Next Generation of Underwriting Holds the Key
Today, humans make underwriting decisions with some help from machines. Tomorrow, machines will make underwriting decisions with some help from humans.
Two centuries ago, life underwriting was pure art: the underwriter would listen to a story and make an assessment. Evidence-based underwriting emerged about a century ago when the science of health data gained credibility. Underwriting, at present, is a fine balance of art and science.
The trend, however, is toward a fact-based scientific approach. Underwriting risk scoring is becoming increasingly evidence-based, with advances in the digitization of health care and scientific data and exponential improvements in predictive models. This is step one toward automation.
Future Automation and the Role of the Underwriter
Let us establish that an automated future for underwriting is a reality and a business necessity. In economics, a concept called the Balassa-Samuelson Effect explains how countries that do not adopt innovative technologies eventually experience higher prices (inflation), lower interest returns and lower currency exchange rates. Similarly, the industries, professions or corporations that do not adopt emerging technologies experience higher costs, lower margins and eventually lower returns.
Since over 90% of start-ups do not meet expectations, expert underwriting is crucial. Everyone has a great story capture in a powerful pitch deck. But what is actually doable? What are the technical barriers? These items are seldom professionally addressed. If they are, are you in a position to put the explanation in the proper perspective?
For example, in 1913, Ford Motor Company introduced the innovative assembly line concept to manufacture cars. This technology reduced car production time from 12 hours to 2.5 hours, a 500% productivity boost. People laughed and Ford went it alone.
Challenges to Effective Coevolution
The established consensus is automation in underwriting is necessary. However, who can address the challenges and think through the long-term implications? The scientific underwriter is an eclectic mix of executive, data scientist, technician, and innovator. Machines can take over the simple cases, and experienced (human) underwriters handle the complex cases
Machine learning platforms that automate risk scoring are developed using past data. Newer data sources like social media, the internet of things, patent databases, and automated research are available for underwriting decision making. Our underwriters understand the implications of this new information on risk scoring. It is difficult to imagine the future of underwriting without a human touch. Today humans do risk scoring and decision making, machines/robots/computers help us make these decisions faster and better.
We combine both technology and the world’s best scientists. We review business proposals to see in they are scientifically doable. We do not review markets or teams, your own gut feelings, and other resources are better at this. We review scientific claims and report whether our experts think that they are doable. We put the scientific claims in perspective and believe in the old adage that extraordinary claims require extraordinary proof.
Before you invest, we strongly recommend that team review the scientific claims made by documentation that is provided by the project team.