Machine learning based systems and methods for forecasting cash flow for a professional employer organization

A machine learning based (ML-based) computing system for computing future cash flow for one or more first users, the ML-based computing system comprising:

• one or more hardware processors;
• a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises:
• a data receiving subsystem configured to receive one or more inputs from one or more second users, wherein the one or more inputs comprise first information related to at least one of:
• one or more entities associated with the one or more first users, and a forecast period associated with a time duration during which the one or more second users are adapted to compute the future cash flow for the one or more entities associated with the one or more first users;
• a data extraction subsystem configured to extract one or more data associated with at least one of:
• one or more cash flow data of the one or more first users and second information associated with one or more third users, from one or more databases based on the one or more inputs received from the one or more second users, wherein the cash flow data comprise at least one of:
• one or more historical cash flow data and one or more real-time cash flow data;
• a cluster feature generation subsystem configured to generate one or more features associated with the one or more third users based on the extracted one or more data associated with the one or more cash flow data of the one or more first users and the second information associated with the one or more third users, the cluster generation subsystem further configured to:
• generate, by a frequency-based feature generation subsystem of the cluster feature generation subsystem, one or more frequency-based features based on at least one of:
• first payment frequency and second payment frequency, made to the one or more third users, wherein the first payment frequency is an average payment gap between one or more payrolls of the one or more third users, and wherein the second payment frequency is a recurrent payment day indicating a common day of one or more months on which one or more historical payrolls are performed for the one or more third users;
• generate, by a distance-based feature generation subsystem of the cluster feature generation subsystem, one or more distance-based features based on at least one of:
• a distance from start of the one or more months and end of the one or more months, a distance from start of a quarter time period and end of the quarter time period, a distance from a predefined day of the one or more months, a distance from a last business day of the one or more months, and a distance from a last transaction;
• and
• generate, by a seasonality-based feature generation subsystem of the cluster feature generation subsystem, one or more seasonality-based features based on at least one of:
• a mode of one or more days of the one or more months and the mode of one or more weekdays,
• wherein the mode of one or more days of the one or more months is configured to identify a recurrent day in the one or more months for the one or more third users based on the one or more historical payrolls, and
• wherein the mode of the one or more weekdays is configured to determine the recurrent day of one or more weeks for one or more payroll transactions by filtering one or more noises comprising at least one of:
• one or more holidays and one or more bank issues;
• a training subsystem configured to train at least one machine learning model, wherein in training the at least one machine learning model, the training subsystem is configured to:
• receive one or more training datasets associated with the one or more features, from the cluster feature generation subsystem;
• and
• pre-process the one or more training datasets associated with the one or more features to convert one or more numerical values of the one or more features to one or more common scale values by at least one of:
• normalizing, by the one or more hardware processors, the one or more numerical values of the one or more features to one or more standardized ranges comprising zero and one;
• and
• standardizing, by the one or more hardware processors, the one or more numerical values of the one or more features to obtain a mean value of zero and a standard deviation of one;
• a cluster generation subsystem configured to generate one or more clusters associated with the one or more third users of the one or more entities associated with the one or more first users based on the one or more features, using the at least one machine learning model,
• wherein the machine learning model comprises at least one of a density-based spatial clustering of applications with noise (DBSCAN) model and a K-means clustering model;
• wherein the DBSCAN model is trained by one or more first hyperparameters comprising an epsilon hyperparameter and a minimum sample hyperparameter;
• wherein in training the DBSCAN model, the training subsystem is configured to:
• receive the pre-processed one or more training datasets associated with the one or more features;
• select one or more first hyperparameters for training the DBSCAN model, wherein the epsilon hyperparameter indicates a radius within which one or more first data points are indicated as one or more neighbors, and wherein the minimum sample hyperparameter is configured to generate one or more first dense regions by determining a predetermined number of the one or more first data points required within the radius;
• generate one or more first clustering models to automatically group the one or more third users comprising one or more analogical characteristics, based on the selected one or more first hyperparameters;
• scan the grouped one or more third users comprising the one or more analogical characteristics, with the one or more first data points to identify at least one of:
• the one or more first dense regions as the one or more clusters and one or more isolated first data points as the one or more noises;
• compute one or more pairwise distances between the one or more first data points;
• determine whether the one or more first data points satisfy a predetermined criteria of the one or more first hyperparameters;
• and
• classify the one or more first data points as at least one of:
• one or more first core data points indicating the one or more clusters, one or more first border data points, and one or more first noise data points indicating the one or more noises;
• and
• wherein the K-means clustering model is trained by one or more second hyperparameters comprising one or more number of clusters hyperparameters, one or more cluster initialization hyperparameters, maximum number of iteration hyperparameters, one or more relative tolerance hyperparameters, one or more verbose hyperparameters, and one or more random state hyperparameters;
• a cash flow computing subsystem configured to:
• determine the future cash flow for each cluster of the one or more clusters associated with the one or more third users;
• and
• compute the future cash flow for the one or more clusters associated with the one or more third users by adding the future cash flow computed for each cluster of the one or more clusters associated with the one or more third users, for the one or more entities associated with one or more first customers;
• and
• an output subsystem configured to provide an output of the computed future cash flow for the one or more entities associated with the one or more first users, to the one or more second users on a user interface associated with one or more electronic devices.

