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 by machine learning and big data. From personalized banking to machine learning-based stock market forecasting, predictive analytics in finance has a multitude of applications.
Let’s take a look at how predictive analytics experts help corporate finance teams, investment banks, and other financial organizations incorporate advanced analytics into their business workflows to make the future as tangible as possible.
What is predictive analytics in finance?
Predictive analytics is a digital process involving the interpretation of financial data from descriptive and diagnostic analysis to calculate the possibilities of future outcomes.
In reality, the process of analyzing historical data for trying to predict the future is not new to the finance sector. Banks, other financial institutions, and businesses in general have always tried to estimate probabilities by interpreting past events.
The value of predictive analytics lies in making this task significantly quicker to perform as well as ensuring far greater precision of the forecasts. Due to the modern improvements in predictive analytics’ speed and accuracy, financial companies can increase the scope of its implementation and apply the technology more broadly across strategic and tactical areas of their business.
According to Statista, the global market for predictive analytics is forecasted to grow to $41.52 billion by 2028.
Top 3 predictive analytics models in finance
In the finance context, these are the three most widely used predictive models:
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Classification model The classification model is among the most straightforward predictive analytics models that produce a binary output. In the banking context, classification models are often used to guide decisions based on a broad assessment of the subject. For example, it can predict whether the shares of a certain company will go up or down. |
Outliers model The outliers model is used to detect significant deviations in a dataset, making it one of the most widely-used models for fraud detection. For example, if a customer’s credit card is used to buy an overly expensive watch in a city that he doesn't live in, the outliers model will flag this transaction as potentially fraudulent on the grounds of this being an unusual behavior. |
Time series model The time series model tracks a certain variable throughout a specific time period to predict how that variable will be affected at another specific time frame. For example, in finance, the time series model is often used to predict how a given financial asset (like a security’s price or inflation ratio) will change over time. |
The 6 benefits of predictive analytics in finance
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Decreased costs |
By integrating predictive analytics into budget building and risk modeling, financial companies can have better insight into daily cash flows and increase cost-effectiveness of their operations. |
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Minimized risks |
Predictive analytics help financial institutions model certain economic scenarios and make evidence-based decisions that minimize risks.  |
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Decreased fraud |
Machine learning and advanced statistical models allow organizations to process large amounts of data in real time and detect fraud more accurately. |
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Personalized services |
By analyzing large amounts of customer data, companies can better understand customer profiles, deliver personalization at scale, and increase customer engagement. |
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Increased revenues |
Machine learning-enabled predictive models allow investment professionals to make data-driven and more profitable decisions about the market. |
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6 |
Increased employee satisfaction |
Predictive analytics take care of manual work, allowing staff to focus on more engaging and value adding tasks, which increases overall employee satisfaction and productivity. |
6 use cases of predictive analytics in finance
1. Cutting the costs of budget building
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, predictive analytics software solutions combined with custom accounting tools with automation capabilities are well-suited for it. By analyzing historical data to highlight patterns and predict costs within 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 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.
Example: PwC
For example, PwC, a second-largest professional services network, helped a large financial institution to better predict daily cash flow and increase return on payment activities. By incorporating a predictive model and a data visualization tool in the budget forecasting framework, the financial institution was able to much better understand how different events impact cash flows. As a result, the client was able to extend their forecast period from 3 to 12 months, free employees’ time for more value-adding tasks, and make more accurate budgeting decisions.
2. Reducing prediction risks in investment banking
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 determine valuations based on that information.
For investors, corporate bonds are arguably a safer option 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 won’t get a reasonable price for the bonds.
Example: Overbond
Overbond is a Canadian startup that provides investment bankers and their clients with an AI quantitative analytics services that helps 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 gauge the dates of new issuances and prepare to get in on the buying action.
3. Providing political risk signals for hedge fund managers
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.
Example: Predata
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 a political 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:
- Election results
- Labor strikes
- National security risks
- Protests and boycotts
Since its launch, Predata has partnered with another company to incorporate geospatial technology into its product. The software is helping hedge funds to stay abreast of events and their possible outcomes even before mainstream media sources begin to report on their emergence.
4. Fraud prevention
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 UK Finance, unauthorised financial fraud losses across payment cards, remote banking and cheques totalled £783.8 million in 2020 in the UK alone. At the same time, billions of dollars in legitimate purchase attempts continue to be falsely rejected by fraud detection systems.
