Analytics in finance
Data Analytics / December, 14 2022

Predictive Analytics in Finance – Use Cases and Benefits

Predictive analytics in finance can significantly impact revenue growth, data security, risk management, and business performance. It has groundbreaking capabilities and scope in every sector. Now you can exponentially improve prediction accuracy, reduce planning efforts, and identify market changes early. Gartner says combining predictive and prescriptive capabilities will help enterprises solve business problems and drive more intelligent decisions.

We are aware of the skyrocketing growth of data. While enterprises feel the heat of managing tons of data, you can make this data growth a boon by adopting predictive analytics. It can sieve through large volumes of data and identify patterns and trends. It uses extensive data mining, statistical modeling, and advanced artificial intelligence capabilities to analyze data and make predictions. Machine learning algorithms, neural networks, and cognitive computing abilities improve predictive models’ efficiency, speed, and accuracy.

The great news for CFOs is that they can gain a competitive advantage as they can predict future outcomes more accurately. Businesses that use predictive analytics models will also have a competitive advantage in M&As, market expansion, and revenue management.

Use cases of Predictive analytics in finance:

Predictive analytics in finance has a wide range of applications. It allows you to forecast cash flows accurately and plan your investments and expenditures. It helps you avoid financial risks and develop better customer relationships.

For instance, can help you identify customer payment patterns, credit risks, and payment default changes in accounts receivable management. More advanced predictive analytics algorithms will even be able to predict if a customer is likely to pay or not.  Here are a few of the significant use cases.

Revenue forecast:

Cash flow forecasting models of predictive analytics can assist you in gaining a better understanding of your cash inflows and outflows. The algorithm can predict the pattern of your cash inflows and outflows based on your invoice data, past payment trends, cash position, and other factors. Furthermore, it allows you to better plan your investments and segment customers based on their likelihood to pay. To arrive at accurate forecasts, advanced models for predicting cash flows analyze trends in historical data using statistical computations that help categorize events, identify influencing variables, and ignore outliers.

Customer payment prediction:

When you sell on credit, there is always an element of uncertainty about payments. With predictive analytics, you can predict whether the customer will pay. Predictive analytics algorithms use input data, such as past payment trends, current financial strength, market conditions, etc., to predict whether a customer will pay on time, make partial or short payments, or require coercion to pay after the due date.

Payment predictions assist you in prioritizing accounts and tailoring customer interactions based on their likelihood of paying. This allows you to avoid wasting time and effort on customers who are likely to pay. This also reduces the follow-up tasks, saving time and effort for your team.

Fraud detection:

With fraudulent cyber activities increasing, fraud detection is one of the top priorities for any CFO to keep financial risks as low as possible. Capital investments, money market investments, technology spending, and selling on credit involve risk, and minimizing it is critical to ensuring that the business does not suffer any unexpected losses.

Predictive tools detect very minute differences in transaction data and aid in predicting and detecting fraud. It also predicts risks associated with various tasks and categorizes them based on their impact on the business.

Risk management:

When you perform a credit sale, predictive analytic algorithms help you score customers and identify the level of risk. It reduces payment risks by utilizing various information sources, including credit reports and market data. The AI-powered engine can also predict blocked orders based on customer payment history and credit limit utilization.

Resource management:

Finance teams also devote significant time and effort to budgeting, planning, and resource allocation. You are the final decision-makers on funds and budgets. It would be best to ensure you are spending appropriately.

Predictive analytics technology analyzes data from various sources to identify patterns and trends and predicts whether or not the budget will likely deliver the desired return on investment (ROI). The model identifies recurring patterns and trends in historical data and recommends the best resource allocation.

Accounts receivable management:

Management of accounts receivable is a critical finance function. Accounts receivable analytics provides real-time insights into the risks and receivables that may limit your working capital. It categorizes accounts and can forecast how much-working capital will be available. The AR team is responsible for collecting customer payments, closing invoices, and reconciling the books.

Benefits of Predictive Analytics in finance:

Predictive analytics in finance can assist firms in modeling specific economic scenarios and making evidence-based risk-mitigation decisions. Here are the benefits,

  • Cost savings – Financial firms can gain better insight into daily cash flows and increase the cost-effectiveness of their operations by incorporating predictive analytics into budget creation and risk modeling.
  • Risk prevention – Predictive analytics assists financial institutions in modeling specific economic scenarios and making evidence-based risk-mitigation decisions.
  • Fraud detection – Machine learning and advanced statistical models enable businesses to process massive amounts of data in real-time and detect fraud more effectively.
  • Tailored services – Companies can improve their understanding of customer profiles by analyzing large amounts of customer data, providing personalized services at scale, and increasing customer engagement.
  • Revenue growth – Machine learning-enabled predictive models enable investment professionals to make more data-driven and profitable market decisions.
  • Better employee experience – Predictive analytics handles manual tasks, allowing employees to focus on more engaging and value-added tasks, increasing overall employee engagement and experience.

Predictive analytics in finance opens doors to take customer experience and business performance up the ladder. It helps you be ready to face the increasing market needs while addressing customer challenges. Here are a few things you need to do to get the most out of predictive analytics.

  • Make sure your data is rich
  • Enhance your data governance model
  • Have an advanced technological base
  • Optimize organizational structure

How can we help?

If you are looking for a technology partner to help you with the abovementioned requirements, then you are at the right place. At Saxon, we help organizations optimize their organizational structure and improve governance and data security. Check out our solutions to know more about us.

Bring your use case. We can help you. To start your journey,

Get in Touch


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