Lending Valley CEO Chad Otar Announces A.I. Powered Funding

Artificial intelligence is the smart investment in finance right now. The financial industry is capitalizing on new tech to work smarter and faster, meeting client demands better than ever. Now funding expert Chad Otar is throwing his hat in the ring with the latest in AI for file underwriting.

Chad Otar, founder and CEO of the Lending Valley startup, explains that the firm is trialing proprietary AI technology to underwrite files in seconds. Condensing a days-long process into seconds? Apparently so.

By reducing the underwriting turnaround time, his advisors can focus on delivering a personalized loan recommendation with a lean wait time. When Lending Valley’s clients need a quick capital injection, turning a ‘pretty fast’ application process into a ‘record speed’ one can be the pivotal move that keeps the front-running company ahead of its competitors. And there are many.

So it’s no surprise the funding powerhouse is fired up to deliver on the ever growing expectation of online consumers.

AI might be the trending topic of 2020, but Otar cautions us on using the right applications. According to the CEO and thought leader, it’s not about using everything—it’s about using the best tools for your industry. In the Finance industry, he doesn’t want to see AI involved in the loan recommendation process, and says we’ll need to tread lightly in the current trend for self-service lending.

According to the experts, funding recommendations should be tailored to what the client needs, what they can afford, and what they need to achieve at various stages of their business’ life-cycle. But that’s only the start. There’s a lot to factor in. Right now, AI can’t do that as well as a lending expert can.

Otar explains, “What AI can do—in our industry—is streamline the processes earlier on in the system. We’re using a highly sophisticated algorithm, backed by years of data, to pull all the information we need and match each unique client to the best possible loan approval…in seconds. That’s what’s going to drive us forward.”

So if AI isn’t ready to handle client interaction right now, what is the FinTech arena using it for?

3 FinTech spaces where AI is stretching its legs

Establishing Creditworthiness

Lending 101: the value of a loan is based on determining how likely an individual or business is to default on that loan.

Even with all information at your fingertips, calculating this formula can be a mammoth feat. Take into account that often in the application process, the information is incomplete, inaccurate, or—as is the case with up to 25% of millennials applying for credit—totally dishonest. Already, we are faced with investing major hours into a part of the loan process with very little return.

AI can examine an extensive list of metrics—not only FICO score, income, and expenses—and evaluate creditworthiness almost instantly. Even applicants without a traditional credit history can often be analyzed accurately, based on the alternative data AI can explore.

Streamlining processes

As Otar mentions, streamlining the processes involved in a lending application are where AI can really earn its keep. Eliminating clunky administrative systems and bypassing the delays of a filing backlog drive profits up, and also push wages down. More inspiringly, AI streamlining means your people are freed up to focus on building client connections, creating strong loan recommendations, and driving your business to the next level.

Improving customer experience

In the field of loan-specific AI range, traditional lenders are using AI-powered tools to help improve loan repayment rates and loan management. Chatbots, prompts, analytics, and CRM—customer relationship management—tools all help manage customers away from loan default territory.

Where to next for AI in Finance?

With over two decades in the FinTech arena, Lending Valley’s CEO is excited about where AI can take the industry he calls home. It’s all about handling technology, and making sure tech isn’t handling us.

Industry execs predict some issues around privacy, ethical concerns, and legal manoeuvring as the industry seeks to find its footing between what is good for business, and what is good for people.

“At the end of the day, what is good for people is what is good for business. Lenders and funding platforms need to keep people at the front of their business strategy, and develop strong systems around storing ‘big data’.”

Otar predicts the use of artificial intelligence to determine creditworthiness will grow, and streamlining systems with machine learning will continue to increase.

He points out that there are billions of people globally without credit histories, and forward-thinking companies are going to need to capitalize on those people wanting homes, credit cards, and personal loans in the future. Developing the systems to cater to this niche now will open up a major market further down the track.

The Lending Valley founder adds one caveat to his passion for AI: a system is only as good as the people who build it. That's why his AI-powered system is built by his own people.

And where to next for Lending Valley?

The announcement of their foray into AI-powered lending is an exciting next step for the startup.

Managed growth is a focal point for its leader right now.

The company has grown exponentially in the 11 months since its inception, and has quickly surpassed its competitors on the micro funding circuit. This first year has seen Lending Valley source over $2 billion in client capital, and help thousands of small businesses achieve their working capital requirements.

As a proud sponsor of NYC’s Broker Fair 2020—putting the Lending Valley name firmly in the sights of the country’s top commercial finance brokers and lenders—things are likely to get a whole lot bigger.

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
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  • Quantitative Trading Strategies with R
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  • Python for Data Science
  • Machine Learning in Finance using Python

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