[1] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities.
Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
[2] The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
ARIMA models are known to have no overall trend, but instead have a variation around the average that has a constant amplitude, resulting in statistically similar time patterns.
A time series is the sequence of a variable's value over equally spaced periods, such as years or quarters in business applications.
[16] The ARIMA method for analytical review uses time-series analysis on past audited balances in order to create the conditional expectations.
If the reported balances are very different from the expectations, there is a higher possibility of a material accounting error and a further audit is conducted.
The other method is the STAR annual balance approach, which happens on a larger scale by basing the conditional expectations and regression analysis on one year being audited.
Besides the difference in the time being audited, both methods operate the same, by comparing expected and reported balances to determine which accounts to further investigate.
[16] As we move into a world of technological advances where more and more data is created and stored digitally, businesses are looking for ways to take advantage of this opportunity and use this information to help generate profits.
In a study conducted by IDC Analyze the Future, Dan Vasset and Henry D. Morris explain how an asset management firm used predictive analytics to develop a better marketing campaign.
One technological advancement is more powerful computers, and with this predictive analytics has become able to create forecasts on large data sets much faster.
With the increased computing power also comes more data and applications, meaning a wider array of inputs to use with predictive analytics.
Another technological advance includes a more user-friendly interface, allowing a smaller barrier of entry and less extensive training required for employees to utilize the software and applications effectively.
Due to these advancements, many more corporations are adopting predictive analytics and seeing the benefits in employee efficiency and effectiveness, as well as profits.
Using time-series analysis, the values of these factors can be analyzed and extrapolated to predict the future cash flows for a company.
DKW (1998) uses regression analysis in order to determine the relationship between multiple variables and cash flows.
Through this method, the model found that cash-flow changes and accruals are negatively related, specifically through current earnings, and using this relationship predicts the cash flows for the next period.
Predictive analytics in the form of credit scores have reduced the amount of time it takes for loan approvals, especially in the mortgage market.