Quantemplate has appointed industry veteran David Lungren as Chief Executive Officer (CEO) and announced ambitious growth plans after receiving an injection of £2.5 million (US$3.34mn) in new capital from Lake Columbia Partners.
The InsurTech provider offers reinsurers data transformation and analytics tools; it boasts a five fold increase in revenue in the last 12 months after multinational insurers and reinsurers licensed its technology solution.
Newly appointed CEO Lundgren brings insurance industry and operational expertise from firms such as Allianz, CUNA Mutual, General Electric, and AgPro Exchange, and currently serves on the boards of Jewelers Mutual and Bunker.
He commented;“this year, to add to the continued confidence of the investment community, Quantemplate has also experienced a real shift in market momentum with leaders in the Life and the P&C sectors, as well as major reinsurers, signing multi-year agreements with us.
“Quantemplate’s data transformation and analytics suite, powered by machine learning, gives our clients the tools to expand their business, cut costs and, most importantly, drive forward the digital transformation which is a strategic imperative for most insurers.”
The latest £2.5 million of capital is in addition to fresh financial commitments from Quantemplate’s original investors. Quantemplate has received a total investment of over £10 million since its founding five years ago.
Daniel Bull from Columbia Lake Partners, the firm’s newest capital partner said Quantemplate has shown “impressive traction in acquiring some of the world’s leading insurance companies as customers.
“The team has proven that they truly understand the needs of the insurance vertical, and Columbia Lake Partners is excited to be a part of their continued growth in the UK and Europe.”
Quantemplate uses analytics from disparate data to offer business insights around market opportunity, underwriting profitability, claims and fraud, and MGA (managing general agent) integration.
The InsurTech firm claims to automatically unify disparate types of data reinsurers process, solving issues of data integrity or compatibility through machine learning – solving problems where traditional technologies and outsourcing have often fallen short.
