We're excited to announce that we'll be attending Solar Power International this September. Having gotten our start in solar, our end-to-end machine learning platform has been helping innovative residential solar companies grow and market their services more effectively since 2012.

This year we'll be offering one-on-one demos of the Faraday AI Platform. Stop by to see how enriched consumer data and predictive machine learning can help you identify the best new leads and better engage your current customer base.

Our platform supports your omnichannel strategy — from digital marketing to canvassing initiatives to direct mail campaigns — by leveraging enriched consumer data on the household level that lets you target customers who live in homes most suitable for solar. This data, used with AI-driven geospatial intelligence and real-time lead scoring, puts the best-fit leads in front of your team.

If you plan to be at SPI this year, make sure to stop by booth 5632 for a one-on-one look at the platform. You can learn more about Faraday and book an appointment with us in advance here.

Faraday is excited to present a live demo on the main stage at FinovateFall 2019. With over 75 live demos, upwards of 120 speakers, and an impressive AI Summit, how you're leveraging data and machine learning to grow and market your financial solutions is a hot topic this year.

As the fintech space continues to evolve, delivering relevant, personalized experiences at scale is crucial to winning and retaining valuable customers. We're excited to showcase how Faraday can help you reach lookalike audiences across your online and offline channels, prioritize and personalize nurture campaigns in real time, deliver relevant product recommendations at every touchpoint, and proactively re-engage churn-prone customers.

If you're heading to FinovateFall this year, make sure to catch our live demo and stop by our booth for a deeper dive into the platform.

Interesting sessions on data and AI

With so many innovative minds presenting this year at FinovateFall, there are a few sessions we have our eyes on.

• "The crucial role of data in executing your AI strategy" with Hossein Rahnama, CEO of Flybits
• "DEBATE: Delivering the next generation in customized customer experience" featuring thought leaders from Bank of America, Citi, and Northwestern Mutual
• "How banks are winning in wealth management with AI" with H.P. Bunaes, GM Financial Services at Datarobot
• "Technology for better data analytics and behavioral change" featuring forward-thinking leaders from Synchrony Financial and Capital One
• "Integrating hyperpersonalization into your AI strategy: The future of customer engagement" with Ramya Joseph, CEO and founder of Pefin

Faraday helps companies optimize consumer outreach and engagement across the customer lifecycle with AI-driven insights and predictions. Our end-to-end machine learning platform integrates with your customer data sources, learns from past successes and failures, and delivers powerful predictive insights to all your marketing and sales systems.

Finovate is a global conference series focused on financial services technology. Held in six locations — London, San Francisco, New York, Singapore, Dubai, and Cape Town — it offers live product demos, fast-paced content, and high-impact networking. The event is organized by KNECT365. For more information about the events or to view videos of previous demos and presentations, please visit finovate.com.

The Symitar Educational Conference

Symitar, a division of Jack Henry & Associates, is the recognized leader in core data processing and ancillary technology solutions for U.S. credit unions.

The annual Symitar Educational Conference (SEC) showcases industry-leading technologies through educational classes, roundtables, and tech leader panel sessions.

The Fintech Panel on Practical AI Use Cases

The Fintech Panel on Practical AI Use Cases aims to cut through all the hype about AI by exploring real-world applications of AI and how they're benefiting credit unions. Here are a few discussion points that will be covered by the panelists:

• What is AI (beyond the textbook definition)?
• What problems is it solving for credit unions?
• What are the requirements for AI to work well?
• Where is AI heading?

The panel will take place at 9:45 AM on August 30th, 2018.

The panelists

We're excited to announce that Faraday COO, Rob Trail, will join the panel to share his insights on how credit unions are leveraging big data and AI to optimize target outcomes across their member lifecycles.

Rob will be joined by Clinc VP, Himi Khan, and Infosys Senior Director of Client Services, Ponsi Sundaram. If AI is on your radar, you won't want to miss this panel.

Get the case study below to see how Mid Hudson Valley Federal Credit Union uses AI to acquire new members and personalize member experiences.

Diminishing barriers to entry for machine learning

Thanks to major improvements in computing power and network speed over the last decade, the barriers to leveraging machine learning have diminished significantly. We’re now seeing a wealth of companies fundamentally changing their industries with innovative data-driven processes optimized with advanced machine learning algorithms.

Common challenges in successful operationalization of machine learning

While machine learning is more accessible than ever before, several companies still struggle to successfully operationalize the technology for a number of reasons.

• Machine learning requires huge datasets to be successful. Companies often lack the volume, breadth, or depth of data needed, so they have to purchase 3rd party data which gets pretty expensive.
• Companies that have the right data still need data scientists and machine learning experts to clean and organize the data, define desired outcomes, and write queries to tell the machine learning engine what to look for. These individuals are in high demand and salaries are through the roof.
• Building accurate models is just the first major hurdle. Once models are built, engineers must develop systems to feed predictions to various destinations and track their accuracy to further refine the models.
• Due to the scarcity of talent, companies struggle to apply their resources to all business functions. Data science teams have lengthy backlogs and tend to prioritize optimizing complex back-office processes like demand forecasting and supply chain management. Consumer-facing functions like marketing, sales, and customer experience are generally lower priorities when allocating machine learning resources, despite their immediate impacts on revenue.

How Faraday helps B2C companies overcome these challenges

We understand that acquiring, managing, and implementing the resources and processes needed to operationalize machine learning can be daunting, so we bundled it all up into a simple, user-friendly platform designed for non-data scientists. With the Faraday platform, B2C companies have access to:

• 240+ integrations to seamlessly upload their prospect and customer data,
• a massive national database containing 400+ attributes on more than 260 million U.S. consumers,
• a built-in machine learning engine that automatically builds predictive models for each and every desired outcome,
• a dedicated data-science team constantly working to validate and improve model accuracy,
• and an automated campaign delivery system enabling companies to take action on their machine learning-based insights and predictions.

"Thanks to Faraday, 1 in 3 of our sales is coordinated using AI" – Chief Product Officer, Burrow.

Learn more about the process here: