AI, ML and Predictive Analytics
Large enterprises harness the achievements of Artificial Intelligence and Machine Learning models to manage large volumes of input data that require unification and analysis of various factors. Our ML models present high accuracy, on condition that an amount of historical data is enough.
AM-BITS has proven expertise in building ML models for various business tasks, such as:
- Automation of the procurement planning process based on predictive models that are built on historical data.
- Optimization of operating costs by automating resource and event planning.
- Voice identification to enhance the workflow efficiency of call centers.
- Analytics and forecasting of the expected results of business processes: demand, consumption, prices, etc.
Our cases in the category of AI, ML and Predictive Analytics
- Cash Flow Optimization in ATM Network System
- Forecasting of Electricity Consumption Rates
- BIOMETRIC VERIFICATION BASED ON VOICE DATA
Challenge:
Reduction in operating costs for provisioning cash machines.
Solution:
The actual daily data of ATM cash withdrawal was used for further data analysis. We utilized Gradient Boosting Regressor to build an ML-model .
The solution comprised three steps. The first stage:
- data evaluation; setting the requirements and success criteria; data loading, depersonalization and data enrichment; experiment procedure agreements.
The second stage focused on:
- segmenting the research objects; training, testing and evaluating the quality of the model.
The third stage was the implemention of:
- automated data loading or model deployment in the customer’s environments; regular quality control by A/B testing; technical support of the model and optimization of new data entry.
Result:
Implementation of automated cash demand forecasting with an error range of 0.01% to 3.5%. Great reduction in operating costs by lowering the amount of allocated funds up to 30%, cashback up to 40%, out-of-cash downtime up to 0.2%.
Challenge:
Obtaining a short-term forecast of energy consumption for procurement planning on the Ukrainian Energy Exchange.
Solution:
While analyzing Machine Learning algorithms and methods for short-term and time-bound forecasting of events, we opted for using Recurrent neural networks (RNN). RNN made possible to process a series of events in time or sequential spatial chains.
To conduct the research and set up the test RNNs, we leveraged the open-access hourly data stats on electricity consumption by New York City alongside temperature fluctuations during this period. As a result of our analysis, we managed to get an accurate forecasting for two days’ consumption rates upfront by using a 3-month range of historical data.
Result:
Utilization of Recurrent neural networks (RNN) to build an effective ML-model. Customers can get short-term forecasts of energy consumption based on the analysis of historical data, with an accuracy of 96.4-99.5%.
Challenge:
The verification of call center customers based on voice data as a part of multi-factor authentication for security improvement.
Solution:
The use of deep neural architecture for comparing voice prints from a database, with verification accuracy of over 90%.
