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Podcast: Using AI to digitize financial documents at scale, with Ocrolus CEO Sam Bobley

Podcast: Using AI to digitize financial documents at scale, with Ocrolus CEO Sam Bobley

Hi Fintech Futurists —

Welcome back to our podcast series! For those that want to subscribe in your app of choice, you can now find us at Apple, Spotify, or on RSS.

In this conversation, we chat with Sam Bobley, founder and CEO of Ocrolus, a fintech infrastructure company that powers underwriting processes for lenders like SoFi, Lending Club, and Enova.

He started building Ocrolus in his parent’s kitchen when he was 22-years-old. Six years later, the company has more than 900 employees globally, across four offices. Along the way, Sam authored a patent application, helped raise over $50 million in venture capital, and surrounded himself with a world-class team of coworkers, investors, and advisors. Inc. Magazine recognized Ocrolus as the #1 fastest-growing fintech company nationwide, and the #1 fastest-growing software company in NY.

Additionally, Sam is a Venture Advisor to Green Egg Ventures and a BD Scaffolding - an investment fund created to support entrepreneurs and venture investors in the Ocrolus network.

NEXTSUMMER20 On Demand: Ocrolus - NEXT Mortgage News

In Partnership

We can’t stop fraud outright, so how do we use fraud attacks to improve our user protection processes? Join the discussion, “Turning pain into gain: How to learn from and adapt to fraud,” Nov 7th at 9 am PT to learn more.

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Topics: artificial intelligence, ai, machine vision, OCR, LLM, machine learning, automation, fintech, embedded banking

Tags: Ocrolus, Plaid, OpenAI, Google, Amazon, AWS, OnDeck

👑See related coverage👑


  • 1’22”: Early entrepreneurial insights: Money's role Sam’s upbringing and fintech's influence

  • 5’15”: The dawn of image recognition: Founding a company on the cusp of AI advancements

  • 12’23”: Building the ideal product: The journey to a four-hour turnaround time

  • 19’48”: The evolution of machine vision: From early days to cutting-edge capabilities

  • 26’34”: Enhancing the developer experience: A deep dive into integration and seamless interactions

  • 34’02”: Navigating the journey of building Ocrolus: Inflection points, growth, and challenges

  • 38’48”: Staying resilient through business challenges: Advice for entrepreneurs from a seasoned founder

  • 40’28”: Maintaining leadership resilience: Strategies for navigating the roller coaster of entrepreneurship

  • 44’42”: Future trends in AI and embedded finance: Insights from a tech entrepreneur

  • 46’21”: The channels used to connect with Sam & learn more about Ocrolus

Sneak Peek:

Sam Bobley:
…human verification, high-stakes decisions require perfectly accurate data. We don't settle for 89% or 91% or 93% or 95% or 97% accurate data. We do the human-in-the-loop validation, reconciliation checks, and we ensure that we get to 99 plus percent every single time.

Second, we run fraud detection on the document. Fraud is a really, really big pain point for our lenders. We have built a technology that analyzes the metadata of a document to see if the document has been tampered or Photoshopped or altered with, and we catch all sorts of crazy things, borrowers, changing their account holder information, adding extra zeros to their balances, hiding certain transactions, taking a 2018 statement and making it look like a 2023 statement.

Ocrolus Pricing, Cost & Reviews - Capterra UK 2023

One stat there is about 6% of the applications that we see from our lending customers have some form of suspicious activity or fraud. And in some cases, we're helping a lender, literally stopping them from putting $50,000 out the door later that afternoon in a business loan because we've identified that the documents are fraudulent.

The third piece is analytics. So, in business lending, again, it's calculating things like the revenue and seasonal cash flows of a business which become inputs directly into the lender scoring model. Over the years, we've also expanded into mortgage lending and consumer lending, including use cases in personal and auto and various different pockets of lending. We offer similar capabilities on the consumer side.

On the consumer side, the lenders are most interested in income calculations, so we synthesize information from bank statements and pay stubs and W2s and other tax documents. We not only return the raw data, but we also synthesize the data into income calculations that go directly into the lender scoring model.

file tampering detection by month and document type chart

And then, the final piece is…

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Fintech Blueprint 🤖🏦🧭
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