Source: The Financial Revolutionist
When the big data boom grabbed headlines in the early 2000’s, it was because the high-tech industry allocated R&D efforts to develop new data-driven applications for their businesses. Out of these developments came the rise of predictive analytics.
It should be noted that predictive analytics represents a different methodology from the more well known approach of descriptive analytics, in which a “looking back approach” is used to make future decisions. With predictive analytics, previously acquired historical data is used in combination with machine learning to predict the likelihood of future action, allowing for a more robust approach to assessing probabilities.
As a testament to the power of predictive analytics in finance, total investment in the industry has skyrocketed over recent years. In 2014, for example, Goldman Sachs led a $15 million round for big data analytics start-up, Kensho, joining firms like Google Ventures, CNBC, Accel, NEA and Fidelity Investments. Why all those big names? Quite simply, Kensho’s predictive analytics capabilities have enabled leading financial institutions to drastically shorten processing times for analyzing the impact of global market scenarios on various asset classes. As a result of the reduced resources needed to analyze once-complex transactions, large financial firms are able to cut costs dramatically while reducing inefficiencies and automating decisions that would otherwise be vulnerable to human error.
In 2015, predictive analytics firm Blue Yonder secured the backing of global private equity firm Warburg Pincus to accelerate the company’s ability to enable its clients to harness the predictive power of their data. Not only does the platform provide insight, but its statistical models also enable automated decisioning, which streamlines the entire decision-making process within organizations.
Today, the predictive intelligence industry is showing no signs of slowing down. In fact, Transparency Market Research reported that the market for predictive analytics software will reach $6.5 billion by 2019. That’s because the ability of predictive intelligence to increase accuracy in decision-making, and subsequently profitability, is showing indications of significant success. Unsurprisingly, innovators developing applications within financial services are growing bolder in their efforts to perform tasks that may have seemed impossible just a few years ago.
As you might expect, hedge funds are at the forefront of using artificial intelligence in predictive applications. A leader in this burgeoning field is Bridgewater Associates, which is designing software intended to automate the day-to-day management of the firm, including investment activities and other strategic decision-making. Similar to the previous examples referenced, the implementation of this project seeks to eliminate emotion and other biases as factors in the decision-making process.
Finally, predictive analytics can be applied purposefully within bond market transactions. For example, our team at Overbond, a digital primary bond issuance platform, uses predictive analytics to make better informed decisions within primary bond issuance. Through the use of big data technology and machine learning, algorithms are constructed to take into account the current state of the market and identify similar transactions that may be of interest to investors. An analysis of the probability of spread-tightening and the likelihood of an issuer coming to market are also provided to investors, many of whom are eager for more real-time insights.
It is clear that the big data boom, combined with machine learning, has paved the way for a renaissance in predictive analytics within the financial services industry. Predicting future outcomes with much greater accuracy than the more traditional, descriptive analytics approach is now possible. As such, the time is now for your financial services organization to come on board.
Our article, featured in The Financial Revolutionist, discusses the rise in popularity of predictive analytics in financial services. Predictive analytics, which uses previously acquired data used in combination with machine learning to predict the likelihood of future action, allows for a more robust approach to assessing probabilities. This article will cover the following topics:
