Alternative Data and Analytics

Empowering Small Market Financial Services to Compete with the Big Boys

The recent news announcement from AIG, Two Sigma and Hamilton Insurance Group announcing their agreement to form Attune, a technology platform for the commercial insurance market, represents further validation of the critical importance of data science to the future of financial services.

It’s no surprise that Two Sigma, arguably the most data-intensive quantitative hedge fund in the world, will manage the data science process for Attune, given its renowned capabilities in data science. This represents great news for Dun & Bradstreet because quantitative hedge funds are discovering new value from integrating our data into their alpha generating trading algorithms, which reinforces the value of our data in financial services.  

In the eyes of many traders and investment managers, data science has been a place for only the hardcore big-market segment. But deals such as this one signal a move to the next tier of business—one that in some ways stands to gain greater benefit than does the large market. In fact, the small to mid-market segment in financial services often lags behind their larger counterparts because of their limited ability to engage in and manage their risk, which is highly correlated with reward.


Their largest competitors often have the ability to take on greater risk because they are better capitalized and/or better equipped to manage and offset that risk. Hence they reap the benefits of higher returns as a result. Historically, this has placed small to mid-market financial services firms at a disadvantage. However, as technology enables even the smallest of firms to quickly level the playing field with today’s leading competitors, their ability to leverage big data to generate higher-order data observations to identify trends and correlations with market direction—in ways heretofore reserved for big companies—can often close the gap between their firms and the largest competitors. 


These higher-order relationships, based on systemic understanding of data relationships, enable smaller firms to build sophisticated products that measure risk on par with all others. How will this manifest itself to provide benefit to those falling below the top tier? It allows them greater depth of understanding of the risk of all of their underwritings, empowering them to insure opportunities that have greater potential of upside reward.

For instance, a smaller firm may take on a deal that previously would have been too risky to assume because now, with greater understanding of the risk, it can offset that risk with accurate hedging strategies. Alternatively, that same level of analytical insight shines a light on a side of the deal that exposes its capital in a way that exceeds the company’s workable threshold. This can to lead to an improved overall risk profile for the firm by declining to underwrite an opportunity, or at least more accurately value it, so that it carries greater benefit to the firm's portfolio. While this is true for the insurance industry, it extends similarly to capital markets, where accurately evaluating the risk/reward of investment positions is the most important factor to succeed.

The news announcement cites the importance of “disruptive forces of data analytics and the powerful technology supporting it” and promises that “these forces will shape the way we work with our brokers and agents.” This is indeed the tailwind that boosts the insatiable demand for alternative data as well as the growing mandate for more insightful and thorough analytics.