Predictive analysis is the process of applying statistics and modeling techniques to determine future outcomes. It uses current and historical data to reveal what might lie ahead for an organization. 

Increasingly, finance professionals have turned to this form of data analysis to make smarter decisions and improve the accuracy of their short-term and long-term projections.

According to one recent study, 49% of senior executives say the biggest benefit of integrating predictive analytics in financial services is an enhanced decision-making capacity. 

Are you thinking about investing in these tools and technologies? Today, we’re sharing the benefits they can provide and how the financial sphere can leverage them. 

Why Does the Finance Industry Need Predictive Analytics?

Among all the various types of businesses, the finance sector is arguably the most data-dependent. Tomes of data flood financial management and professional services software every day, and much of it is highly sensitive and confidential. 

Traditionally, decision-makers have relied on standard business models and skills to deliver products and services to customers. Yet, the speed of innovation is rapidly accelerating, and clients, partners, and stakeholders are expecting financial institutions to follow suit.

If you’re not already, your company should be thinking about how to use data to your competitive advantage. 

This means referencing past trends and patterns to predict what might happen next. It means looking at ways to integrate predictive analytics into your existing business intelligence (BI) platforms. It means optimizing your analytic data pipelines to enable quick decision-making.

Last, but certainly not least, it means empowering your workforce to take advantage of new analytics tools. 

Software Selection & Process Improvement Case Study

In helping the client get its project back on track, one of our primary focus areas was decreasing their customization needs by improving their processes to align with the system's best practices.

5 Ways to Leverage Predictive Analytics​

1. Making Smarter Decisions​

With predictive analytics, users can forecast and assess potential scenarios based on data that’s readily available. While this data is real-time and hyper-contextualized, finance companies need a way to organize it so it’s useful and logical. 

This is where platforms such as enterprise resource planning (ERP) can help. An ERP system helps centralize incoming data and automates its day-to-day management. This makes it easier for users to integrate data into the analytic data pipeline where employees can access it as necessary. 

In the realm of ERP, you can find predictive analytics tools that encompass the following functions, among others:

  • Data mining
  • Data querying
  • Data reporting
  • Data visualization
  • Statistical analysis
  • Data cleaning and preparation

These processes and methods help finance employees collect, store, and analyze the data they collect through day-to-day business operations. Stakeholders can then review this data and make decisions that benefit the organization.

It’s important to remember that these stakeholders need the ability to efficiently create forecasting models. With the advent of the Internet of Things (IoT), finance companies are handling more data than ever so efficiency is key.

As a result, many companies are turning to advanced predictive analytics and ERP software to augment their existing planning models. 

These sophisticated systems feature other dynamic elements, including:

  • Visualization dashboards 
  • Robotic process automation (RPA)
  • Real-time outcome sensing
  • Agile forecasting

2. Planning for Increases in Demand

Finance companies need to be able to reference historical data to understand fluctuations in earnings over time. With predictive analytics, they can look at past reports as well as their current status and plan accordingly. 

With this advanced insight, industry ebbs and flows are less surprising. Executives can see them coming before they even occur based on what their BI models predict. In response, they can put the right steps in place to mitigate any risks they might incur.

3. Understanding External Drivers

Within any company, there are external factors, like economic conditions, that could affect business outcomes. This is especially true in the finance industry where factors outside of a company’s control could have a direct impact on an organization’s bottom line. 

With predictive analytics, users can model different scenarios and measure the impact of those possibilities. Each scenario includes different drivers and data sources, and each scenario varies in terms of quality and relevance. 

Users can create models that link these drivers to overall business performance. This helps them understand how the elements are connected. They can use this data to establish foolproof, actionable strategies in response to each scenario.

4. Gauging Customer Preferences​

With an ERP system implementation, financial institutions can glean deeper insights into customer behaviors and preferences.

Predictive analytics takes this process a step further. With these tools, users are not only able to understand what their clients are doing now but also how those actions might influence future actions. 

Then, they can use this knowledge to create products and services that cater to this audience. This enhances service levels and improves customer service, leading to a greater number of long-term clients. 

5. Empowering Your Workforce

One of the greatest benefits of predictive analytics tools is that they’re user-friendly. Employees can use customizable dashboards and self-service BI tools to access advanced forecasting models.

Nonetheless, companies should still invest in data literacy training to help their workforce understand how to make the best use of the information at hand. With the right training, employees can act according to the insights they find.

Optimize Predictive Analytics in Financial Services

Predictive analytics is changing the way companies operate, allowing them to automate data collection efforts and make better use of the data that crosses their desks every day. In the finance sector, these insights can be used to predict future company performance, empower employees, and improve customer service. 

An ERP platform with built-in data analytics, including advanced analytics, can serve as a central source of information across your enterprise. Contact our independent ERP consultants below to learn more about the role of predictive analytics in financial services. 

Posts You May Like:

Overcoming Data Quality Issues in Implementing an ERP System

Overcoming Data Quality Issues in Implementing an ERP System

Implementing an enterprise resource planning (ERP) system is an important step for organizations hoping to integrate their processes in a single platform. However, the journey can be fraught with challenges, notably data quality issues that can impact the...