, said, “Most financial software focuses on measuring the past. But growing a profitable, sustainable business is based on understanding the future. giniPredict brings the speed, simplicity and convenience of consumer apps to business forecasting to make better, faster, more informed decisions.”
Previously, capitalising on the potential of artificial intelligence and machine learning required specialist in-house technical capabilities and the deployment of substantial resources. This effectively excluded most small and medium-sized businesses, which account for 98.5 percent of businesses in Australia, 97 percent in New Zealand and more than half of employment worldwide. giniPredict changes that by putting powerful data modelling technology within reach of every business.
giniPredict integrates with Xero, and with just a few clicks, small business leaders can model a variety of future commercial scenarios based on their historical data. As a special incentive for businesses to trial giniPredict, the first 200 licences in Australia and New Zealand will be provided free of charge for a period of six months. Otherwise, a subscription will cost $19.99 per licence per month.
Up until now, accessing the potential of machine learning to do predictive modelling required resources and technical capabilities most companies simply don’t have. That means the world’s small businesses have been unable to leverage this tech.
giniPredict was built so that snaller organisations can understand their business better through the use of analytics, and run accurate forecasts, model scenarios, and assess the impact of a range of variables to identify the most productive outcomes.
giniPredict plugs into familiar interfaces – integrating with familiar tools and workflows, such as Xero, Google Data Studio and Google sheets.
Fung Lim continues, ‘Growing businesses in Australia and New Zealand are at the head of the pack globally when it comes to adopting cloud business technology, and giniPredict effectively gives them access to a data scientist. It plugs straight into Xero and immediately enables non-technical and non-specialist users to model scenarios, explore options and select better commercial futures.”
gini chose to launch their product in the A/NZ region first, due to high levels of uptake for cloud solutions, the relatively advanced adoption of technology in general and maturity of the digital market.
Businesses interested in the service can find out more at www.gini.co
Why data and analytics are critical on the farm
From the introduction of the steel plow in 1837 to the adoption of advanced technologies like GPS, IoT, and AI, farmers have always looked at how technology and innovation can enhance the work they’re doing. This is incredibly important as they are tasked with an enormous job: to create the food, fuel, and fiber for 7.7 billion people worldwide.
With researchers projecting that our population will grow to nearly 10 billion by 2050, the ability to increase crop yields while lowering traditional inputs’ levels is more important than ever. In a typical year, farmers experience many challenges out of their control, such as unpredictable weather, varying soil types, and fluctuating markets. To better manage such variables, farmers rely on data to make timely, informed, and precise decisions.
The Value of Data in Agriculture Today
Nearly every farmer in the world relies on a mix of historical and real-time data to make informed decisions in their fields. A 2020 study by Purdue University of 800 farmers highlights that only a small minority of 7% do not collect any data related to their yield, soil sampling, or satellite imagery. Data collected through AI, IoT, and advanced robotics is vital for farmers to know what’s happening with each seed, each plant, and each machine.
Through various data points captured on the field and in their equipment, farmers continuously explore data for all variable conditions on the farm to ensure operations run seamlessly at a speed and scale unattainable through manual labor. Every year machines are deployed throughout each step of the farming cycle, and as they perform their jobs, they’re gathering a vast amount of information–from planting conditions all the way to crop success. These insights are gathered year-over-year, and that historical data is working in tandem with real-time data to help a farmer make the most informed decision possible. This is incredibly important as agriculture is one of the most unpredictable industries.
Adopting new technologies is also imperative for enabling interoperability between different software and hardware agriculture solutions to process large volumes of data while making businesses more efficient, sustainable, and profitable.
Capturing Data at Every Stage
Data collection on a farm is more complicated than data collection in a strictly office-based business because key data on a farm can come from places humans aren’t physically able to go to or conditions where a human can’t see. That’s why data and analytics capabilities on a farm have had to become so advanced. For example, a farmer must do multiple jobs simultaneously, so through connected machines and screens that provide real-time data, they can get insight into the most critical functions at all times. Additionally, AI helps farmers “see” beyond human capacity, monitor what’s happening in real-time, and gather data that is used to create insights at any point throughout the growing season.
Farmers leverage data to make smart decisions throughout the life of a single crop. Farmers know exactly where each seed goes into the ground, and overall conditions during planting as data-driven planters can vary the rate at which they plant seeds to eliminate overuse – from the target rate and depth of the seed to how hard it to push the seed into the soil. These machines are also able to self-steer themselves, precisely place seeds and develop accurate geospatial data insights. In the next stage, advanced spraying technology treats each plant individually, applying the exact amount of nutrients needed to protect the plant and surrounding plants and soil.
Farmers are then able to monitor the growth of their planted seeds remotely continuously. Once crops are ready to be harvested, the powerful combination of data and technology enables the equipment to precisely separate grains from the rest of the plant without damage to the kernel. With more and more data being collected for each seed in each stage, farmers leverage AI to combine more data points and better understand the impact of each independent decision taken on or off the field.
Additionally, predictive and preventative maintenance is made possible by collecting and monitoring machine data from any farmer opting into that. This, in turn, enables dealers to detect any issues proactively and from faraway locations, providing support in many cases before a farmer even knows there’s a problem. Many updates and fixes can be done over the air, and with these proactive alerts, downtime can be kept to a minimum.
Access to real-time and historical data and advanced automation transform the smart farming industry as more farmers embrace technology like AI and machine learning to aggregate trends, boost innovation, manage risks, save costs, and enhance supply chain management. The availability of smart sensor data enables farmers to make more informed decisions and comprehend in-the-moment conditions that can impact their operations. Sensors also continuously collect more data with time so that farmers can identify recurring patterns, predict future trends, and prioritize necessary changes.
Data’s Role in Farming’s Bright Future
Big data is key to producing quality food sustainably that can feed today’s growing population. The U.S. Department of Agriculture estimated that farms could add $47 to $65 billion annually to the domestic gross economy by implementing “broadband e-connectivity and next-generation precision agriculture technology.” Technology and analytics, combined with a farmer’s experience and determination, have the power to transform a network of fields into an efficient and highly profitable business. Thanks to data and analytics, the future of farming have never looked so bright.
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