Benefits of Automated Predictive Analytics for Financial Services

In our current financial environment, manual data analytics is cost-intensive and inaccurate. The 2020 Mordor Intelligence Report claims the global demand for AI in fintech will rise from $7.91 billion in 2020 to $26.67 billion by 2026. And we can tell you why it’s important you don’t miss the boat and make sure to get on board with the change.

Automated predictive analytics is crucial for successful corporate finance companies. You might be wondering why’s that? Let’s look at how real-time analytics transforms the fintech landscape and how different services use it to maximize profits.

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How automation transforms real-time analytics

Real-time data analytics allows data to be processed, measured, and evaluated immediately after entering a database. This process would take hours, days, or even weeks ten years ago. But these days, Artificial Intelligence (AI) and Machine Learning can make this data available almost instantly.

Companies in different industries have been automating tasks for over a century. Financial organizations are now doing the same with data processing by using analytics automation tools and predictive modeling. Automated analytics can help businesses develop a data strategy, which consists of descriptive, predictive, and prescriptive analytics.

• Descriptive analytics is the raw data (sales numbers, counts, customer statistics) you can turn into actionable information.
• Predictive analytics uses AI, machine learning, and Big Data to predict events in the future.
• Prescriptive analytics provides intelligent recommendations to drive you towards desired outcomes.

When used correctly, prescriptive and predictive analytics can provide extensive insight into issues a business may be facing and provide optimal ways to deal with them. For instance, they help finance leaders to:

• Capitalize on large volumes of internal and external information
• Get objective forecasts, such as estimated demand and revenue
• Identify vulnerabilities in the system’s performance to adhere to fintech security requirements
• Improve cash flow management to avoid a liquidity crisis
• Prevent fraud with real-time analysis and proactive alerts
• Analyze and predict financial risks

Basically, automated data analytics help companies identify new opportunities which allow them to act swiftly. With that being said, how about some actual examples of predictive analytics in finance?

Predictive analytics for financial services and banking industries

Nearly all successful financial and banking institutions rely on automated predictive analytics in their business as it consolidates and simplifies data to help companies maximize profits. 

Need some examples? Here are several use cases of predictive analytics and forecasting in finance:

Fraud detection

Real-time analytics help companies pick up on the slight differences in a user’s behavior when compared to identify fraud. Machine learning-powered technologies can analyze discrepancies based on a user’s transaction history and, impressively, even publicly available data such as social media.

For example, the fintech platform will automatically block transactions, large cash withdrawals, or access from unusual locations until the customer confirms their actions. So, if you’re walking the streets of Barcelona and you’re unlucky enough to be pickpocketed, fear not: fintech will have your back.

Modeling customer value

Algorithm-based software has long been used by banking institutions to generate insights about prospective customers. Predictive analytics tools automatically assess a customer’s credit history, transactions history, interactions with the government, and even social media activity. As a result, banks can speculate any credit risks there may be and the net profit for different users. 

Personalization

Financial companies are wholly dependent on their customers; they are their most valuable asset. According to McKinsey&Company 2018 Report, personalized experiences drive revenue growth up by 15% in the fintech industry. 

Predictive analytic tools and AI give invaluable insights into social-demographic trends, spending habits, and many other factors which help personalize service for customers.

Benefits of data analysis automation for the finance industry

Predictive analytics accelerates and automates processes that were previously bottlenecking financial companies. We refer primarily to three types of operations: accounting, taxes, and audits. 

Accounting

Collecting and analyzing data efficiently is impossible with an old spreadsheet-based approach, especially for fintech services. Put simply, organizations just wouldn’t be able to process daily data from orders, emails, apps, and social media.

AI-powered tools allow companies to deliver accurate results without human error at a lower cost and with a quicker turnaround. Hence, data analytics tools help you extract more data across your business to preserve their profitability.

Taxes

Data pipeline automation can aid companies in transforming tax data into actionable insights. Don’t want to waste trained personnel on masses of clerical tasks? Of course, you don’t. Luckily for us, specialized software does the same thing at a rapid rate and with fewer resources.

Apps can quickly identify invoice errors, missing tariff classification codes, and violations of tax laws. Here’s the kicker: there’s little to no possibility for human error. At the same time, predictive analytics can examine complex data patterns to design the ideal tax profile.

Audit

Data analytics tools allow corporate finance companies to do the audit work in real-time. What does that look like? Well, automated solutions can scan data continuously, making it possible to test entire market segments and thousands of transactions in mere minutes.

A successful audit requires human judgment and business skills. Nonetheless, AI and machine learning transform the internal audit process, generating the data faster and meticulously. Plus, continuous auditing software can help your company with data visualization, customizable rule-based testing, and accurate predictions.

Automating data pipeline

An automated data analysis pipeline gives you control over your business intelligence and real-life info. Just imagine a seamless flow of structured data between all your departments, systems, and applications.

How can data pipeline automation help your business? To name a few:

• Productivity boost. Technologies like AI can save hundreds of man-hours by automating routine operations and streamlining data maintenance. As a result, your employees will focus on other productive tasks, while managers benefit from relevant information.
• Improved revenue. Collecting data is one thing, using it is another. Most corporate financial organizations can’t monetize the entire scope of the information they receive. An automated data pipeline helps analyze info for risk assessment, faster decision-making, and successful financial app development.
• Correct data. Automated data analytics tools in the financial services industry can seamlessly detect missing values, typos, and other errors. Consequently, you can be sure you’re banking only on valid data for business decisions.
• More growth opportunities. Forrester’s 2018 Report indicated that businesses relying on insight and analytical data grow about 30% more than companies with low business intelligence. With automation tools, you can utilize multiple cloud environments for databases and applications.
• Competitive advantage. According to Gartner’s 2018 Report, less than 13% of organizations have high business intelligence and analytics maturity. Most companies lack the skills, technology, and resources to exploit machine learning and AI-based analytics to the fullest. However, automation of data analysis will help you surpass your competitors with informed decisions.

Data stands at the core of your systems, therefore, a robust data pipeline will help your business adapt and thrive. And with automated data analysis, you’ll be ready to optimize your financial services model and technology to the ever-changing market conditions.

Conclusion

Data is a crucial resource for corporate finance firms. But collecting raw information isn’t enough — you need the right tools to gain actionable insights. 

Automated predictive analytics is invaluable for gathering intelligence from large magnitudes of internal and external data. In turn, you can use this data to predict how your business, products, and the market will prosper — giving you the heads-up you may need to act accordingly.

Do you want to enhance routine financial processes with lightning-fast software and boost your revenue? Relevant can build an AI-powered data analytics tool for efficient and swift centralized data processing. Drop us a line to learn more about automating data analytics and how to empower your company with the tools it needs to excel.