Thought Leaders in Big Data: Krishna Venkatraman, Senior Vice President of Analytics at OnDeck (Part 2)

Krishna Venkatraman: In the case of understanding credit-worthiness, for example, the signals that will allow us to make good decisions reside in many different places. The first is information you get from Bureaus. That will tell you something about how businesses have been discharging on their past obligations. How did they behave when they had past loans? We get information about the industry in which they are.

There are multiple data providers that can give us that. We also work a lot with transaction data and bank data that give us an idea of cash flow. How is the business performing at the current point? What are the trends we see in the business revenue, income, and expenses? Are they consistent with what we think would be the norm for a business in that industry? It’s important for us to be able to do all of this in an automated fashion as possible, and also be >>>

Thought Leaders in Big Data: Krishna Venkatraman, Senior Vice President of Analytics at OnDeck (Part 1)

The online lending industry is going through rapid changes with the advent of big data and machine learning in processing loan applications. Krishna discusses OnDeck’s workings.

Sramana Mitra: Let’s start with talking a bit about yourself in setting some context, and also introduce our audience to what OnDeck is doing in the realm of Big Data.

Krishna Venkatraman: Let me tell you a bit about myself. I joined OnDeck in October of 2013 as the leader for the data analytics organisation. I hadn’t really heard of OnDeck until I first talked to them. I found their value proposition fascinating. There was this huge unmet need among small businesses when they were looking for capital. >>>

Thought Leaders in Big Data: Scott Zoldi, Chief Analyst Officer at FICO (Part 4)

Sramana Mitra: Let me now ask you given the trends of your industry, what are you seeing as open problems that need addressing where new entrepreneur can build stuff around?

Scott Zoldi: I’ll phrase it in terms of problems and opportunities. From a problem perspective, there are a lot of regulations that are coming up right now in terms of usage of different types of data. In Europe, they have what they call the GDPR. It is a new set of regulations around every company’s use of data to ensure that the company has the ability to remove all data if you, later on, ask them to remove your personalized data. It has a lot of responsibility around privacy and monitoring of breach information. >>>

Thought Leaders in Big Data: Scott Zoldi, Chief Analyst Officer at FICO (Part 3)

Scott Zoldi: These are technologies that are facilitated in Big Data because we have the ability, with the Storm-based architectures today, to build a model that reaches out to a NoSQL database, retrieves a data record associated with your previous transaction history. That data record is not of the transactions you did in the past but variables or summarization of your behaviours. We deal with a small latency in terms of the NoSQL database pull. We update the variables that are contained there and they’re re-updated. Those variables are sent to a model. For a fraud model, it’s a neural network-based model that produces that score. The bank takes that score and they apply their rules. They they will make the decision. That’s what get sent back to the acquirer and the merchant, which then gets back to you. If it’s declined, they’ll provide you some challenge questions or ask you to phone in.

Sramana Mitra: The kind of clustering and behavioural analytics that you’re describing, does it have heuristics to extrapolate other rational behaviours? For example if I book theatre tickets in London three months before my travel, by the time the trip happens, does your fraud algorithm know that since I have theatre tickets in London at this time, it’s legitimate that I’m spending on other things in London or is it going to trigger some sort of an exception due to a foreign transaction usage? >>>

Thought Leaders in Big Data: Scott Zoldi, Chief Analyst Officer at FICO (Part 2)

Scott Zoldi: We’ve developed, since the year 1992, technology that allows us to do that real time assessment of fraud risk using technologies like Storm, NOSql so that we can persist data in a summarised way and retrieve it very efficiently. That is one of the major areas for FICO and many of the products that we differentiate in is having analytics that run at very low latency and very high throughput to deal with the velocity of big data.

Sramana Mitra: I’m still trying to take this conversation in a slightly more use case-oriented way. Take what you described in terms of the technology and put in the context of a consumer use case and flip it around and show us how that happens. I’ll lead you into a use case that I encounter often. I travel quite a bit for pleasure. >>>

Thought Leaders in Big Data: Scott Zoldi, Chief Analyst Officer at FICO (Part 1)

Have you noticed that your credit card sometimes declines transactions or flags fraud alerts? This conversation will help you understand the backstory of that workflow and potentially trigger other ideas in that domain.

Sramana Mitra: Let’s start with introducing our audience to yourself as well as to FICO.

Scott Zoldi: I’m the Chief Analyst Officer at FICO. I’ve been with FICO for 17 years. Today I have responsibility for all the analytics products and technologies that we use within our products. FICO, as a company, is 60 years old, and is well known for the analytics that we deliver to make better decisions for companies. The main thing that we offer is the FICO score. It’s a score that’s used in lending decisions as well as fraud detection. >>>

Thought Leaders in Big Data: Mark Schwarz, VP of Data Sciences at Square Root (Part 2)

Mark Schwarz: It was very clear to the corporate owners and to a select set of stores that they needed to change their transaction prices and make them lower, and that they need to raise operational efficiency to compete with the third-party oil change providers. They work with us to build new ways of looking at their business.

It helps them collect data they never had before. They can cluster those in ways that are novel and more actionable. Then they can roll them into a set of next steps that are reasonable for both the stores and the corporate staff. As I said, in the middle, we have field managers. I see them as analysts. They are experts at dealing with data on a store-by-store level.

We’re highly concerned about what they think about how the business is evolving and how it can be better. Our job is to augment their efforts. We can give them the data they need to make a change. >>>

Thought Leaders in Big Data: Mark Schwarz, VP of Data Sciences at Square Root (Part 1)

Store management is going through transformation because of the data science. Read how the automobile sector is evolving.

Sramana Mitra: Let’s start at the very beginning and introduce our audience to booth Square Root and yourself.

Mark Schwarz: We’re a software company based out of Austin that delivers software in the store relationship management space. The store relationship management space has to do with making field staff in retail environments the champions of their respective businesses, and dealing with specific accounts that they have such as managing a set of relationships both at the store and corporate level at the same time. As I said, we have a SaaS offering. We augment that data science team in two ways—by supporting the platform and by helping with specific consulting services when necessary. There are nine of us. >>>