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Driving with Data: How AI is Personalizing the Auto Insurance Industry and Saving Lives

In this special guest feature, Gilad Avrashi, CTO & Co-Founder at MDgo, believes that now is the time for the insurance industry to leverage AI technology to unlock the power of data and provide personalized services that customers demand to retain them as loyal customers.

Big Data & Credit Score: What’s Changing

In this contributed article, Magnolia Potter describes how the availability of large quantities of consumer data, also known as big data, has broad implications for the way we measure credit. No longer will consumers be tied to traditional banking for building credit. Now, all kinds of metrics can help prove credit worthiness.

How Can We Best Identify and Nurture Promising AI Startups?

In this special guest feature, Salvatore Minetti, CEO, Fountech.Ventures, outlines a number of important points for how we ought to help promising early-stage startups build towards commercial success. Investment alone is not enough, and solving key societal problems will only happen if we properly support and nurture the innovative AI startups who are developing the actual technology.

Be More Da Vinci: Why Data Scientists Should Be Working with Designers, Futurists and Business Executives

In this special guest feature, Michael Kanazawa, EY Global Innovation Realized Leader and EY Americas Advisory Growth Strategy Leader, discusses why data scientists and engineers should be working with designers, futurists, and business executives as the way to lead the future. Failing to do so risks being consigned to the past.

Artificial Intelligence Startups Raised $61.6bn in Total Funding, a 35% Jump in a Year

As the algorithms and data-driven methods continue to develop, venture capitalists are becoming more interested to invest in artificial intelligence startups providing the most beneficial solutions. According to data gathered by BuyShares.co.nz, the total amount of funds AI startups raised over time reached $61.6bn in the second quarter of 2020, a 35% increase in a year.

The Evolution of Digital Banking Post COVID-19

In this contributed article, Karen Krivaa, VP of Marketing at GigaSpaces Technologies, discusses how Open Banking will become more of a necessity now and the need for efficient data architecture to help drive the adoption. In the current COVID-19 climate, this notion brings the Digital Banking (with need for Open Banking) idea back into the limelight.

New Salesforce Research AI Simulates Millions of Years of Economic Data Using RL

Salesforce Research published a groundbreaking paper, The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies, which applies AI and reinforcement learning to create tax policy for the first time.

Interview: Justin Grossbard, Co-founder and CEO, Compare Forex Brokers

I recently caught up with Justin Grossbard, Co-founder and CEO of Compare Forex Brokers, to discuss his perspective for how big data has made a significant impact on financial markets, including Forex. He also assesses the success in incorporating machine learning and algorithmic trading techniques to expand upon execution and trading strategies in order to make more informed decisions.

Understanding Forecasting Methods in an AI World

In this special guest feature, Geoff Birnes, SVP of Customer Engagement at Atrium, discusses how the CRM space is being completely altered by the introduction of machine learning, data analytics and advanced AI. Traditionally, the world of CRM has been focused on data storage and minimal engagement, but now there is an opportunity to create full-scale systems of intelligence.

The Pros and Cons of Google’s New AI Transparency Tools

In this special guest feature, Jay Budzik, CTO at Zest AI, discusses the set of developer tools that Google launched that allow data scientists to create explainable ML models, and also the reality of these new tools and how they are likely to impact the financial services market. Google is taking important first steps to bring more transparency to the world of ML. That’s encouraging because the sooner our industry cracks open those scary black boxes, the better—for lenders, consumers, and the tech community.