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Use Cases of AI/ML in Finance

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Modern organisations are massively transforming their finance function by leveraging data and applying advanced statistical techniques, machine learning algorithms.
AI/ML advantage for the Finance Functions
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
ML Use Cases in Finance
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.
1. Revenue Improvement
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 .
2. Financial Predictions
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. 
3.Inventory Management
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.
4. Customer Retention
The predictive model is used to predict at-risk customers and specific recommendations are made to retain these customers.
5.Product Lifecycle Management
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.
6. Optimizing cash conversion cycle
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.
7. Fraud/ Anomaly Detection
Advanced ML techniques are used to analyze and control the risks by identifying and highlighting fraudulent factors/ transactions.
8. Identifying the profit drivers
Profit drivers (at the product line, production center, or customer level) are readily and quickly identified using machine learning techniques.
9. Root cause analysis
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.
Conclusion
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.
AI/ML is incredibly valuable - Only when properly deployed

Reduce the possibilities of failure and provide a seamless digitization experience to your FP&A teams with Aays Analytics. We offer the deepest and broadest set of Machine Learning and Artificial Intelligence solutions and services. Our ML solutions easily integrate with your applications to generate ready-made intelligence for your applications and workflows.
We offer end-to-end deployment and consolidation of machine learning models into the FP&A processes

Data labeling, clustering, and segmentation

Model testing and fine-tuning


Deployment and integration with enterprise systems

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