Have you ever wondered how financial institutions seem ahead of the curve when it comes to markets and related services for their clients? This can largely be attributed to teams of analysts performing their magic through predictive analysis using current and historical data, identifying trends, and making calculated predictions of markets and client needs.
Organizations like personetics.com, for example, empower financial institutions to establish themselves as a trusted financial service provider and build deeper relationships with their clients. This is done by leveraging the power of artificial intelligence (AI) and machine learning (ML). Predictive analytics can assist clients to govern their finances and simplify their money management, helping clients grow and making their businesses more profitable.
For data to be correctly aggregated for predictive analytics though, data must be parsed through various data cleaning tasks. These include the removal of errors, duplicates, and obvious outliers. To make sense of the data, one such method which can be applied is called imputation.
What is Imputation?
Abraham Lincoln was onto something when he stated: “If I had six hours to chop down a tree, I’d spend the first four hours sharpening the ax”. This principle of preparation seems like a universal truth in business and especially when it comes to predictive analytics.
Analysts typically spend most of their time cleaning data. Although this process is vastly time-consuming, it is crucial to the success of any study.
Imputation is part of this process of data cleansing.
Imputation, in short, is a method used in data analytics to address the occurrence of data points that are missing from a particular dataset. This implies that it will address the existence of null values in a dataset which could impact the outcome of predictive financial analyses. The process is mainly utilized since the indiscriminate elimination of data points might not only be impractical but could also lead to the introduction of certain biases into the data.
Based on the size of the data set and the frequency of null values, simply removing incomplete points might also significantly decrease the population of entries in the data set. Imputation, therefore, is utilized to derive and generate values to replace these null value data points.
The methods utilized for imputing data would typically be dictated by the type of data being dealt with, whether qualitative or quantitative. The data might even be unstructured, as in the case of organizational big data.
We would like to highlight five of the various imputation methods in use by the financial industry today.
Listwise Deletion
This method deals with rows or columns that have missing data points, that constitute more than half the values by deleting such row or column. Analysts should however use this method sparingly.
Replacing missing values with the dataset median or mean
Using this method has the distinct benefit over likewise deletion since no data loss is incurred by replacing the null values with either the median or the mean value.
Cold Deck Imputation
In this method, values are replaced from other datasets with similar information types. This replacement will only be done after its probability has been ascertained.
Hot Deck Imputation
In this method, values are also replaced. The difference, however, is that this replacement will normally be done with a random variation of the missing data from the same column.
Regression Imputation
With the existing variable, a value prediction is produced, and the predicted value is then utilized to populate the null value position. This process is typically more time-consuming but delivers more accurate results.
Summary
Although predictive analysis might seem like the crystal ball of the data analytics world, it is a highly sought-after capability in the financial sector today. Having the ability to accurately predict business and financial trends is a priceless commodity in this industry.
By leveraging AI and ML to clean data through imputation, financial institutions have greatly improved the quality of their data, and by extension the accuracy of their predictive analytics results. Multiple imputations, via AI and ML, are a breakthrough in predictive analytics. It solves many difficulties with missing data points and, when done correctly, results in unbiased parameter estimations.