Modern organisations are massively transforming their finance function by leveraging data and applying advanced statistical techniques, machine learning algorithms.
Machine learning is providing the finance and FP&A teams a greater opportunity to evaluate external and internal data and identify the business drivers. By contextualising business goals with advanced analytics , finance professionals are now in a state to make and influence more informed and profitable choices.
A much-faster and an accurate picture of analysis, by using much more information than manual data analysis ever could
Ability to make an instant prediction based on any number of variables
Reduced process burden due to optimization and automation of processes, faster insights, and data-driven decision-making
The production of better and more accurate predictive analysis in shorter periods with minimal manual input
Reduced operational costs as ML allows process automation
Increased revenues as a result of better productivity and enhanced user experiences
Better compliance and reinforced security
The businesses, and hence the finance data, across industries, are becoming increasingly complex. In order to stay afloat amidst the changing market dynamics, enterprises are utilizing AI/ML by building and curating ML models to enable faster decision making. Analytical insights by these ML models have to be turned into actual business outcomes. Below are a few examples of how AI/ML is empowering the finance teams.
People, data, processes, technology, and team accountability - all affect revenue. With ML-models, organizations address the gaps in these areas by improving operational efficiency at all levels and thereby optimizing the revenue streams .
Advanced analytics can predict a company's financial trajectory. A sophisticated and elegant algorithm is used to predict future revenues, expenses, cash flow, and improve profitability.
AI/ML is being used by businesses to minimize overstocking by analyzing needed inventory levels and modeling stock-keeping unit (SKU) level parameters (e.g. lead times, safety stocks, minimum order quantities, replenishment rates, etc.). Furthermore, these advanced analytics solutions uncover trends that businesses may adapt to minimize inventory issues such as deadstock, supply mismanagement, and waste.
The predictive model is used to predict at-risk customers and specific recommendations are made to retain these customers.
Given the vast amount of data available on product performance in its various stages, advanced analytics techniques help to identifying the key variables impacting product performance and consequently help predict the growth trajectory so as to determine the optimal allocation of marketing funds. This improves marketing ROI, helps in root cause analysis to improve product lifecycle management etc.
FP&A teams can better analyze the cash conversion cycles at a much granular level and improve company's internal policies and processes that are impacting the conversion cycle viz. collection of outstanding invoices etc.
Advanced ML techniques are used to analyze and control the risks by identifying and highlighting fraudulent factors/ transactions.
Profit drivers (at the product line, production center, or customer level) are readily and quickly identified using machine learning techniques.
It helps understand the exact combinations of production scenarios and sales circumstances that consistently produce losses. By performing what-if analyses on key variables, the finance teams can know if some product lines or tie-ups are not profitable.
Advanced statistical techniques and ML algorithms are transforming the finance functions - enhancing profitability, compliance, and competitiveness while also assisting in the quick and informed business decisions. Organizations can begin the data-driven journey for finance functions by taking little steps, such as incorporating data analytics into operational models, and then expand from there.
Data labeling, clustering, and segmentation
Model testing and fine-tuning
Deployment and integration with enterprise systems