Feb 03 2022
Data Analytics

Predictive Analytics in Finance & How it Can Help Manage Risk

How organizations can use their data to protect themselves against potential losses.

Businesses have been clamoring for ways to incorporate data analytics into their operations for years. The rise of digital products has made it easier than ever to collect information about customers and their behavior, and it’s become increasingly necessary to put that data to work.

Financial institutions are in a particularly powerful position when it comes to data. They have access to a tremendous amount of information about their customers, from their debts and savings to their spending habits and investments. According to CDW’s Scott Hiemstra, the challenge is what comes next.

“What they’re often lacking, however, is a way to convert massive data sets into targeted, actionable recommendations for advisers and call center agents,” Hiemstra wrote in BizTech.

One of the most productive ways organizations can leverage this data is in the area of risk management. Predictive analytics can use the data to generate forward-looking insights that can help determine the risk of a loan or investment. Harnessing that power can help financial institutions make smart moves for both their customers and their organizations.

What Is Predictive Analytics?

Predictive analytics is an aspect of data science that’s growing increasingly popular across industries. At its core, predictive analytics combines data mining, statistical modeling and machine learning to forecast likely outcomes.

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For financial institutions, this kind of forecasting can directly translate to revenue. Models can predict how a stock might perform or how much interest the organization can collect on loans. According to Deloitte's David Cutbill, it plays a crucial role for those financial services navigating tumultuous times. 

“Agile planning, short-term capital allocation, and predictive forecast models collectively foster a more resilient business that can improve business and finance performance now while setting up future success in an ever-changing environment,” Cutbill writes in a blog post

A large part of becoming resilient is being able to properly assess and mitigate risk, and a growing number of financial institutions are using predictive analytics for this purpose.

How Predictive Analytics Can Be Used in Risk Management

Forecasting and risk management aren’t new concepts for financial institutions, which have always accounted for risk when approving loans or making other decisions. The difference now is that risk analysis doesn’t have to be done manually, and that can lead to fewer mistakes and biases while freeing up key resources. 

“Organizations can start by hotwiring traditional planning and forecasting processes and leveraging more predictive analytics and data technology for data consolidation, planning, budgeting, and scenario assessment,” writes Cutbill. “Better risk mitigation with scenario analysis capabilities can identify the degree of influence of inputs and outputs and lessen the impact of management biases on decisions and execution.”

For financial services, risk management is particularly critical. There are compliance standards that must be met, and the sensitive nature of the business means that there is more potential backlash from customers if a mistake is made. Predictive analytics can help inform sound investment decisions, detect fraud and provide accurate projections for revenue.

MORE FOR FINANCIAL SERVICES: Explore the tech trends transforming the industry in 2022.

There are additional benefits to customer operations as well. For example, organizations can use forecasting to assess whether someone is a good candidate for a loan, shortening what can otherwise be a tedious process. Leaving the decision up to a model can also eliminate potential bias that can arise when these procedures are done manually.

What Infrastructure Is Needed to Use Predictive Analytics

Like most data-driven tools, predictive analytics must be built on a strong foundation of data infrastructure. Artificial intelligence and machine learning solutions are key to predictive forecasting, and the organization’s data center must be able to support those tools. 

Organizations also need to have multiple inputs available when building these models. This can be difficult if data is siloed throughout the IT environment. Integration and visibility are key to success with predictive analytics, and cloud solutions can often provide both.

This can present a challenge for financial services, because there are additional compliance concerns surrounding data center infrastructure. According to FinTech Futures, 39 percent of IT leaders interviewed said that complying with regulatory standards would prevent them from using predictive modeling or predictive analytics solutions in their organizations.

Financial institutions also face different circumstances in the cloud, but there are many ways organizations can tackle their cloud environment to ensure they reap the benefits of next-generation data solutions while obeying regulators.

Data analytics isn’t just for examining the past — it also can help organizations build toward a better future. For financial institutions batting back risk every day, predictive analytics can unlock a more stable and productive operation.

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