A non-transitory computer-readable storage medium having instructions stored therein that when executed by a hardware processor, cause the processor to execute operations of:

• receiving one or more inputs from one or more second users, wherein the one or more inputs comprise first information related to at least one of:
• one or more entities associated with the one or more first users, and a forecast period associated with a time duration during which the one or more second users compute the future cash flow for the one or more entities associated with the one or more first users;
• extracting one or more data associated with at least one of:
• one or more cash flow data of the one or more first users and second information associated with one or more third users, from one or more databases based on the one or more inputs received from the one or more second users, wherein the cash flow data comprise at least one of:
• one or more historical cash flow data and one or more real-time cash flow data;
• generating one or more features associated with the one or more third users based on the extracted one or more data associated with the one or more cash flow data of the one or more first users and the second information associated with the one or more third users, by;
• generating one or more frequency-based features based on at least one of:
• first payment frequency and second payment frequency, made to the one or more third users, wherein the first payment frequency is an average payment gap between one or more payrolls of the one or more third users, and wherein the second payment frequency is a recurrent payment day indicating a common day of one or more months on which one or more historical payrolls are performed for the one or more third users;
• generating one or more distance-based features based on at least one of:
• a distance from start of the one or more months and end of the one or more months, a distance from start of a quarter time period and end of the quarter time period, a distance from a predefined day of the one or more months, a distance from a last business day of the one or more months, and a distance from a last transaction;
• and
• generating one or more seasonality-based features based on at least one of:
• a mode of one or more days of the one or more months and the mode of one or more weekdays,
• wherein the mode of one or more days of the one or more months is configured to identify a recurrent day in the one or more months for the one or more third users based on the one or more historical payrolls;
• and
• wherein the mode of the one or more weekdays is configured to determine the recurrent day of one or more weeks for one or more payroll transactions by filtering one or more noises comprising at least one of:
• one or more holidays and one or more bank issues;
• training at least one machine learning model, by:
• receiving one or more training datasets associated with the one or more features, from a cluster feature generation subsystem;
• and
• pre-processing the one or more training datasets associated with the one or more features to convert one or more numerical values of the one or more features to one or more common scale values by at least one of:
• normalizing the one or more numerical values of the one or more features to one or more standardized ranges comprising zero and one;
• and
• standardizing the one or more numerical values of the one or more features to obtain a mean value of zero and a standard deviation of one;
• generating one or more clusters associated with the one or more third users of the one or more entities associated with the one or more first users based on the one or more features, using the at least one machine learning model,
• wherein the machine learning model comprises at least one of a density-based spatial clustering of applications with noise (DBSCAN) model and a K-means clustering model;
• wherein the DBSCAN model is trained by one or more first hyperparameters comprising an epsilon hyperparameter and a minimum sample hyperparameter;
• wherein the DBSCAN model is trained by:
• receiving the pre-processed one or more training datasets associated with the one or more features;
• selecting the one or more first hyperparameters for training the DBSCAN model, wherein the epsilon hyperparameter indicates a radius within which one or more first data points are indicated as one or more neighbors, and wherein the minimum sample hyperparameter is configured to generate one or more first dense regions by determining a predetermined number of the one or more first data points required within the radius;
• generating one or more first clustering models to automatically group the one or more third users comprising one or more analogical characteristics, based on the selected one or more first hyperparameters;
• scanning the grouped one or more third users comprising the one or more analogical characteristics, with the one or more first data points to identify at least one of:
• the one or more first dense regions as the one or more clusters and one or more isolated first data points as the one or more noises;
• computing one or more pairwise distances between the one or more first data points;
• determining whether the one or more first data points satisfy a predetermined criteria of the one or more first hyperparameters;
• and
• classifying the one or more first data points as at least one of:
• one or more first core data points indicating the one or more clusters, one or more first border data points, and one or more first noise data points indicating the one or more noises;
• and
• wherein the K-means clustering model is trained by one or more second hyperparameters comprising one or more number of clusters hyperparameters, one or more cluster initialization hyperparameters, maximum number of iteration hyperparameters, one or more relative tolerance hyperparameters, one or more verbose hyperparameters, and one or more random state hyperparameters;
• determining the future cash flow for each cluster of the one or more clusters associated with the one or more third users;
• computing the future cash flow for the one or more clusters associated with the one or more third users by adding the future cash flow computed for each cluster of the one or more clusters associated with the one or more third users, for the one or more entities associated with one or more first customers;
• and
• providing an output of the computed future cash flow for the one or more entities associated with the one or more first users, to the one or more second users on a user interface associated with one or more electronic devices.