Accurate prediction of the future has long been a coveted ability. Throughout human history, people have sought it, applying every possible resource and discipline to its attainment, from the scientific to the supernatural.
At last, it seems we’re as close as we’re ever going to get to future insight, aided not by crystal balls but big data. Predictive analytics is coming into its own, and nowhere is it more welcome than in the world of finance and investment.
Let’s take a look at how corporate finance teams, investment banks, and other financially focused organizations can, do and will continue to incorporate predictive analytics into their business practices.
Predictive analytics is a digital process involving the interpretation of data from descriptive and diagnostic analysis to calculate the possibilities of future outcomes. As such, it is not a standalone solution. It depends and expands upon the intelligence provided by descriptive and diagnostics analytics, the more mature cornerstones of digital business intelligence.
In reality, the process of analyzing historical data to try and predict the future is not new to the finance sector. Banks, other financial institutions, and businesses in general have always tried to identify and measure probabilities by evaluating and understanding past events.
The value of predictive analytics is that it makes this task infinitely quicker to perform as well as adds far greater precision to forecasts. With speed and accuracy comes the ability to increase the scope of predictive efforts and apply them more broadly across strategic and tactical areas of business practice.
Perhaps your organization has been considering possible applications for predictive analytics, but you’re not sure where it can deliver the most promising return on investment. If so, you’re not alone.
When McKinsey conducted a global survey into AI adoption in 2018, the lack of clear strategies for applications such as AI-driven predictive analytics was the most-cited adoption barrier. Close to half of the respondents (43%) declared it as a primary challenge.
Perhaps some pointers will be of benefit, so the following sections of this article highlight a few predictive analytics use cases. You might wish to consider the advantages that each might bring to your financial services enterprise or the finance department of your company.
Before exploring how predictive analytics can help enterprises and institutions in the financial services industry, it’s worth taking a moment to look at its potential value to any business team with economic interests. You don’t need to be in banking, after all, to benefit from glimpses into the future of financial performance.
Finance and accounting departments in any industry have every reason to leverage advanced predictive analytics for the following purposes:
The above are all typical routine tasks and processes that financial teams perform regularly or periodically. With the application of predictive analytics, financial specialists can execute them more accurately and with a reduced level of labor required.
The annual round of budget-building is a prime example of how companies can benefit from advances in financial software development, including predictive analytics.
Building a budget may not be a high-risk activity in comparison with some that will be discussed later in this article. However, it typically ties up a company’s accounting department for weeks, perhaps even months, toward the end of every financial year.
As setting budgets for hundreds-of-line items is an activity that's dependent on cost prediction, it's one for which predictive analytics software is eminently well-suited. By analyzing historical data to highlight patterns and predict costs in a few moments, a predictive analytics solution can create routine budgets without consuming the efforts of an entire accounting team.
Better still, because predictive analytics can answer “what if” questions, accountants can take a routine budget and test different strategies and scenarios, in order to implement new budgeting approaches. All in all, with the application of predictive analytics the annual budgeting process can be faster, more accurate, and completed at a lower cost than ever before.
For some enterprises, such as investment banks, finance is not a peripheral function but a core competency requiring every decision to be made with an eye on the future. Naturally, this type of business is fraught with risk, and accurate forecasts of financial performance are essential—and typically focused on factors external to the organization itself.
For example, two primary roles of investment banks are to help companies manage mergers and acquisitions and to serve as intermediaries in the release of stocks, shares, and other securities. In both cases, the bank must rely on insights and predictions about its client and to determine valuations based on that information.
Using predictive analytics, an investment bank can co-mingle proprietary data from its internal databases, those of the client, and from external information sources to drive predictions relating to future performance. These predictions, in turn, can help investors decide how much to pay for bulk securities purchases, and how much to resell them for, to profit from an IPO.
Similarly, when advising clients on M&A opportunities, investment bankers can take advantage of predictive analytics to pick out patterns and trends in the performance of potential targets. This intelligence can help them determine if a target enterprise is a good fit for the acquiring organization, and to highlight the pros and cons of acquisition.
For investors, corporate bonds are arguably a safer proposition than stocks and shares, since the rewards are not linked directly to profit and loss. Furthermore, in the event of a corporate financial crisis leading to bankruptcy, bond-holders are at the front of the queue for reimbursement.
