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Benefits of Automated Predictive Analytics for Financial Services

Today, financial institutions require strong FinTech support to keep the competitive edge in the industry like never before. FinTech companies offer banks and financial organizations a wide set of services that significantly facilitate document management and workflow, decrease the number of manual tasks and the possibility of human errors, improve customer service and customer retention, and much more.

One of the recent trends that are rapidly gaining popularity in the financial community is empowering their software infrastructure with automated predictive analytics solutions. The reasons for that are obvious. According to Gartner, there are only 16% of decision-makers who say that they can easily leverage financial data for decision-making. This means that most financial organizations miss out on the opportunity to use this valuable source for their business growth. Some companies have found the solution – harnessing the power of predictive analytics for financial services.

In this article, you’ll find out what predictive analytics is and how it can help financial institutions improve their financial services, upgrade their customer service, and, therefore, increase their revenues.

What Is Predictive Analytics in Finance?

Predictive analytics is a realm of advanced analytics that makes informed predictions based on the accumulated data and various statistical and data processing methods. By leveraging statistical modeling, data mining, and machine learning technologies, predictive analytics finds common data patterns and determines the likelihood of these patterns appearing again.

Banks and financial organizations make the use of predictive analytics to:

  • better allocate their resources and benefit from the possible upcoming events;
  • increase their performance, e.g. improvement of their data processing operations, client service, etc.;
  • minimize risks connected with investments, crediting, and other services.

How Automation Transforms Real-Time Analytics

Automated real-time analytics processes and evaluates data instantly. As a result, employees don’t have to spend hours on data processing. Instead, they use data analytics tools based on Artificial Intelligence (AI) and Machine Learning (ML) that quickly find the common data patterns and build hypotheses on them.

To make data-driven forecasts, AI and ML implement the following techniques:

  • a decision tree is a method when predictive analytics solutions create a branching tree-like graph that illustrates what are the outputs for a specific input; with this method, businesses can find out what are the chances for certain event outcomes, what will be the resource and utility costs, and much more;
  • regression model is used when businesses want to find the relationship between several variables; this correlation is described with a linear regression model; for example, with linear regression, a financial analysis solution can calculate how unemployment rates and inflation affect the GDP, and more;
  • data mining with neural networks is a complex system based on AI and pattern recognition that uses various analytics approaches and can solve diverse types of problems of different complexity; the system imitates the work of a human brain and is aimed at finding hidden data patterns, anomalies, and correlations within large volumes of data; above all, neural networks can improve their accuracy by using data learning algorithms, each time making the system more precise.

A common automated predictive analytics working process usually looks like this. Relying on the outlined techniques, predictive analytics tools:

  • automatically gather the raw financial data across the company;
  • systemize and analyze the acquired data with AI and ML;
  • deliver intelligent recommendations to business owners.

As a result, businesses gain quick insights on how to solve current business issues in the most optimal ways as well as get straightforward and reliable forecasts about upcoming events.

Predictive Analytics for Financial and Banking Sector

Many banks and financial organizations opt for financial software development and create their own decision-making tools, adjusted to their business needs. Here are the most common examples of how predictive analytics can be used to enhance financial and banking services.

Fraud Detection

Predictive analytics tools can help businesses easily detect any unusual behavior and prevent fraud. Empowered with AI and ML, it can quickly gather and analyze the data of numerous  company customers and effectively detect any financial behavior deviations.

For example, if the system finds that the transaction is too large or is made from an unusual customer location, it’ll block the money transfer and will ask the customer to confirm their identity and actions.

Credit Scoring

Crediting a customer let alone a business is always a risk for a lender. With real-time predictive analytics, crediting companies can quickly get the applicant’s credit history, credit scoring, and others. Based on these characteristics, the analytics tools estimate the borrower’s risk score, assessing if the borrower can make due payments.

Marketing

Predictive analytics technology has been long used by such renowned companies as Netflix, YouTube, Meta, Tinder, and many others for improved marketing and service personalization.

Today, many financial companies also use predictive analytics to deeper understand their customers’ behavior, demographics, and preferences. Using this knowledge, financial companies can enhance their marketing campaigns and better adjust their services to meet their customers’ demands.

Demand Planning

Predictive analytics tools can simply forecast the events that will happen in the near future. This way, banking, and finance-related organizations can predict sales and high product or service demand or decline in their popularity. By adjusting their services to these tendencies, businesses can better address customer pain points and, therefore, increase their revenues.

What Are the Benefits of Predictive Analytics For the Finance Industry

Here are the top 6 benefits the financial companies of any size can reap from leveraging real-time predictive analytics technology:

  • Planning future events with certainty – analytical tools can provide a broader picture of the company’s development as well as the tendencies on 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 of the analytics tasks that have been long performed manually can be automated with predictive analytics tools. With task automation, businesses can free up their employees from such basic activities as reporting, controlling, transaction processing, and many others. Instead, companies can delegate higher value tasks to their staff, including strategy and planning, service expansion, decision support, and others.
  • Service personalization – better understanding of customers’ needs increases their retention with the company. Using advanced analytics tools, companies can improve their customer services and make them more personalized.
  • Risk minimization – based on each customer’s history data, financial companies can accurately estimate the risks involved with crediting or project financing. Apart from customer evaluation, companies can also gather inner company data to estimate its performance and future risks as well as the financial-world tendencies and changes.
  • Better protection against fraud – predictive analytics detect any unusual customer behavior in the system and can prevent any fraud attempts just in time.
  • Product enhancement – analytical tools can carefully analyze the product and introduce it to the owners at a different angle. This way, businesses can more effectively enhance their product appearance, distribution, and pricing.

Conclusion

Predictive analytics in finance is a new realm in the FinTech industry that has much potential to disrupt the current data analytics practices. Many companies are already successfully leveraging the real-time predictive analytics technology in their work to better understand their customer demands, inner workflows, markets, and more.

Those finance-related companies that are considering implementing predictive analytics in their work, should think how to make this solution 100% adjusted to their business requirements. SCAND offers various fintech development services, including the development of predictive analytics tools with a powerful support of Big Data processing tools, Artificial Intelligence, and Machine Learning technologies.

Author Bio

Victoria Puzhevich Lead Business Development Specialist
Victoria has over 15 years of experience in IT, keeping track of the current and future trends in the sphere, sharing expert advice and relevant industry experience.

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