Energy Credit Best Practices – Chapter: Information Technology http://ccro.org © Copyright 2022, CCRO. All rights reserved. 15 application, and flag any parties that are deviating from their expected behaviors outside pre-set rules. Regardless of specific functionality, any ETRM and all Credit Information Ecosystem in general, must handle an ever-increasing abundance of both data and processing power. Given this large volume of data, both from internal operations and Systems and third-party providers, it is impossible for any organization, much less a Credit Group, to collect, organize, cleanse, manage, and interpret this data without a formalized IT Data Governance process and data management tools in place. Some typical ways to help govern credit-related data include: • Optimize processes • Gain greater insight into customers, suppliers, and trading counterparties • Identify sources of risks and opportunities and • Automate decision-making using advanced technologies (Artificial Intelligence, machine learning, etc.). The effective and efficient movement of credit-related data among the entire Credit Information Ecosystem through careful and well executed integration is thus the gold standard for success. As a result, it is not uncommon for the mapping exercise identified as Step 3 to take the lion’s share of time when applying any integrated Credit Risk Management strategy, especially for the first time. 2.2.1 Automation and Analytics The practice of automating transactions, processes and procedures using Information Systems to analyze data has been around since the dawn of computing. A compelling reason for incorporating IT into a credit processes is the benefit of decreased costs and increased efficiencies. Their most recent application in credit includes Artificial Intelligence, Robotic Process Automation, Machine Learning and Natural Language Recognition. This has resulted in the following material benefits. • Reduction of Labor – Automating certain credit transactions that have traditionally been completed manually allows for those human resources to focus on other, higher-value processes that require human intervention or are not otherwise good candidates for automation. • Increased data accuracy – Removing the opportunity of human error through automation improves data quality. This, in turn, improves end-user confidence and overall decision-making. • Decrease in Cost – Automation of certain credit processes and procedures, such as Parametric (intraday) limit calculations, retail real-time credit evaluations, and portfolio scenarios, reduces costs in several ways. For example, the number of analysts required to assemble data to run intraday exposure position or approving retail energy customers has been substantial for companies that have automated these historically manual intensive processes.