Tractable claims $25M to sell damage-assessing AIs to more insurance giants


This post is by Natasha Lomas from Fundings & Exits – TechCrunch

London-based insurtech AI startup Tractable, which is applying artificial intelligence to speed up accident and disaster recovery by using computer vision to perform visual damage appraisal instead of getting humans to do the job, has closed a $25 million Series C, led by Canadian investment fund Georgian Partners.

Existing investors also participated, including Insight Partners and Ignition Partners. The round nearly doubles the 2014-founded startup’s total funding, taking it to $55M raised to date.

When TechCrunch spoke to Tractable’s co-founder and CEO Alexandre Dalyac, back in 2018, he said the company’s aim is to speed up insurance-related response times around events like car accidents and natural disasters by as much as 10x.

Two years on the startup isn’t breaking out any hard metrics — but says its product is used by a number of multinational insurance firms, including Ageas in the UK, France’s Covéa, Japan’s Tokio Marine and Polish insurer Talanx-Warta — to analyse vehicle damage “effectively and efficiently”.

It also says the technology has been involved in accelerating insurance-related assessments for “hundreds of thousands of people worldwide”.

Tractable’s pitch is that AI appraisals of damage to vehicles/property can take place via its platform “in minutes”, thereby allowing for repairs to begin sooner and people’s livelihoods to be restored more quickly.

Though, of course, if the AI algorithm denies a person’s claim the opposite would happen.

The startup said its new funding will go on expanding its market footprint. Continue reading “Tractable claims $25M to sell damage-assessing AIs to more insurance giants”

Episode 114: What Makes a Successful AI Project?

In this week’s podcast Jon Prial is joined by Tara Khazaei, Chief Data Scientist, National AI Team, Customer Success Unit at Microsoft. Jon and Tara talk about how domain knowledge, as well as statistical intuition, make for more successful outcomes in machine learning projects. They discuss performance through the lens of projects Tara and her team have led at Microsoft. For instance, you’ll hear about projects the Microsoft team led to improve customer service at a bank, to improve bus time departure estimates and to predict opiate overdoses. Through their conversation, you’ll hear what makes a successful AI project.

In this episode you’ll hear:

The 10 Principles of Applied Artificial Intelligence

Introduction

Artificial intelligence (AI) is rapidly moving out of the laboratory and into business and consumer applications. As a result, we’re seeing a fundamental shift in how software is built and what it’s capable of doing.

Of course, the type of AI we’re talking about isn’t the artificial general intelligence of science fiction, where a robot or software program can do whatever a person can and more. Instead, we’re referring to artificial narrow intelligence. In other words, the use of AI in very specific contexts. It’s this type of AI that’s already being used to power some of today’s most successful technology businesses, including Google, Facebook, Amazon, Netflix, Uber and Airbnb.

AI is a rich and complex topic, and one that has evolved as a result of years of computer science research. Today, we have reached the point where AI can be applied within most businesses,

Start with the Processes
Integration
Models
Data
Governance
Start with the Processes
Start with the Processes
Integration
Integration
Models
Models
Data
Governance
Governance
Governance

Continue reading “The 10 Principles of Applied Artificial Intelligence”