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AI

AI-powered deep neural nets increase accuracy for credit score predictions

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Credit Karma has more than 110 million users and a customer approval rate of 90%, but that wasn’t always the case. When the company launched 14 years ago, its approval percentages were in the single digits, chief technical officer Ryan Graciano said during VentureBeat’s virtual Transform 2021 conference last week.

The reason for this turnaround? Big data and machine learning.

When Credit Karma launched in 2007, the company relied on traditional datacenters because the cloud wasn’t yet part of the conversation. There would have been trouble with banking partners and credit bureaus, and “compliance people wouldn’t even let you in the door,” Graciano said.

The company got very proficient at hardware procurement and systems management but realized the physical hardware was limiting.

“The thing about big data and cloud is that big data moves really quickly, [and] the technologies change very rapidly,” Graciano said. “If you’re needing to do a six-to-nine-month hardware procurement cycle [and] a significant platform change, you’re going to be pretty far behind the curve.”

That was the first issue Credit Karma sought to resolve — the company needed more elasticity. It wasn’t just the time required to set up the hardware, but the fact that the hardware requirements were changing rapidly to keep up with new capabilities and the technology stack couldn’t keep up.

Credit Karma wound up picking Google Cloud and its machine learning offerings because BigQuery and TensorFlow made it easier to handle big data.

The machine learning evolution

The machine learning attempts were initially very straightforward. The company applied simple linear regression models to the anonymized data from its databases. Later, Credit Karma moved on to using gradient boosted trees. Nowadays, the company relies on wide and deep neural nets to predict which banks will approve customers, and at what rates. This technique runs about 80% of Credit Karma’s methods and helps facilitate Darwin, an internal system of experimentation and problem-solving.

The platform Credit Karma built is reusable, Graciano said. There was a recommendation engine on top of the machine learning platform, and everything else connected to it. Anything that happened with Credit Karma came from the system, whether it was receiving an email from Credit Karma, a push notification, or badges on the site.

“All of those things are powered by this one single system. And so that gave us the ability to spend a lot of time on the nuts and bolts of how our data scientists would work in the system,” Graciano said.

It is far easier to add new data sources and clean up the data than it is to define new algorithms. One way to improve the system is to add orthogonal data, rather than innovating on the algorithm, Graciano said. The company’s prediction capabilities expanded as more data sources were added.

“Getting those additional elements is actually a lot more powerful than the 32nd iteration on our algorithm can ever be,” Graciano said.

Graciano acknowledged it took a while to figure out what Credit Karma needed — such as a platform that allowed data scientists to automate retraining models.

“I would say we stumbled through many, many issues,” Graciano said.

Cloud was the way forward

Graciano recommends businesses move toward the cloud because it increases interoperability within the external ecosystem.

“If you’re looking for uplift, you’ll usually get more uplift by adding orthogonal data than you will by innovating on your algorithm,” he said. For Credit Karma, this was a strategic decision that paid off for the longevity of the platform, allowing it to amass useful data and making the company able to leverage it.

“Nothing is more strategic to us than data, and having a lot of power over our data,” Graciano said. Many businesses are likely going to make this move for the very same reasons, shifting from a deterministic way of developing software to a more experimental framework.

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Categories
AI

AI and big data analytics startup Noogata nets $12M

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Noogata, a startup developing products for designing, implementing, and deploying big data analytics models at scale, today announced that it raised $12 million. In addition to expanding its workforce, Noogata plans to spend the capital on accelerating its go-to-market efforts as it seeks to acquire new customers.

IDC expects the worldwide big data analytics market will be worth $274.3 billion by 2022. Thanks to AI, enterprises can collect, enrich, and model data insights, forecasts, and recommendations across departments ranging from sales and operations to finance and marketing. But historically, getting this AI into production required in-house development or proprietary out-of-the-box solutions. Until relatively recently, there hasn’t been an easy, no-code way to integrate enterprise data systems with predictive models.

Tel Aviv, Israel-based Noogata, which was founded in 2019 by Assaf Egozi and Oren Raboy, a former senior product manager at Cisco, offers modular preset “AI blocks” engineered to target business needs like managing ecommerce channels and supply chains. The platform leverages no-code, AI-powered frameworks to deliver data insights that companies can embed in reports and dashboards. And because Noogata abstracts away much of the development work, customers don’t have to continuously retrain or audit these AI solutions, the company claims.

Noogata

“We started Noogata because we believe the data wars won’t be won by data scientists and data engineers alone. We realized we needed a new kind of data operator,” Egozi told VentureBeat via email. “Digitization of everything accelerated in 2020 and data is everywhere — in existing systems inside the enterprise, with partners and along the supply chain. It’s also in new and alternative, external sources like social media, news, weather, research reports, and customer buying patterns and profiles … Enterprises have made great strides in building their data foundations. Now they need to use that foundation to make better, faster decisions.”

Noogata says that the Colgate-Palmolive Company is using its platform to support key sales and marketing functions. Another customer, PepsiCo, is employing the platform across certain farming sites in Europe to help optimize the yield of agricultural raw materials.

“PepsiCo is always looking for ways to leverage breakthrough technology that can help advance our sustainability goals,” PepsiCo senior director David Wilkinson said. “By applying AI to the crop data captured in partnership with farmers, we can gather important insights to further improve decision-making across our agricultural supply chain. The Noogata AI platform is helping us take steps to further optimize crop yield, which helps support farmers’ productivity, profitability, and resiliency.”

Noogata

Noogata competes with several startups in the big data analytics space. There’s Firebolt, a startup developing a cloud data warehouse for analytics. Leadspace offers a customer data platform that uses AI and big data to help sales and marketing teams build B2B customer profiles. And Dremio, which recently nabbed $135 million, sells tools to help streamline and curate data.

Time will tell whether Noogata can maintain its early market momentum. But Team8, which led the seed funding round announced today with participation from Skylake Capital, is optimistic about the platform’s future. “Noogata is perfectly positioned to address the significant market need for a best-in-class, no-code data analytics platform to drive decision-making,” Team8 managing partner Yuval Shachar said. “The innovative platform replaces the need for internal build, which is complex and costly, or the use of out-of-the-box vendor solutions which are limited. The company’s ability to unlock the value of data through AI is a game-changer. Add to that a stellar founding team, and there is no doubt in my mind that Noogata will be enormously successful.”

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Repost: Original Source and Author Link