Any business would definitely want to know the future in order to benefit out of it. Will customers buy more products during the Christmas season, or will demand to drop off? How much will the business have to spend on overtime during the holidays? Will a hot new product sell out in New York next month?
With the increasing role and responsibilities of the CFO and financial professionals seek solutions to help provide answers to the above questions, and drive performance across the business. Today, predictive analytics is changing the game for companies and their teams.
The basics of predictive analytics in finance
Predictive analytics in finance is where massive amounts of data crunched to find patterns. It helps business owners to decipher customer behavior & also help identify how deep each business factors influence the measures. This, in turn, helps the decision makers to anticipate the outcomes in a complex scenario to make better decisions.
Predictive analytics in financial services can directly affect overall business from strategy, revenue generation, resource optimization to sales nurturing. The reluctance of executives and analysts, however, to embrace automation is hugely expensive in terms of capital, productivity, reaction time, time to market, and in most cases bottom-line results. Full automation of predictive and prescriptive analytics supported by machine learning must be a core part of the equation. Of course, some industries already use predictive analytics. The insurance industry is a fine example, where companies have sliced and diced mortality data to predict when policyholders with life policies will die. But the world of predictive analytics goes far beyond the insurance industry.
Here are just four of the many ways predictive analytics can help finance teams move their companies ahead of the competition.
1. Predicting revenue
A plethora of marketing, sales, operation, and even customer behavior data is making it possible for finance teams to not only forecast revenue more accurately but anticipate future demand for products. For instance, car manufacturers have long used historical purchase data to predict demand; now they can overlay that data with information regarding current internet searches to better forecast sales.
2. Slashing expenses extraordinarily
Predictive analytics can help lower a variety of costs, particularly unexpected ones, by detecting where underperformance is likely to occur. In the health care industry, for example, hospitals are subject to reduced Medicare payments if their patient readmission rates are high. These fines can be up to 3 percent of total revenue from Medicare for hospitals with the highest readmission rates, according to the U.S. Centers for Medicare and Medicaid Services. By using predictive analytics to identify which patients are most likely to have post-discharge problems, hospitals can give extra care and instructions to at-risk patients and potentially save millions of dollars.
3. Improving supply-chain efficiency
Predictive analytics can take massive amounts of data from point-of-sale systems and make real-time forecasts of when and where products are likely to sell out or not move at all. This foresight can save retailers significant amounts of money that might otherwise be wasted on emergency inventory purchases to meet unexpected demand or shipping inventory where it’s not needed. Ensuring that popular products are always in stock keeps customers happy, too.
4. Reducing labor costs
There are dozens of ways to apply predictive analytics to labor costs. In the utility industry, for example, predictive analytics can use data from meters to forecast which customers will have high bills that month. This can be used to boost customer satisfaction by alerting households about high bills before they arrive. If customers are more likely to call the utility provider when their bills are high, predictive analytics can not only help finance teams project revenue more accurately, but they can also predict labor costs at call centers.
5. Detecting fraud
It’s an obligation for financial teams to guarantee the highest level of security to its customers. The main challenge for companies is to find a good fraud detecting system with criminals always hacking new ways and setting up new traps. Only qualified data scientists can create perfect algorithms for detection and prevention of any anomalies in user behavior or ongoing working processes in this diversity of frauds. For example, alert for unusual financial purchase for a particular user, or large cash withdrawal will lead to blockage of such an action until the customer confirms them. In the stock market, machine learning tools can identify patterns in trading data that might indicate manipulations and alert staff to investigate.
However, the greatest thing of such algorithms is the ability of self-teaching, becoming more and more effective and intelligent over time.
The use of predictive analytics is spreading like wildfire. The ability to see even a tiny piece of the future can lead to happier customers, improved efficiency and productivity, and more successful business decisions.