Pepper the robot with two attendees at the National LINC conference in San Diego. (© LILA PHOTO for TD Ameritrade Institutional)

Vijay Sankaran is on a mission: not only to foster innovative ideas at TD Ameritrade, where he serves as chief information officer, but to engineer those ideas into useful products for the advisors who custody at TD Ameritrade Institutional.

That goal was the genesis of the innovation center that he leads in Ann Arbor, Michigan, with 200 people in Michigan and employees now in other TD Ameritrade offices in St. Louis and Chicago. The center, says Sankaran, has grown into a product developer. While the group continues to create gee-whiz tech devices like Pepper the robot and the holograph machine on display at TD Ameritrade Institutional’s annual LINC conference for RIAs in San Diego, it’s also been able to quickly turn ideas into usable products.

That’s the case with three product introductions at LINC, all of which have been helped by a focus on agile software development at TD Ameritrade since Sankaran became CIO in 2016.

The first are two Amazon Alexa ‘skills’ for advisors: one is a voice-activated briefing that provides advisors with practice management insights and technology tips; the other allows clients of RIAs who custody with TD Ameritrade Institutional to check their account balances, positions and quotes, along with requesting market updates, all by using the virtual assistant’s voice command features. Those products took four months from idea to market, Sankaran reports.

A similar Alexa offering for retail TD Ameritrade clients was rolled out last October.

The second rollout at the LINC conference that began life at the innovation center is a completely redesigned version of AdvisorClient.com, the portal site for clients of advisors who custody with TD Ameritrade Institutional. There are mobile AdvisorClient apps for Android and Apple, too, and the site was built with a typical TD open architecture to allow integrations with third-party technologies. That development, says Sankaran, took six months from idea to market, thanks to its agile chops.

The value behind these innovations is that they allow end clients to access their account information on demand, and using familiar technologies, without having to call their advisors for such quotidian information.

The overall goal, says Sankaran, is to make “every associate an innovator” at TDAI, leveraging the company’s technology resources to move from “fulfilling ideas to creating ideas” that will be useful to advisors because they emanated from the TDAI associates who are closest to advisors and their needs.

When asked which new technology now germinating will prove to have the greatest effect on advisors, Sankaran responds by naming artificial intelligence and machine learning, which he thinks will prove “incredibly important” for three use cases.

The first use case is self-service. There are many times when advisors will call into their TD Ameritrade support staff to access certain tax forms, for instance. But Sankaran says his team is training the TD AI engine to build a repository of such documents so that advisors can use the Veo platform’s new “virtual assistant” to request the delivery of such documents through a chat function.

The second use case comes from a goal of driving more personalization into the platform. Sankaran’s team has already built out an AI recommendation engine on the company’s education resources so that, in ‘Netflix style,’ the platform will monitor an individual advisor’s prior trading history or past information requests and then suggest additional education or guidance on those topics.

The third use case focuses on analytics, using data big and small. Building on TD Ameritrade’s FA Insights benchmarking research, Sankaran’s team is working with TDAI’s analytics team to determine how they can provide better data on advisors’ own clients and other advisors’ clients to create a dashboard “over time.” That dashboard could yield ways for each advisor to better serve existing clients and discover ways to attract similar clients based on a number of data points, such as the shared interests of clients and prospects, Sankaran says.

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