2018 is poised to further revolutionize the way companies use Big Data to improve their financial bottom lines. The term Big Data generally refers to a dataset that is too large to be efficiently administered and evaluated by a traditional relational database. Its meaning has recently expanded, however, to include advanced technologies that can store, process, and analyze these large collections of data to solve complicated functions.
As the prevalence and scope of Big Data have grown, an increasing number of firms are hiring data scientists to understand, categorize, and process it in new and innovative ways. Firms spent an estimated $36 billion on cloud data storage and software in 2017, and this figure is expected to double by 2020. Firms are upping their investments in Big Data infrastructure to boost cost efficiencies, but how are its benefits realized?
Dashboards Enhance Decision-Making
Data scientists use Big Data to address a wide range of logistical problems. To a large degree, the value of using Big Data is a function of its dashboard functionality. Dashboards simplify complex data and allows managers to track significant data points and performance metrics. Interactive dashboards make it possible to “drill down” into preset data points in a report or presentation and further explore the underlying information that would not be available in traditional reports. This capability facilitates decision making.
Big Data analytics can be used to predict future revenue streams, demand for consumer products or services, and any number of other metrics. Efficiently conveying accurate forecasting via well-conceived dashboards enables firms to appropriately allocate resources to stay in line with market trends.
Big Data analytics functions to introduce fundamentally new information to current prediction and decision-making processes. This newly mined information helps fill gaps in existing predictive decision-making models when data is insufficient to provide accurate estimates. This in turn contributes to lower organizational risk and better managerial decisions.
Big Data’s ability to perform “predictive maintenance” has particular value to the mortgage industry. It facilitates and improves the reliability of risk management tools by enhancing their ability to forecast credit risk, prepayments, and defaults. Predictive maintenance applications can help a mortgage originator forecast staffing needs to accommodate future volume expectations or to innovate new products in line with future market trends. It is interesting to contemplate whether things may have played out differently if mortgage industry participants had employed predictive maintenance in the years leading up to the 2008 financial crisis.
Machine learning models are increasingly capable of leveraging Big Data to predict future outcomes for things ranging from home values to prepayment and default curves. This allows mortgage investors and servicers to make more realistic expectations as to how their investments will perform and the yield they can expect to receive based on the assumptions made and the multi-dimensional loan-level data that is increasingly available. Big Data holds particular promise for the structured finance industry as analytical models and software continue to be developed. These tools use extrapolated values to predict delinquencies and foreclosure rates and calculate default risk and credit risk on underlying collateral loans in mortgage-backed securities.
Ultimately, the goal of machine learning applications for structured products is to be able to configure collateralized deals as quickly and seamlessly as Amazon’s Alexa human-speaking digital assistant can give an accurate real-time weather forecast. The increasingly widespread application of machine learning is also used for enhancing cybersecurity, improving risk management, and preventing fraudulent transactions in the retail and banking sectors.
Predicting Bond Markets
While artificial intelligence and machine learning applications are becoming more integrated in our day-to-day lives, the impact of predictive Big Data machine learning models can also be found in bond markets. Bond prices reflect a complex set of market interactions and policies, which makes predicting them an extremely difficult exercise. Relevant information and market color can be scarce and figuring out what it means can be a challenge even when it’s available. Timing is also an issue since market conditions are continually evolving. Therefore, predictive model algorithms created using Big Data analytics need to be both accurate and time efficient in order to provide value. The goal is to make accurate, real-time bond pricing predictions that will reflect current market conditions based on Big Data sources and machine-learning techniques and systems.
The bond market is a demonstrably powerful prognosticator of future economic activity and inflation levels, both of which have a significant impact on the prices of equities, real estate, and consumer goods. Consequently, once Big Data models are able to effectively predict future bond prices and yields, they will, by extension, be better able to hypothesize what the future economic environment will look like and inform strategic planning. The United States yield curve has been historically flat in recent months, but the expectation is that the Federal Reserve will continue to raise the federal funds rate in 2018. The ability of big data analytics and machine learning models to predict future bond prices and yield curves will also allow us to more accurately predict future interest rates, home prices, levels of inflation, and economic activity.
Advanced technology is constantly progressing, and the phenomenon of Big Data and its limitless applications is at the forefront of this new evolution. Through the use of advanced artificial intelligence applications, Big Data continues to enhance the way that structured finance deals are designed, marketed, and priced. Companies are continuing to invest heavily in future applications to improve their own efficiencies and profit margins via interactive dashboards, predictive maintenance processes, and decision-making tools. Being able to quickly and accurately gather information to forecast future bond prices will allow experts to efficiently predict future interest rates, levels of inflation, and economic activity. The newest technological advances are continually arriving all around us. Virtually all of them are making use of Big Data, its applications and capabilities.