Lemonade, a company offering home insurance policies, is a pioneer in the InsurTech world where its use of machine learning (ML) goes beyond satisfying customers and driving efficiencies to underwriting risks and managing claims. Viewing Lemonade as a revolution to the insurance industry, investors like SoftBank and Google Ventures backed the company with a $120 million funding . The investors’ trust was strengthened as the company managed to attract more than 14,000 subscribers in eight months only .
Figure 1: Lemonade’s growth trajectory 
Despite its exponential growth, Lemonade’s scalability is questioned considering increasing customer demand and limitations around availability and precision of data.
Lemonade’s unique business model
Lemonade’s business model is based on a transparent fee model, fast claim settlement, and social good.
The InsurTech company communicates its fee structure with its customers to achieve trustworthiness and transparency; two key attributes missing in the insurance industry.
Collected customer premium is utilized by Lemonade in the following manner:
- 20% flat fee for Lemonade
- 40% for reinsurance to cover major claims
- 40% for claims; any surplus goes to charity
Figure 2: Lemonade’s business model 
Apart from its transparent fee model, Lemonade differentiates itself by replacing agents with bots that run insurance processes from end-to-end. However, more than half of Lemonade’s claims are still being handled by humans given their complexity which means that Lemonade still has a long way to go .
Use of machine learning to transform the DNA of insurance
Lemonade uses ML to underwrite customer risk and handle claims transforming insurance from “grudge purchase” to a “convenience purchase”. The usage of AI significantly improves processes in insurance as it increases underwriting precision improving premium pricing accuracy, enhances customer experience through fast paper-free processes, and lowers claims costs by reducing fraud.
When issuing insurance policies, Lemonade uses big data to predict risks and quantify losses by placing the customer in a risk group and thereby quoting a relevant premium. These groups of “uniform insurers” share similar risk behaviors and are compiled by AI algorithms that gather extensive customer data and monitor loss ratios (“ratio of claims paid by an insurer to premiums earned” ). The more data accumulates, the more recursive risk patterns emerge enabling more precise assessments.
Figure 3: recursion patters forming with data accumulation 
Figure 4: improvement of underwriting precision with more data points 
In claims settlement, Lemonade’s “AI Jim” assesses the claim by cross-referencing home information, compares it to the customer’s policy, runs fraud algorithms, and finally approves or rejects a claim. The whole process takes up to three minutes and is approved within seconds .
Figure 5: claim handled by Lemonade’s AI Jim 
Lemonade’s scalability questioned
Lemonade acknowledges that its AI models and infrastructure are not yet equipped to achieve its innovation targets . Improving current models is important, but the company should also have a vision for its expansion plan to new products and geographies.
The following challenges should be addressed for Lemonade to achieve scale:
- Increase in customer demand: increase in claims especially after natural disasters should still be handled in a cost-efficient manner. Will lemonade’s bots and “no-employee” model be able to process all the data as these transactions become more complex? 
- Data shortage: Lemonade’s databases are still limited. How will it compete with traditional insurance companies that own extensive data and are already digitizing processes? Will Lemonade’s AI-based underwriting still be relevant? 
- Low quality of data: gathered data is based on customer information which could potentially cover factors important in decision making. How can Lemonade ensure its data is accurate, representative, and unbiased? 
While the company mentions a medium-term plan to scale, it still does not have a concrete strategy to address the aforementioned issues. Lemonade should consider practical solutions such as acquiring representative external data to train its models, partnering with large insurance companies to handle complex claims, and outsourcing customer service when capacity is exceeded.
Future of machine learning in insurance going forward
As the company thinks about improving its current AI models and prepares to scale, it could explore a wider use of AI across the insurance value chain. Will it be possible to use AI in product development i.e. in customizing policies? Can it use ML and IoT to prevent risks from happening? (800)
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