How Intelligent Automation Is Going to Improve Customer Experience, Employee Productivity, Security, and Compliance in the Financial Services?
Automation delivers value when manual tasks within the enterprise that are repetitive are automated. This increases productivity and reduces errors.
Automation can be split into various categories, such as basic, process, integration, and robotic process automation. Each of these types of automation uses different techniques, tools, and processes to cater to various levels of automation of tasks, workflows, and operations within the enterprise.
Tasks like filling up forms automatically, online customer support, digitization of backend processes, streamlining protocols that employees need to follow, and integrating applications with data and devices are some examples of enterprise automation.
Automation Becomes Intelligent
Robotic process automation is a type of automation where software tools and technologies are trained to simulate repetitive tasks. Then these tasks are played back to automate the same said task.
Intelligent automation is the result of combining robotic process automation with artificial intelligence (AI), machine learning, and natural language processing (NLP) to automate complex tasks within an organization. Intelligent automation systems can analyze and correlate large amounts of data, learn on their own from previously stored actions, and make complex data-driven decisions.
Since intelligent automation makes it possible for enterprises to automate complex tasks, it enhances and speeds up business processes, reduces errors, and increases efficiency and productivity.
Intelligent Automation Gains Momentum in the BFSI Sector
Digital transformation has accelerated automation in the Banking, Financial Services, and Insurance sector. There are very large enterprise systems in this sector catering to customers globally in real-time.
Efficiency, productivity, and costs are key metrics that business performance depends on, and IT teams are adopting tools and technologies such as intelligent automation to streamline processes across the enterprises in this sector.
The BFSI sector has been an early adopter of intelligent automation and has incorporated this technology in very sophisticated and complex systems.
Helpline and Customer Support
Online chatbots are now powered with intelligent automation using AI and ML to provide high-quality 24/7 customer support by way of answering basic queries and frequently asked questions (FAQs).
Backend Operations
All BFSI sector business enterprises have had manual-intensive legacy backend operations. With the adoption of intelligent automation, many of the repetitive tasks have been automated, and human intervention has become nominal, thus improving efficiency. This inclusion of automation frees up human resources for more high-value work.
Fraud detection
Intelligent automation can process and analyze large amounts of data. Using the inherent power of AI and ML, intelligent automation can be used to detect fraudulent transactions such as money laundering, identity theft, and credit card fraud by early detection of patterns and suspicious activities.
Onboarding, KYC, and Validation
Onboarding, KYC, and validation are common activities across the BFSI sector. These activities were being done manually, consuming a lot of time and effort. Now with automation charged with data using AI, OCR, IDP, and ML, all these processes can be automated easily.
Risk Assessment, Loan and Claim Processing, and Disbursements
Assessment of borrowers, loan processing, and paperwork before actual disbursements are lengthy and complex tasks. However, with automation powered by AI and ML, these tasks can be automated to a large extent without the risk of manual errors. This helps in increasing productivity and facilitating faster response times.
Audits and Compliances
Financial audits are complex processes involving all sorts of finance-related documents and their dependencies. With AI and automation, much of this complex overload can be reduced, and the audit can be completed faster. Regulatory compliances and documentation can be extremely long and tedious processes, but if automated with a dose of AI and ML, these can be completed quickly and without errors.
Intelligent Automation and IT Infrastructure Prerequisites
For intelligent automation to be deployed in any enterprise, the IT infrastructure has to be brought to a certain level of performance and standardization. The various technology stacks need to be seamlessly integrated, and clear communication channels need to be established across all systems and their dependent components.
Relevant data required for the AI and ML components of intelligent automation should be easily accessible along with high storage capacities. The more data available, the more effective will be the state of the automation. The data must also be comprehensive and well-structured.
The cloud business model and the “as a service” subscription should meet the scalability demands of increasing storage capacity, processing power, and upgrades required to expand intelligent automation across the enterprise.
The enterprise’s security posture has to be of the highest level of governance to take the required precautions against vulnerabilities that might get introduced into the enterprise due to the adoption of intelligent automation.
Besides, intelligent automation needs robust planning and IT support during its adoption and deployment. A dedicated IT team will be required to monitor the intelligent automation systems deployed across the enterprise.
AIOps and Infrastructure Management
As indicated, it is necessary for the infrastructure of the enterprise to be maintained at high levels of readiness and performance for the effective deployment of intelligent automation. These levels of sophistication and performance can only be maintained with the introduction of AIOps into IT operations.
AIOps (Artificial Intelligence for IT Operations) is a process that can be used by IT teams to monitor IT systems in real-time, provide insights, and issue alerts of potential issues before they become critical. This ensures that the state of the IT infrastructure is being primed at all times to ensure that sophisticated processes such as intelligent automation have the required environment to work effectively.
With the capability of quick root cause analysis and automated remediation of AIOps, the operational state of the IT infrastructure is maintained at a very high level, thus satisfying one of the key requisites of deploying intelligent automation systems.
The success of intelligent automation is in its continuity of operations. AIOps, with their predictive analysis capabilities, can ensure the smooth running of the IT infrastructure so that the automation process can uninterruptedly complete its task iterations.
AIOps, Intelligent Automation, and IT Operations – All Link to Enterprise Productivity
Enterprise automation is becoming increasingly popular in various industries, especially in the BFSI sector. Intelligent automation, which combines robotic process automation with AI, ML, and NLP, is being used to automate complex tasks within an organization.
This automation technology can analyze and correlate large amounts of data, learn on its own from previously stored actions, and make complex data-driven decisions. This explains why intelligent automation is being used in helpline and customer support, backend operations, fraud detection, onboarding, KYC, validation, risk assessment, loan and claim processing, disbursements, audits, and compliance.
However, for intelligent automation to be deployed effectively, the IT infrastructure must be brought to a certain level of performance and standardization. AIOps is necessary to maintain the enterprise infrastructure – as the success of intelligent automation depends on its continuity of operations.
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