Predictive analytics in finance (Future of finance)

Predictive Analytics In Finance

Predictive analytics is used by businesses all over the world to produce predictions or model forecasts that employ data analytics, automation, and machine learning to make business decisions.

Data is usually run through a series of predictive models and algorithms, allowing organizations to track consumer behavior and business outcomes. These models can then identify patterns and trends in data that are more insightful and deeper in analysis than simple visual data depiction. This information enables businesses to model the future for business planning and growth.

In this blog post, we specifically discuss how predictive analytics is used in finance, so you get a better understanding of how advanced analytics can aid companies in taking important financial decisions.

What is predictive modeling in finance?

How predictive analytics work

Predictive modeling in finance is the application of statistical models to financial data to make predictions about future events. A predictive model will enable your company to generate multiple economic scenarios, providing you with all the facts you need to make data-informed decisions that are as risk-free as possible. This can be used to make investment decisions, forecast cash flow, and get real-time data updates.

How finance teams use predictive analytics in different areas

For predictive analytics, different methods can be used, but the best one will depend on the type of data being looked at and the goals of the analysis.
 
Let’s take some examples of predictive analytics in different sectors:

Predictive analytics in corporate finance

The financial records of your business should be reviewed periodically. Predictive analytics can help forecast your organization’s future and help the finance teams make more informed decisions about the following:
 
  • Investment opportunities
  • Risk management
  • Pricing strategies
  • Helping finance leaders with data visualization

Predictive analytics in healthcare finance

Predictive analytics in healthcare is based on big data and artificial intelligence. Data originating from electronic health records (EHR), insurance claims, administrative paperwork, medical imaging, etc., is aggregated and processed to identify patterns. Healthcare providers can figure out the following things from the predictive analysis:
  • How likely are certain diseases to develop in patients?
  • How will different treatments affect them?
  • Will they be able to attend their next appointment?
  • What is the likelihood that they will return within 30 days of discharge?

Benefits of predictive analytics in healthcare finance

Predictive analytics can benefit healthcare leaders in the following ways:
  • Reduce costs associated with no-show appointments and readmission penalties.
  • Automating administrative tasks such as discharge procedures and insurance claims submission.
  • Ransomware and other cyberattacks can be prevented by analyzing ongoing transactions and assigning risk scores.

Predictive analytics in auto finance

Predictive analytics in auto finance depends on data collected from various sources, including OEM data, credit bureau data, and telematics data.
 
This data is used to develop models that can predict:
  • Auto loan default rates
  • Loss severity in the event of default
  • Auto insurance fraud
With predictive analytics, auto lenders can make better decisions about:
  • Loan approval and pricing
  • Risk management
  • Fraud prevention

How financial services can invest in the future with predictive analytics

There is a flood of data in the financial services industry. This is the industry that gathers the most information about its customers out of all others. It’s also one of the industries poised for big changes as new business models and skills are needed to drive the evolution of services and products.
 
Even though the financial industry has access to all this information, it struggles to make the best use of its data. Almost half (55%) of UK employees working in financial services believe their company uses data effectively to gain a competitive advantage.
 
Why do financial services organizations hesitate to adopt the latest innovations in data and analytics?
 
Lack of trust and regulatory risks!
 
Understandably, trust and regulation pose challenges. Nonetheless, if financial organizations are to make full use of the data they possess, they must overcome these obstacles. Below are two key factors to consider:

Analyze your data pipeline first

Once you achieve this, you’ll be able to move from just operating in a passive mode of data consumption to a state of active intelligence. However, the pipeline must be robust for this to be possible. In that case, it is impossible to trust – and this is the primary concern – that action will be taken based on the correct information.
 
The problem is that many companies are having trouble with this. The data is not being integrated into the pipeline in a reliable enough state to feed the predictive analytics programs. This leads to concerns regarding its quality, privacy, and speed of integration.
 
“The data itself isn’t what matters; it’s what you do with it,” said Nick Blewden, Lloyds of London. To achieve business-ready, trusted insights, investing in the entire process is critical.

Make your people feel empowered

To challenge the data’s output, employees must be confident about their understanding of it. This is especially true about decisions that might directly impact customers’ lives, such as approving an overdraft and getting to payday or approving a mortgage application on time.
 
As a result, it is also important that customers and other stakeholders understand how and why specific decisions were made, which is achievable using predictive analytics.

Why automated predictive analysis is the future of financial services

Predictive analytics has the potential to completely change how financial services are delivered. It can provide insights that help businesses make automated processes and accurate decisions.
 
For instance, banks can use predictive analytics to automatically:
  • Approve or decline loan applications
  • Detect and prevent fraud
  • Make recommendations about products and services
These are a few examples of how predictive analytics can be used in financial services. The possibilities are virtually endless.
 
What’s essential is that predictive analytics is not just a pipe dream; it’s a reality. And it will only become more commonplace in the years to come.
 
If you work in financial services, now is the time to start getting familiar with predictive analytics and how it can be used in your business. It’s an investment worth making.

Conclusion

The world of finance is complex and ever-changing. To stay ahead of the competition, it’s essential to utilize all the tools and methods, including predictive analytics.

 
Enterprise Performance Management (EPM) systems can aid you with financial planning and complex predictive analysis. Its powerful analytics ability can reveal a better future for your business as it shows you the areas needing improvement.
 
In InnoVyne, we can guide you to more accurate forecasts and data-based decisions by customizing an EPM solution based on your unique challenges and needs. We make sure to support you in each step of your journey with our implementation, training, and managed services.
Role of big data analytics in the financial sector
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Correlating data to drive success

The whitepaper explores the role of big data analytics in the financial sector, and examines how organizations can use it to prepare for increased competition, reduce profitability risk and improve market insight.

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