Emily Flitter for The New York Times published a month ago that many businesses were struggling to stay afloat while waiting for Congress to decide how to deliver these loans (https://www.nytimes.com/2020/04/16/business/coronavirus-sba-loans-out-of-money.html).

Lack of time and manual processes are two factors that are having a negative impact on the outcome and timing of these decisions. The problem is clear: the longer businesses have to wait, the greater the chances that they will close.

All companies are exploring what capabilities they have to help beat COVID-19 and its negative impact on society and the economy. At Wenalyze, we have found that there is a way where open data can be useful in identifying which businesses are stronger and weaker when facing the economic impact of COVID-19. The idea is to help financial and public institutions prioritize how to allocate their loans and financial support beginning with those whose need for receiving resources is higher. The main idea is to better manage resources in order to ensure we can save as many businesses as possible, provide a faster answer, and lower the negative impact of this pandemic.

 To begin with, we have considered two main groups of data sources: macro-economic indicators and open data sources information. There are many reports explaining which industries and what type of businesses will be more likely to close. We can find in a more vulnerable side travel agencies and gyms while we find in a stronger side online food supermarkets. With this in mind, the spread of the virus also has a significant position in predicting which areas are more likely to reopen businesses sooner. At this general level, governments and financial institutions are already working and studying, probably with a very high quality of data and knowledge.

However, there are relevant data points these institutions are generally not considering: the small and non-traditional data, which is necessary to determine one by one what businesses are more prepared to face this situation.

Wenalyze platform has the capacity to implement new data sources in a short period of time. The idea of our project was to create a solution where open data sources information could be used in different ways with different outputs, but always helping to improve the efficiency of all kinds of internal processes, and this situation is not an exception for us.

In this sense, we adapted our solution to create an indicator that determines the vulnerability of SMBs in this pandemic and the chances of continuing or stopping their business activity. To begin with, the simple ability to identify if a business has any kind of online presence is already a valuable and differential indicator. When it is not possible to find a business online, considering we know its industry, the chances that this business will persist are much lower compared to a business that has an online e-commerce shop. For example, two restaurants in the same area would be equally affected by COVID-19 if analyzed from a macroeconomic point of view. However, one restaurant has a profile in TripAdvisor, DoorDash, Yelp, Seamless, UberEATS, and also has a website where clients can call or even buy food online and they have been receiving good reviews during the pandemic. Meanwhile, the other restaurant has no presence at all. Of these two it seems clear which one is likely to continue its activity after the lockdown, and which one will have to face a more serious struggle.

How can insurers use this data?

Wenalyze has the ability to allow financial institutions to analyze businesses up to this point and sort through the list of loan applicants or clients in order to start helping the most vulnerable.

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This new and unpredictable situation that we are living in does not allow insurers to build risk models based on past data as this society has never lived a pandemic before. All models will be based on macro-economic data and theories. It is the perfect timing to adapt and build new risk-models based on non-traditional data and have a perfect picture of every client’s risks.

The post-COVID scenario is also calling for a risk reassessment of insurers clients. Adapting premiums and products to businesses new situation and risks, will not only ensure these businesses are properly insured but also will help prevent them to search for another insurance, especially for those clients that are thinking they need now to pay less for their policy. Anticipation is key to maintain clients and avoid potential losses by having an updated underwritten portfolio of customers.

From the institution’s perspective, this viewpoint is not generally considered, as the granularity of its analysis stops at general and historical data. The idea of investing in working with non-traditional data causes a reasonable but serious sense of vertigo in many of these institutions. We believe that to better cope with a non-traditional and unpredictable situation like the one we are living, we need non-traditional and innovative solutions. A reaction in which innovation is a preference is for us the clear answer that institutions must have, and we are here to help them do so.

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