How open API standards will transform financial services

Open standards will have a huge impact on driving innovation in banking. Learn the status in the U.S. – and the bold new opportunities open standards are set to usher in.

Urbint, a company developing software that can predict threats to critical infrastructure, today announced that it raised $60 million in a series C funding round led by Energize Ventures with participation from American Electric Power, and OGCI Climate Investments, as well as existing investors Energy Impact Partners, National Grid Partners, Blue Bear Capital, and Salesforce Ventures. Founder and CEO Corey Capasso said that the proceeds will enable Urbint to scale its technology, introduce new solutions, and enter new verticals of infrastructure as well as expand its workforce.

Today’s critical infrastructure owners and operators are facing new challenges as aging assets and climate change create a more complex operating environment. Electric utilities in the U.S. alone spend more than $16.6 billion annually on operations and maintenance, it’s estimated. And according to one source, 6 billion gallons of treated water is lost to infrastructure failures each day in the U.S. — enough to fill over 9,000 swimming pools.

Founded in 2015 by Capasso, Josh Troy, and Ryan Schmukler, Urbint seeks to prevent infrastructure failures from happening by leveraging real-world data and AI. The company’s software can make predictions about construction, maintenance, and field operations failures up to a week in advance, including things like a work crew hitting a gas main or a fiber cable damage knocking out internet service to a town.

Above: Urbint’s predictive platform.

Image Credit: Urbint

“[Urbint] actually started out focused on buildings, but then we had a meeting with the largest gas and electric utility in New York that changed everything,” Capasso told VentureBeat via email. “They asked us if we could use AI to predict which buildings in NYC had corroded gas pipes, in order to prevent gas leaks in people’s homes, which was not something we had attempted before … After [a] successful pilot project, we pivoted to focusing on infrastructure risk for gas and electric utilities, and never looked back.”

Predicting failures

As the U.S. prepares to spend hundreds of billions of dollars repairing national infrastructure, AI is being heralded as a solution to longstanding maintenance challenges. Google parent Alphabet’s “moonshot” X lab recently announced that it’s working on new computational tools for the electric grid. Meanwhile, startups like Myst and Autogrid are partnering with utilities to deliver AI-informed power usage insights.

As for Urbint’s technology, it brings together information about its customers’ assets, worksites, and historical records with what it describes as a dynamic “model of the world.” A representation of the environment in which Urbint’s customers are operating, the model takes elements such as weather, topography, environment, surrounding infrastructure, traffic, population, and essential facilities into account to provide a view of risk factors.

“We engineer this information into highly granular insights that inform action, so something as general as wind data becomes over 6 different data features, enabling our AI to know something as specific as which power lines are exposed to wind over 20 miles per hour from the northeast and for how long,” Capasso explained. “While that sounds complex, we present it to the customer in a simple risk alert, only highlighting the major takeaway and recommending an action to prevent catastrophe.”

Capasso said that the pandemic bolstered the demand for Urbint’s platform as field workers, who didn’t have the luxury of working remotely, suddenly became sick with COVID-19. The company claims that its customer base now includes National Grid, Southern Company, and 50 of North America’s other largest energy and infrastructure companies.

“Our biggest competitor is really inertia, as sometimes it can be hard for infrastructure operators to change when they have been attempting to manage risk one way for so long,” Capasso said. “When you have fewer resources, it’s imperative to take a risk-driven approach to maximize threat reduction with the resources you have. With society’s essential services strained, energy outages and failures to key facilities simply could not happen. We had companies turning to us to help prevent this.”

Urbint has raised $109 million in venture capital to date. The company has 100 employees currently, with plans to grow to 130 by the end of the year.

How open API standards will transform financial services

Open standards will have a huge impact on driving innovation in banking. Learn the status in the U.S. – and the bold new opportunities open standards are set to usher in.

Presented by Kasisto

Digital transformation has gone from a buzzword to an essential strategy for banks and financial institutions over the course of the pandemic — a surprising silver lining in a tough time. To meet the challenges of a world in lockdown, banks accelerated their adoption of digital channels in order to serve their customers wherever they were sheltering in place.

It’s brought the need for intelligent digital assistants that can service, engage, and acquire new customers to the forefront, says Zor Gorelov, CEO and co-founder at Kasisto.

“The acceleration of adoption of intelligent digital assistants gave traditional banks an opportunity to build better relationships with customers and establish emotional connections,” Gorelov says.

Post-pandemic challenges for banks

As the pandemic tsunami starts to recede, it’s become clear that the urgent need to adopt digital solutions continues to be the biggest challenge for traditional banks. Banks are still in the process of upgrading their core banking systems from running on mainframes and AS400s. It’s a hefty, expensive task that also requires a lot of delicacy to maintain high levels of customer service while their tech is in flux.

With legacy systems, the quality of banking data is still not great, says Gorelov. Banks have a hard time understanding where their customers’ money is, where they spend their money, where their customers are, and reconciling multiple accounts outside of their own domain. Awareness is growing in the traditional banking industry, but the pace tends to be glacial.

“Banks are reluctantly accepting open banking, banking as a service, and account aggregator companies,” he says. “They still have a lot of challenges.”

How cognitive banking is upending traditional banking

Now banks have another digital technology to accept — cognitive banking. Cognitive systems use AI technologies to help banks leverage their data to deliver insights about themselves, their customers, and their competitors. Intelligent digital assistants are the critical enabler of cognitive banking for customers, enabling users to better understand and manage their money, and make better financial decisions.

Gorelov describes it as making sure that everybody has a personalized banker that has a broad view of your finances, understands your personal life, and can provide the most unbiased financial advice to you.

It’s a level of personalization and service that traditional digital chatbots, long used in the financial services industry to answer basic consumer questions, can’t touch. Banks often rely on chatbots to lift the burden from their customer service call centers. Fintechs adopted them first, but traditional banks were not far behind.

Yet, digital chatbots are simply reactive, waiting for a user to ask a question, and they’re highly impersonal — a millennial in the early stages of their career and a multi-millionaire get the same generic response. And they could never help a client make better financial decisions, better understand and manage their money, and do it at scale.

A digital assistant wipes the floor with the traditional chatbot. When you leverage cognitive banking you’re offering your users an AI-powered solution that’s sophisticated enough to answer whys — why a customer’s balance is low, what deposits will bring them back up into the green, and so on, and then offer personalized advice about managing bills, increasing savings, and more.

“Financial institutions need to adopt cognitive banking because there’s an opportunity for them to set themselves apart,” Gorelov says.

The R&D behind digital assistants

Kasisto’s Enlighten is based directly on the company’s research into the limitations of chatbots in their system, their own users, and their own data. They analyzed 24 million utterances, or communications from a user to a system, and identified the 15 percent that made up most engaged users. They then followed those users over a period of six months, analyzing their digital footprints and their utterances. The goal was to understand who these users are, how they interact with AI, and what their expectations are, and from there they identified four distinct personas.

Then they conducted focus groups with their end users to collect data around how they react to situations to deal with issues, and solve problems. They also went to some of their most sophisticated customers and asked for their feedback about the company’s vision of what the future of cognitive banking entails.

“We started with the data. Data does not lie. Then we went to real users, real use cases for them. That’s how Enlighten started,” Gorelov says. “Trying to identify the most invested users in our systems, understand their needs, understand their personas, and then build the Enlighten product around them.”

And now financial institutions can leverage Enlighten to build digital assistants that have more discoverability, that are hyper-personalized, that are engaging, and make them available to their users.

Dig deeper: Learn more about how Kasisto’s Enlighten is enabling cognitive banking. 

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