Predictive Analytics Solutions for Fintech and The Benefits They Bring

Predictive analytics in finance has been around for a while, but in the last few years, its popularity has spiked tenfold. You will be surprised to know that 70% of financial institutions see big data and analytics as offering a significant competitive advantage. Not only that, 88% believe that these are essential for gaining an edge in the market. (Source: Big Data in Finance)

But have you ever wondered why so many organizations have started trusting predictive analytics solutions, especially when it comes to fintech? What benefits do they actually bring? In this post, we will take a deep dive into this subject and cover exactly that. So, read on to uncover all the answers!

Key Takeaways 

• Understanding what predictive analysis is
• Looking at their diverse benefits
• Discovering its impact on fintech 
• Uncovering some effective predictive analytics solutions for fintech.

What is Predictive Analytics?

Predictive analytics is a subset of data analytics which is focused on building accurate forecasts based on historical data. Predictive analytics deploys a variety of techniques, such as data mining, statistical modeling, and machine learning, to identify the non-obvious patterns and dependencies in the data and transform them into valuable insights. As a result, it is often applied in various technological solutions across industries.

With the help of predictive analytics, companies can better understand the state of their business and adjust their strategy correspondingly, at the same time, avoiding major financial losses. With the benefits it brings, the market size and share of predictive analytics are expected to reach $28.10 billion by 2026, according to statistics. 

Intriguing Insights 

This infographic shows the benefits of using data analysis in the financial spectrum

The Benefits of Predictive Analytics

As mentioned earlier, predictive analytics is used in a variety of industries, including healthcare, retail, manufacturing, governmental and public sectors. But what benefits does it bring to the fintech industry? Let’s have a look.

Fraud Prevention

By analyzing massive data sets, predictive analytics tools can easily determine any suspicious behavior, events, or actions that are related to fraud. Companies can use this analysis to identify the most serious risks and problem areas and take preventative measures ahead of time.

Because the fintech industry deals with extremely sensitive customer data, even the smallest cyber fraud attempt may result in huge financial losses. In order to avoid them, companies should consider using predictive analytics for fraud detection and prevention.

Accurate Business Strategy

The analysis of historical data can help companies not only analyze past mistakes but make smarter decisions in the future. The forecast provided by predictive analytics can guide the entrepreneurs towards a more efficient business strategy and help adjust it in accordance with set business goals.

By knowing the possibilities of the company’s development and seeing the potential risks and opportunities, business owners gain a chance of significantly growing the business and avoiding major losses in terms of finances, time, and resources.

Better Customer Service

Another important benefit that predictive analytics brings is better customer service. By analyzing the past customer behavior, actions, and decisions, a predictive analytics tool can identify the customers’ likes and dislikes, potential future actions and responses to certain offers and services.

Such knowledge empowers companies with valuable marketing insights and helps provide more efficient and customer-oriented services. This, in turn, leads to an increase in customer satisfaction which positively impacts the company’s profitability.

Interesting Facts 
According to the Mordor Intelligence report, the AI fintech Market size is estimated at $42.83 billion in 2023. It is expected to reach $49.43 billion by 2028. Furthermore, it is anticipated to grow at a CAGR of 2.91% during the forecast period (2023-2028).

Predictive Analytics Solutions for Fintech

Now that we described all the benefits that predictive analytics brings to the industry, it’s time to review the most popular solutions that can be used by any company that wishes to make accurate and data-driven decisions.

Board

Board is a Business Intelligence and Corporate Performance Management solution that is widely used in the banking and finance industries. Even though this tool comes with a variety of complex functions (i.e. database handlers), it has a very intuitive and user-friendly interface that helps the user navigate through functionality.

Board enables its users to test different scenarios and see the possible future outcomes due to live dashboards that automatically update and reflect the real-time data. Such an approach eliminates the need to create a new model for every new scenario which, in turn, is very time-efficient. As well, Board allows extracting the data from a variety of sources, including the ERP systems, cloud, CRM, flat files, etc.

Board is a great solution for fintech companies as it provides the option to perform “what-if” analysis and make accurate predictions and forecasts, thus, helping companies dodge any potential risks.

SAP Analytics Cloud

SAP Analytics Cloud is a comprehensive cloud solution for all types of analytics. From BI to augmented analytics, it offers a variety of specific solutions for different industries, including banking and fintech.

Here are the features that come in SAP Analytics Cloud:

• Liquidity management,
• Financial planning,
• Financial and profitability results,
• Expense management

As well, the platform can identify the top-performing employees and the leaders which may also become a valuable insight in terms of strategic development.

Alteryx Platform

Named a Leader in Gartner Magic Quadrant for Data Science and Machine Learning Platforms, the Alteryx Platform is a feature-rich self-service solution. It specializes in data science and lists finance as one of the key industries that it serves. 

This platform focuses on data mining and predictive analytics and enables the users to upload the data from multiple sources, such as Hadoop and Salesforce. Once the data is analyzed, the platform can generate the report and convert it into a desirable format.

KNIME Analytics Platform

KNIME is an open-source platform that specializes in data science and predictive analytics. Its strong suit is a very user-friendly interface that is combined with complex and rich functionality. 

The platform allows users to retrieve data from various sources, such as Google Sheets, Azure, AWS S3, etc. After the data is collected, the platform can sort and filter it, and prepare the comprehensive data analysis that is easier to explain. As well, KNIME offers predictive analytics features and can easily export the data into the desired format.

Even though fintech is not the primary point of focus of this platform, it is suitable for credit scoring, credit rating, and customer risk management.

H2O 

The H2O.ai is also a new-gen AI platform that needs to be observed by the enthusiasts. The solution specifically emphasizes the importance of understandable and valuable machine learning and aims to help businesses extract value from their data with minimal effort.

For the fintech industry, H2O offers the following services:

• Customer churn prediction,
• Credit risk scoring,
• Fraud detection,
• Anomaly detection,
• Know Your Client.

The platform seems like a suitable solution for those companies that wish to avoid future losses and risks and, at the same time, grow their business with the help of data-driven decisions.

Summary

A predictive analytics platform is a valuable tool that helps companies grow while avoiding serious issues and risks. On the other hand, it demands the availability of robust time and financial resources.

Before starting to work with Big Data, it is important to first align the business goals and educate the employees on the use of Big Data. Only after completing the groundwork, a company integrates a predictive analytics tool into its processes in order to gain the maximum benefit from it.