Nevertheless, for investment banks that support companies with bond issuance, plenty of risks exist. A potential hazard, for example, is that incorrect sensing of demand—always possible in manual forecasting—can mean the bank fails to get a reasonable price for the bonds.
Overbond is a Canadian startup now breaking into the US market. It provides investment bankers and their clients on the demand- and supply-side with a by-the-numbers tool to eliminate human fallibility from the business of issuing, selling and buying bonds.
Overbond’s algorithms, powered by neural networks, can predict the pricing and timing of new bond issues, with a reported accuracy of within 0.02% on yield predictions. They do so by analyzing real-time secondary-trading data for companies and their peers, along with credit ratings and data from corporate balance sheets.
For investment bankers and their clients, these capabilities can provide insights for guiding the timing, pricing and maturity decisions required for corporate bond issues. For investing creditors, a free-to-use limited version of the tool helps them gauge the dates of new issuances and prepare to get in on the buying action.
For hedge funds, political events and the likelihood of their emergence have traditionally been challenging to predict and mitigate. While geopolitics may not heavily influence broad market movements, it can have a critical impact on investment assets sensitive to government actions.
A New York-based startup received $3.25 million in venture capital to create a platform for hedge fund managers to identify potential political events and forecast their effects on sensitive strategies. The company, Predata, is enabling predictions of events up to 90 days out through analysis of signals arising from social media activity.
By monitoring and breaking down digital conversations, the Predata predictive platform can alert managers about the likelihood of an event and in many cases provide insights into its probable outcome.
Hedge funds can use the application to receive advance notifications of events such as:
Since its launch, Predata has partnered with another company to incorporate geospatial data into its algorithms. The software is helping hedge funds to become aware of events and their possible outcomes even before mainstream media sources begin to report on their emergence.
In addition to letting business accounting teams, investment bankers, and hedge fund managers glimpse the future for performance improvement, predictive analytics technology is finding favor among retail banks. Use cases have been identified and are being realized in several areas, including fraud prevention, where interest among financial organizations is increasing steadily, as the below chart illustrates.
Ever since credit cards emerged as a method of consumer funding, card issuers have been trying to identify fraudulent transactions on the fly, yet with limited success. According to the 2019 Nilson Report, card fraud losses in the United States alone amounted to $9.47 billion in 2018. At the same time, billions of dollars in legitimate purchase attempts continue to be falsely rejected by fraud detection systems.
Of late, though, several predictive analytics solutions have been deployed to supersede the limitations of rules-based fraud detection applications. While a complete exploration of their capabilities would require an article in itself, consider the following scenario as an example of their value.
A bank's rules-based solution flags a customer's purchase attempt as suspicious, simply because the seller uses a third-party payment processing system. The system declines the card payment. The merchant loses a sale, the bank loses revenue from the transaction, and the customer might stop using that card for future purchases, leading to further losses for the issuer.
If the bank upgrades its fraud detection tool to one using predictive analytics, the solution could, if trained correctly, cross-check the transaction against the third-party payment system's established processing baseline. It could then recognize if the purchase is legitimate and allow it to proceed.
Of course, the successful use of predictive analytics in fraud prevention is not just about reducing the false positives, but also increasing successful interception of genuinely fraudulent transactions.
DataVisor is one example of a fraud-detection engine that does that successfully. Its vendor claims the software, through its predictive capabilities, can accurately assess the likelihood of fraud across a range of transaction types, from card purchases to loan applications.
After being deployed by one of the largest banks in the United States, DataVisor raised successful interceptions of fraud attempts in online loan applications by 30%. Their false-positive rate was just 1.3%.
The Danish financial institution Danske Bank used a predictive analytics solution by Teradata to raise accurate fraud detection by 50% with the help of artificial intelligence. Even more impressive is the claimed reduction of false positives by 60%, with expectations of further increasing this result to 80% as the algorithm continues to mature.
Before closing out this summary of predictive analytics use cases in finance, it’s worth urging the need for caution in any procurement decision. Many of the platforms currently available require a deal of initial input from data scientists and banking experts, and should not be considered as surefire solutions out of the box.
It will be wise to allow for the engagement of a consulting and development partner during the implementation phase of any new predictive analytics application.
As time goes on and solutions mature, though, the business of predicting the future in finance may become less dependent on humans, and adoption might become a less complicated matter. In the meantime, it would be unwise to overlook the possibilities of digital prediction, or to dismiss the potential returns offered, even given the need for substantial initial investments.