How Big Data Brought Good Decisions to Life; A Big Data case study

June 12, 2014 | US

How Big Data Brought Good Decisions to Life

A Big Data case study.

Keith Button

For Merchant Cash and Capital of New York, an alternative financing company for small merchants, the initial goals for its first Big Data project were pretty straightforward, says its CFO, Jeffrey Beckwith: lower the company’s underwriting cost per deal and improve the underwriting decisions.

The 120-employee company has already purchased IBM SDSF software and started building a modeling process for the project, aiming to use it to search the Internet for data on it customers and then analyze it. Internet sources might include, for example, Facebook and other social media websites; Yelp and Google Reviews; and the performance of a particular merchant on the MCC website.

The company, which provided annual merchant cash advances totaling $125 million last year and expects to advance $150 million to $175 million in 2014, operates by buying a portion of a merchant’s present revenue stream. Out of that, it provides the company with cash advances.

Currently, about 20 MCC underwriters analyze the Web data as a part of their evaluation of the credit risk of potential clients, Beckwith said. “We’re moving from a more traditional underwriting standpoint,” in which people perform the analysis, however, “to a much more statistical approach,” he added.

That approach seeks a uniform standard against which the firm can gauge an individual merchant’s ability to generate business. MCC wants to use that standard to assess its current underwriting guidelines and procedures.

The idea is to extract some of the human variability out of the underwriting process so that the company’s underwriters have a more consistent basis for approvals and declination. For some smaller transactions, the company in the future may be able to remove individual underwriters from the decision. That would allow MCC to process it and get the merchant an answer much quicker. And faster decisions would give MCC an advantage over its competitors.

Larger transactions, because they carry a higher potential risk, probably will never end up as an automated decision, Beckwith said.

If the company is able to scale its underwriting department and keep the number of underwriting positions on its staff steady or expand that staff as needed at a slower rate, then underwriting cost will come down, Beckwith said. The company looks at several measures of cost, including per deal, per transaction and on a monthly, quarterly and yearly basis.

“That’s how we’re gauging our investment in those particular software products,” he said, as well as the company’s investment in staff for its new business intelligence group, which includes the big data project. The company also bought a server to handle a newly created database and statistical analysis for the unit.

Data on Deadbeats

Beckwith is looking to the big-data project to help the firm manage its entire debt portfolio. “When you put money out, there’s obviously a risk that individual companies will not pay us back. So we’re looking for commonalities among those companies that we’ve had difficulty with, and we’re also looking for commonalities in those individual companies that have been stellar performers or good performers,” he said.

MCC hired the CFO about a year ago, in part to help the company with its data. Although the firm had gathered a lot of data through the underwriting process, it wasn’t evaluating it for commonalities. The commonalities analysis will eventually incorporate information pulled from the Internet by the big data project.

One step CFOs can take before they embark on a big data-project for the first time is to look to early adopters of big data, such as IBM, General Electric, Mount Sinai Hospital in New York and nearly all major retailers, according to Chris Scott, a regional partner in charge of technology issues at Tatum Consulting.

During the early stages of planning MCC’s project, Beckwith said, he spoke to other companies with big data projects of their own as well as to consultants. That input helped him decide that the company should focus on its specific needs rather than become an open-ended “Big Data shop,” he said. “Really, for us, the need is to understand our customer better, and then how do we go about doing that.”

So far, Merchant Cash and Capital hasn’t contracted with big data providers that typically provide data on individual customers, partly because those providers usually won’t give up raw data along with their answers, Beckwith said.



About bambooinnovator
KB Kee is the Managing Editor of the Moat Report Asia (, a research service focused exclusively on highlighting undervalued wide-moat businesses in Asia; subscribers from North America, Europe, the Oceania and Asia include professional value investors with over $20 billion in asset under management in equities, some of the world’s biggest secretive global hedge fund giants, and savvy private individual investors who are lifelong learners in the art of value investing. KB has been rooted in the principles of value investing for over a decade as an analyst in Asian capital markets. He was head of research and fund manager at a Singapore-based value investment firm. As a member of the investment committee, he helped the firm’s Asia-focused equity funds significantly outperform the benchmark index. He was previously the portfolio manager for Asia-Pacific equities at Korea’s largest mutual fund company. KB has trained CEOs, entrepreneurs, CFOs, management executives in business strategy, value investing, macroeconomic and industry trends, and detecting accounting frauds in Singapore, HK and China. KB was a faculty (accounting) at SMU teaching accounting courses. KB is currently the Chief Investment Officer at an ASX-listed investment holdings company since September 2015, helping to manage the listed Asian equities investments in the Hidden Champions Fund. Disclaimer: This article is for discussion purposes only and does not constitute an offer, recommendation or solicitation to buy or sell any investments, securities, futures or options. All articles in the website reflect the personal opinions of the writer.

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