Of late, though, several solutions based on predictive analytics and machine learning have been deployed to supersede the limitations of rules-based fraud detection applications. You can delve into this topic in our article on machine learning-powered fraud detection. However, consider the following scenario as an example of their value.
Of course, the successful use of predictive analytics in fraud prevention is not just about reducing the false positives, but also increasing the successful interception of genuinely fraudulent transactions.
Example: DataVisor
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 improved successful interceptions of fraud attempts in online loan applications by 30% and achieved a false-positive rate of just 1.3%.
5. Managing credit card default risk
Traditional credit scoring systems take into account many factors including payment history, length of credit history, number of credit inquiries, and many other metrics. However, when there is a lack of conventional financial data, such systems become powerless. Machine learning and advanced predictive analytics methods, in their turn, allow banks to more accurately assess customers' creditworthiness and draw insights from significantly wider datasets.
Example: Carbon
Carbon, a digital bank for the underserved African market, struggled to assess the credit risk of citizens without a prior credit history. To tackle this issue, the bank turned to DataRobot, a company that delivers AI- and cloud-based solutions. DataRobot’s AI Cloud platform allows Carbon to automatically assess the creditworthiness of individual customers within five minutes.
The machine learning-based system gathers data from first-, second-, and third-party sources, and builds a credit score, allowing customers with higher scores to gain access to better rates. On top of that, the system also estimates the default probability for each customer, which is then used to adjust lending terms.
Apart from the credit decision engine, the bank also adopted a churn model that spots customers that are most likely to stop using Carbon’s services, allowing the bank to take preventive measures and retain them.
As a result, the Head of Data Science at Carbon reported that the team would have needed 25% more team members to do the same amount of work. Now Carbon's team can allocate freed employees' time to perform more strategic work.
6. Optimizing customer journeys
In the current era of banking, the competitiveness of a financial institution largely correlates to its ability to provide personalized customer experiences. Drawing on the enormous amounts of data that users generate online, banks can stand out from the competition by tailoring services down to an individual customer, as is the case with AI-enabled wealth management many banks now employ. However, hyper-personalizing customer experiences at scale call for machine learning-based predictive analytics and more granular data.
Example: Teradata
Teradata Vantage is an intelligent multi-cloud data platform that streamlines data analytics for large enterprises. During its digital transformation, a division of a large, multinational bank recognized that they need to increase the depth of understanding of their customers to maximize customer acquisition, spot stalling points in the customer journeys, and increase customer engagement, and adopted Teradata Vantage.
By introducing new digital variables like page scores and visit durations, the bank can now identify customers with a high level of interest prior to starting an application. Based on customers’ past transactions and interactions with all digital channels, the bank targets those high-value prospects with increasingly personalized messages. As a result of the adoption of Teradata's system, the bank increased the click-through rate of personalized messages by 50 times.Â
Tips on implementing predictive analytics in finance
To reap the most benefits of predictive analytics, the majority of financial institutions need to make substantial organizational changes. The transformation should be approached holistically, ensuring that technical base, organizational structure, and staff are on the same page.Â
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Enhance data governance With established data governance standards in place, data science teams have a much better chance of making predictive models that bring tangible benefits. While financial firms often have an abundance of data on their hands, its poor quality or inaccessibility minimizes its potential. Taking the time to establish a system where data is cleaned, structured, and consolidated in one place is one of the biggest prerequisites for the successful implementation of predictive analytics. |
Improve the technological base The overarching inefficiencies commonly associated with legacy systems are especially relevant in the financial industry. Banks’ old monolithic architectures weren’t built for loads of data that a modern organization is expected to handle, and proved to be largely inflexible in terms of adopting new technologies. Predictive analytics or any other technology that relies on data to produce tangible business outcomes benefits from modular infrastructures, where technologies can be easily removed or added. |
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Reimagine the organizational structure The adoption of advanced analytics strategies often calls for the reorganization of team structures and workflows. While financial institutions have similar end goals, the ways in which predictive analytics integrates into workflows are different from company to company. Thus, to embed predictive analytics, data science teams, IT departments, and business units need to work effectively together to develop actionable data-driven strategies. |
Instill a cultural change In general, data-driven digital transformations require a shift in organizational mindsets. Conventional training programs with a linear approach to learning can be a good start, but organizations with cultures that embrace continuous self-learning have a higher chance of squeezing the most out of predictive analytics. Decentralized decision-making coupled with engaged and data-literate staff can accelerate the adoption of new use cases and help business users derive insights from models quicker. |
A big future for predictive analytics in finance
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, similar to the applications of predictive analytics in real estate, 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.
