Leveraging Predictive Analytics For Better Financial Services

By leveraging digital products, companies can collect more information about their customers and behaviors than ever before – by Madhu Kesavan

 
With the use of predictive analytics, banks can mitigate the risks and streamline the entire financing process. For example, one might review prior performance while forecasting revenue-generating trends, understanding client behaviour, and using the information to provide better products and services. Using such authority can assist financial institutions in making wise decisions that benefit both their consumers and their organizations.

One of the current trends that are fast gaining traction in the financial world is the use of automated predictive analytics solutions in their software infrastructure. The reasons behind this are self-evident. According to Gartner, only 16% of decision-makers claim to be able to easily exploit financial data for decision-making.

As a result, most financial institutions miss out on the opportunity to leverage this important source for corporate growth. Some businesses have discovered a solution by leveraging the potential of predictive analytics for financial services. This article discusses what predictive analytics is and how it can help financial institutions enhance their financial services, improve customer service, and therefore increase revenue. Let us examine some of the processes that can be aided:
 

Why should you choose predictive analysis?

 
Predictive Analytics is a significant step forward because it goes beyond typical analytics approaches. Predictive analytics has the potential to transform practically any industry. It’s especially valuable for financial organizations like banks and fintech firms because it allows them to obtain access to clients’ subconscious behavior.

This includes their purchasing and saving habits, as well as their social routines. They can utilize this information to personalize client service and customize items based on their interests. To harness its potential, an increasing number of firms are incorporating it into their day-to-day operations. By analyzing trends in data, predictive analytics can help firms and investors anticipate future events and reallocate resources to capitalize on them.
 

Predictive Analytics For Finance Sector

 
Many financial institutions choose financial software development to design their decision-making tools that are tailored to their business requirements. Here are some of the most popular ways predictive analytics can improve financial services.

 

• Fraud detection

 

Predictive analytics solutions can assist firms in quickly detecting and preventing aberrant behavior. With AI and ML, it can swiftly collect and analyze data from many firm consumers and efficiently detect any financial behavior aberrations.

For example, suppose the system determines that the transaction is too large or originated from an odd consumer location. In that case, it will halt the transfer and ask the customer to validate their identity and actions.

 

• Credit evaluation

 

A lender is always taking a risk when crediting a customer, let alone a corporation. Creditors may immediately obtain the applicant’s credit history, credit score, and other information using real-time predictive analytics. Based on these criteria, the analytics tools calculate the borrower’s risk score and determine whether or not the borrower can make timely payments.

 

• Marketing

 

Predictive analytics technology has long been utilized for greater marketing and service personalization by well-known firms such as Netflix, YouTube, Meta, Tinder, and many others. Many financial institutions now employ predictive analytics to gain a better understanding of their client’s behavior, demographics, and preferences. 

 

• Demand forecasting

 

Predictive analytics software can easily forecast events that will occur shortly. Finance-related firms can estimate sales and strong product or service demand or drop in popularity in this manner. Businesses can better address client pain points and improve profits by tailoring their offerings to these trends.

 

• Transactional analysis

 

A large-scale transactional analysis can be used to enable financial institutions to personalize marketing to specific customers by identifying transactional behaviors that may indicate a particular life event. Customers with transactional behavior may be in the market for a new car loan, tuition assistance for college, retirement investments, or mortgage refinancing. Banking companies can utilize this information to target their sales and marketing efforts at the right time to the right customer. 
 

What infrastructure is required to use predictive analytics in the finance sector?

 
When developing these models, organizations must also have a variety of inputs available. This might be challenging if data is siloed across the IT system. Integration and visibility are critical for predictive analytics success, and cloud solutions may frequently provide both.

This can be difficult for financial services since data center infrastructure raises extra regulatory problems. In the cloud, financial institutions face unique challenges, but businesses can take numerous steps to ensure compliance with regulators while benefiting from next-generation data solutions.

Data analytics isn’t just for looking back; it can also help firms plan for the future. Predictive analytics can unleash a more reliable and profitable operation for financial institutions that face risk every day.
 

Benefits of predictive analysis in the finance industry

 
Here are the top six advantages that financial institutions of any size can gain from utilizing real-time predictive analytics technology:

 
• Planning future events with certainty – Analytical tools can provide a more comprehensive picture of the company’s development as well as market trends in the exchange, commodity, futures and options, currency exchange, and other markets. Using this data, businesses can more accurately address their current issues and define their further course of action.

 

 
• Task automation – Many analytics tasks that were previously performed manually can now be automated using predictive analytics tools. By automating routine tasks such as reporting, controlling, transaction processing, and many others, businesses can reduce their employee workloads. Instead, businesses can delegate higher-value tasks to their employees, such as strategy and planning, service expansion, decision support, etc.

 

 
• Personalization of services – Customers are more loyal to companies that understand their needs. Companies can improve and personalize their customer services by utilizing advanced analytics tools.

 

 
• Risk reduction – Using customer history data, financial institutions can accurately estimate the risks associated with crediting or project financing. Aside from customer feedback, businesses can collect internal company data to estimate their performance.

 

 
• Improved fraud protection – Predictive analytics can detect any unusual behavior in the system and prevent fraud attempts in real-time.

 

 
• Product enhancement – Analytical tools can thoroughly analyze the product and present it to the owners from a new perspective. Businesses can improve their product appearance, distribution, and pricing in this manner.

 

How to Use Predictive Analytics in the Financial Industry?

 
Businesses are now spending time analyzing consumer data to understand what their customers want and improve the quality of their service. A company can use this data to find out where customers spend the most time and link those locations to purchasing patterns. For instance, the bank collects data from each transaction a customer makes and uses predictive analytics to learn more about the consumer’s banking habits. As a result, the bank can develop products that are exactly what the customer could need. 

1.  

   To begin, decide the feature you wish to enhance

    

You must first determine what part of your business you want to improve to make use of predictive analytics effectively. To increase client spending on a certain product, you would need to improve product sales.

2.  

   Identify the elements that influence the contested attribute

    

Now that you know you want to increase sales of product A, you need to figure out what will persuade people to buy it. Think about the primary factors that led buyers to purchase it, for instance. 

3.  

   Examine trends and make predictions

    

Analyze historical trends and forecast future outcomes using the logic you built above, incorporating it into a data model.

4.  

   Automation

    

Finally, automate the system so that it can automatically update itself each time a consumer makes a purchase thanks to a discount. Additionally, permit access to the system for your sales team so they can observe factors affecting sales.
 

Conclusion

 
It is predicted that predictive analytics in finance will significantly disrupt existing data analytics practices. To better understand customers’ demands, internal workflows, markets, and other factors, many businesses are already successfully implementing real-time predictive analytics technology.

Those in the finance industry considering implementing predictive analytics in their work should consider how to tailor this solution to their specific needs. SCAND provides a wide range of fintech development services, including the creation of predictive analytics tools that leverage Big Data processing tools, Artificial Intelligence, and Machine Learning technologies.
 
Author Bio Credit 
 
Madhu Kesavan is the Founder & CEO of W2S Solutions, a globally recognized big data analytics company supporting enterprises and governments in their digital transformation. With 20+ years in the IT market, he makes his vision for a sustainable future come true by leveraging technology.