How to optimize CTRM tasks using RPA & AI?
Commverse is assisting the commodity companies in enhancing the efficiency and effectiveness of routine trading and risk management activities by leveraging the power of Robotic Process Automation (RPA) and Artificial Intelligence (AI), in conjunction with their existing CTRM applications.
CTRM (Commodity Trading and Risk Management) applications are crucial for managing the end-to-end processes involved in commodity trading, including trade capture, risk management, logistics and accounting. Combining RPA and AI in CTRM applications can lead to a more streamlined, accurate and responsive trading environment, allowing organizations to focus on strategic decision-making and value-added activities.. Here’s how:
Data Entry and Validation:
- RPA: Bots can be deployed to automate data entry tasks, reducing the risk of human errors and increasing speed.
- AI: Natural Language Processing (NLP) algorithms can be used to validate and extract relevant information from unstructured data sources, such as contracts, emails and news articles.
Trade Execution:
- RPA: Bots can automate the execution of routine trades based on predefined criteria, ensuring timely execution and reducing the response time to market changes.
- AI: Machine learning algorithms can analyze market trends and historical data to provide insights for better decision-making in trade execution
Risk Management:
- RPA: Bots can assist in automating risk assessment processes by regularly monitoring and updating risk parameters, flagging potential issues and triggering alerts.
- AI: Advanced predictive analytics and machine learning models can help in identifying potential risks by analyzing historical data, market trends and external factors.
Contract Management:
- RPA: Bots can be used for contract management tasks, such as document generation, tracking contract terms and managing renewals, improving overall contract lifecycle management.
- AI: Natural Language Processing (NLP) can aid in understanding and extracting relevant information from contracts, ensuring compliance and reducing the risk of contractual errors.
Logistics and Supply Chain Optimization:
- RPA: Bots can automate routine logistics tasks, such as order processing, shipment tracking and inventory management, ensuring smoother operations.
- AI: Predictive analytics and machine learning algorithms can optimize supply chain processes by forecasting demand, identifying potential disruptions and suggesting optimal routes for transportation.
Reporting and Analytics:
- RPA: Bots can automate the generation of routine reports, ensuring accuracy and timeliness in reporting.
- AI: Advanced analytics tools can provide deeper insights into trading patterns, market trends and risk exposure, enabling better-informed decision-making.
Data Aggregation and Cleansing for EOD and EOM:
- RPA: Bots can automate the collection of data from multiple sources such as trading platforms, market data providers, and internal systems. They can then cleanse and normalize the data, ensuring accuracy and consistency.
- AI: Machine learning algorithms can assist in identifying and correcting data anomalies, outliers, and discrepancies, improving data quality for EOD and EOM reporting.
Risk Assessment and Analysis:
- RPA: Bots can perform routine risk calculations and analysis tasks, such as Value at Risk (VaR) calculations, exposure monitoring and stress testing, enabling timely risk assessments.
- AI: AI-powered predictive analytics can enhance risk assessment by identifying emerging risks, analyzing historical data patterns and providing insights into potential risk exposures.
Customer Support:
- RPA: Bots can assist in handling routine customer queries, providing quick responses and freeing up human resources for more complex tasks.
- AI: Chat bots powered by AI can enhance customer interactions by understanding natural language queries and providing intelligent responses.
