The Future of Rating Agencies and Big Data

“There’s two superpowers in the world today,” said Thomas Friedman. “There’s the United States and the Moody’s Bond Rating Service. And believe me, it’s not clear sometimes who’s more powerful.” Capital markets could not function without rating agencies. As financial firms and bond issuers begin to rely more on real-time analytics and data analyses, rating agencies, on which much of the function of the market depends, must also adopt new technologies and approaches to understanding the market.

The modern credit rating industry has its roots in the mid-19th century: following the financial crisis of 1837, mercantile credit agencies (the precursors to ratings agencies Moody’s and S&P) developed as a way to rate a merchant’s ability to repay his or her debts. By the 1850s, American bankers Lewis Tappan, John Broadstreet, and Robert Dun published ratings guides that were similar to today’s ratings reports. In 1909, John Moody issued his first publication — focusing solely on railroad bonds — which was the first widely accessible ratings guide and the first to establish the now-standard letter grading system, and which later grew into the present-day Moody’s. Along with Moody’s, the antecedents of the ‘Big Three’ (Moody’s, S&P, and Fitch) had all entered the credit ratings industry by the mid-1920s.

As the economy has changed and become increasingly globalized since the early 1970s, the role of credit ratings agencies has changed. As the global economy grew more complex and capital markets expanded, ratings agencies became more essential to the functioning of capital markets.

More recently, ratings agencies (and the industry as a whole) are faced with the challenges posed by the demand for efficient analysis of large volumes of financial data. As transaction volumes in the bond market have increased exponentially, the demand for ratings and the amount of available data has similarly increased. More than 90 per cent of all financial data in existence has been created since 2013, and in 2015 the market for data analytics surpassed $16 billion, up from just over $3 billion in 2010, according to the International Data Corporation. For ratings agencies, the ability not only to analyze the large volumes of data but to do so efficiently, accurately, and in real-time will dictate their future success and competitiveness.

Moving forward, the possibilities of big data are very exciting for boths banks and ratings agencies. It is estimated that by 2018, the value of the big data analytics and services industry will surpass $40 billion. It’s a trend we’ve discussed before at Overbond: as the economy advances technologically, the primary source of value will be less in the infrastructure, and more in user-created data networks, enabled by analytic platforms.

In recent years, rating agencies have placed big data at the centre of their strategy, and are investing heavily in developing their analytic capabilities. In 2015, S&P Global, then called McGraw-Hill (the parent company of Standard & Poor’s) purchased SNL Financial—a data-driven financial information firm, in order to bolster their data analytics offerings in the years ahead. “Both S&P Capital IQ and SNL possess strong and sophisticated content delivery platforms,” wrote S&P Global in their 2015 Annual Report. “The combined team is now determining how to most effectively consolidate into one best-in-breed product platform.”

According to Chris Iervolino, a research director at Gartner, the ability to incorporate analytic tools directly into financial systems allows companies to turn big data into relevant information. “We can look at and understand more information because we have more data,” said Iervolino. “We can use predictive, statistical methods that weren’t useful with less data.” Technologically speaking, the development of more powerful tools able to process volumes of data never before seen in capital markets will allow firms to reduce latency between the completion of a transaction and the ability to analyze it; increasing the speed at which this is done on a large scale will allow firms (including ratings agencies) to derive value from their data more quickly and accurately.



About Vuk Magdelinic:

Before founding Overbond, Vuk’s career spans over 10 years in capital markets and technology. As PwC Risk and Regulatory consulting manager Vuk led large digital transformation programs at Deutsche Bank and BNY Mellon in New York City. Prior to that he worked at CIBC Fixed Income trading floor in Toronto in structured products origination capacity. Vuk has collaborated on numerous publications addressing key trends in fintech innovation. Vuk holds electrical engineering degree from University of Toronto, MBA from Ivey school of business and is an avid abstract painter.