IT automation delivers plenty of excitement for the financial services industry, with its power to make AI even more intelligent, as AI plays a part in today\u2019s automation journeys. <\/p>\n\n\n\n
As banks and other financial services providers make more and more digital moves forward, the modernization of core functions becomes more critical, with change starting in the IT department with the support it provides. For example, IT teams improve their organizations\u2019 employee experiences by using chatbots and conversational AI to facilitate service requests that support employees\u2019 IT-related needs, like unlocking accounts and resetting passwords. Here, automation complements AI by fulfilling those requests, speeding up mean time to resolution (MTTR), and setting up automations to execute common service requests \u2013 driving self-service to improve employee productivity. <\/p>\n\n\n\n
Automation in IT is critical for financial service providers and banks to survive as the speed of change revs up. Customer demands and operational excellence requirements are far from easy to meet, making the need to augment automation with AI clear, as AI fuels fast innovation. <\/p>\n\n\n\n
The financial services industry needs solid innovation that packs a punch. The idea of automation, AI and machine learning (ML) for enhanced banking is becoming more of a foundational strategy than a new, cutting-edge way to accelerate digital transformation. Financial services providers today must open their minds to what\u2019s possible, especially in terms of operationalizing AI. <\/p>\n\n\n\n
AI and automation as a team create new levels of capacity for IT. Automation enables IT to optimize processes and move faster while minimizing risk. From a business perspective, it\u2019s about using automation and AI for fraud detection, as just one example. In the back office, AI and automation provide key support and intelligence to improve the organization\u2019s productivity. <\/p>\n\n\n\n
IT automation, AI, and robotic process automation (RPA)<\/strong> together are also very relevant to financial services. The global robotic process automation (RPA) in the BFSI (banking, financial services, and insurance) field\u2019s market size should reach $8.79 billion by 2030, Grand View Research<\/a> found. <\/p>\n\n\n\n Early adoption of these technologies has been the heaviest in large global banks of Europe and the U.S. with the spending power to experiment. But once the tried-and-true methods became known, smaller banks with digital-only models quickly caught on. They topped other challenger banks by being early adopters of new technology. Industry leaders make it work by connecting application programming interfaces (APIs) and enterprise systems. <\/p>\n\n\n\n Automation and AI empower the financial services industry to effectively automate operations with the potential to completely transform the business, by reducing the time manual tasks take, lowering costs, simplifying employee workloads and more. <\/p>\n\n\n\n AI will continue to empower automation and likely hand new, incredible opportunities to IT teams as digital transformation drives change throughout financial services. The hype of both AI and automation as a pair is demonstrated every day as leading IT teams use them together to stay ahead of change. <\/p>\n\n\n\n No matter where your IT team and their organization might be in their journey to digital transformation and the ultimate modernization of services offered, the potential of an automation and AI team is endless. The following three use cases are popular picks among the crowd that top financial services firms rely on to meet customer demands and outdo the competition. They\u2019re also ideal places to start. <\/p>\n\n\n\n 1. Building an Automation Pipeline Using Ticket Data <\/strong><\/p>\n\n\n\n The value derived from automation can be exponentially increased by treating it as a strategic IT-wide initiative. There\u2019s so much data already at hand in the form of IT tickets, but an ideal starting point is looking for insights that can maximize automation return on investment (ROI). AI and machine learning (ML) can analyze service desk ticket data, understand high-volume and high-impact tickets, and help point IT in the right direction as they begin or scale automation initiatives. <\/p>\n\n\n\n Resolve, in doing so, runs IT tickets through our proprietary Resolve ML models, which examine the language from each ticket and displays grouped opportunities for automation \u2013 with the goal of building automation pipelines. <\/p>\n\n\n\n In turn, ROI estimation allows for costs to be associated to each group of automation, and it visualizes the potential value and savings your business can experience. <\/p>\n\n\n\n DISCOVER MORE: <\/strong>The Resolve Automation Flywheel: A \u201cGood to Great\u201d Automation Journey<\/strong><\/a> <\/p>\n\n\n\n 2. Real-time Incident Remediation Using Historic Data <\/strong><\/p>\n\n\n\n Automation has earned a permanent, increasing role in financial services with its ability to make things easier for employees and customers alike. But where can IT tap into automation \u2014 the topic of the hour \u2014 and make the most of its abilities when paired with AI? <\/p>\n\n\n\n Along with AI, ML and large language models (LLM), IT can use Generative AI (GenAI) to resolve issues using historical data and provide recommendations for how to do so. <\/p>\n\n\n\n Resolve offers a way to use ChatGPT to actually take data from a ticket and tell IT what it should do based on the historical data it pulls, with a desired result of leveraging the data to determine next steps. In a use case like unresponsive web applications<\/strong>, the ticket appears on your screen to notify you that a web app is unresponsive. <\/p>\n\n\n\n Resolve will pick up the ticket once you\u2019ve changed just a couple properties. By saving the \u201cnew\u201d ticket, Resolve would pick it up for this particular outage and automatically start the remediation process. The ticket state would be set to \u201cactive\u201d while below, in the work notes, its information automatically populates. Resolve would find any and all tickets similar to the one at hand that are marked either \u201cclosed\u201d or \u201cresolved\u201d and then list them. <\/p>\n\n\n\n Resolve would take the closed notes and closed codes from the listed tickets, formulate a question, and ask ChatGPT for steps to solve the active ticket. Closed notes might include clearing the cache, emptying the recycling bin, emptying the C: drive, and the notes would be naturally and organically constructed text. <\/p>\n\n\n\n 3. Modernizing Legacy Scripts Using AI Script Converter <\/strong><\/p>\n\n\n\n Many banks still use legacy systems and technology that\u2019s decades old. In fact, more than 800 billion lines of COBOL code<\/a> (a programming language from 1959) still run on mainframe systems every day. Supporting digitization of core banking applications requires marrying the old and the new, with an urgency for adopting automation to uphold business priorities and to prepare for the future. <\/p>\n\n\n\n IT teams have been using scripts to automate tasks for a long while now, but as mentioned above, GenAI can create a new world of efficiency and effectiveness. Resolve enables IT teams to orchestrate their existing scripts. The Resolve Actions<\/a> platform can integrate with Ansible playbooks for example, to automate network configuration or chef or puppet scripts for CI\/CD-related activities. <\/p>\n\n\n\n Taking it a step further, the Resolve Actions Exchange houses the AI Code Translator that turns existing scripts into Resolve Actions tasks, allowing you to build a library of automations that can be reused securely and consistently. <\/p>\n\n\n\n The translation functionality integrated in the AI Code Translator is powered by a single Action task, designed to deliver a customized prompt to OpenAI\u2019s GPT-3.5 LLM. It provides you with a streamlined solution for translating code or scripts from their source language into Groovy, for instance. It\u2019s a functionality that aims to modernize legacy scripts. <\/p>\n\n\n\nHow Can AI Augment Automation Along the Journey? <\/strong> <\/h3>\n\n\n\n