‘Automation is good, so long as you know exactly where to put the machine.’ - Eliyahu Goldratt, Author, and Business Philosopher
The global financial crisis (2007-2008) was disastrous for a majority of professionals in banking and finance. As the sector recovered, automation software helped save time on operational tasks. Gradually coming to be known as Robotic Process Automation (RPA), it grew to eliminate almost all 'repetitive tasks'.
As technologies evolved, one question that surfaced was 'How can automation get smarter?'
Enter: Artificial Intelligence (AI). Teamed with RPA, it automates complex data-intensive tasks.
Notably, RPA and AI can be accommodated in existing infrastructure without a full sweep. By thinking big, starting small, and scaling fast, most financial entities could make use of contemporary technology rollouts with fewer roadblocks.
Intelligent Automation: Wide-reaching AI Capabilities for Large Scale Applications
The fusion of RPA and AI – termed intelligent automation (IA) looks promising for accelerating processes. According to a Forrester report, intelligent automation will release $ 134 billion in labor value in 2022. So, how does IA work?
To begin, let's look at its components and functions. Artificial Intelligence (AI) utilizes information gathered from various sources and feeds that information to tools used for Robotic Process Automation (RPA). The framework is mostly the same for all IA-based processes.
This merger which forms Intelligent Automation (IA) majorly enables automation at larger scales and accelerates complex workflows. It creates solutions using a technological knowledge base to streamline interactions between multiple, otherwise disconnected applications.
Majorly today, IA is used for -
Intelligent document processing - Extract, validate, and process unstructured data using AI tools like Natural Language Processing (NLP)
Process discovery - Create complete guides for automation in RPA-based processes
Streamlined workflows - Automate interconnected workflows to increase efficiency
As seen in today’s banking and finance sector, the duo is iconic in tackling problems at large scales. To unchain IA's true potential, developments are based on considerable research. Consequentially, high costs for its development and integration demand that Financial Entities consider all possible use cases while drafting blueprints for its inclusion.
Intelligent Automation in Banking: Complete Overhaul of Backend and Data Processes
Covering an ocean of opportunities by building seamless experiences in automating end-to-end operational work, IA has reached new heights. In all major industries, IA is capable of automating almost all back-office and data management tasks.
In the banking sector, IA marked its presence by enabling banks to easily carry out automated data sourcing, data verification, data processing, complex analytics, and reporting. The benefits of Intelligent Automation (IA) for Banks and Financial Entities are -
Minimized Operational Costs
Technology is in its golden era. The potential for operational cost savings is colossal. As here, advanced interoperability for any banking activity helps with cost-efficiency.
RPA extracts data and collates information at large scales, and AI addresses difficulties in data cleansing and aids in the creation of reports for comprehensive insights. Apart from delivering a massive overview of workflows, the duo makes it possible to –
Automate hyper-personalized customer interaction processes at every level with dataflows for real-time data
Build databases and highlight customer behavior predictions in segments like collections and marketing
It is often difficult for banks to trace all the transactions flagged for possible fraud. Although, RPA can track transactions and raise red flags for possible fraud transaction patterns in real-time. In an interconnected systems network, AI is used for advanced know-your-customer (KYC) processes and new-age Anti Money Laundering (AML) safety procedures.
In the mentioned cases, with RPA, the capabilities of AI such as machine learning, deep learning, speech recognition, natural language processing (NLP), and visual recognition, highly enhance audit abilities and real-time traceability respectively.
Risk Supervision and Reporting
RPA can be used to trail full audits and other pivotal processes for mitigating risks and maintaining compliance. RPA in risk management can be primarily seen in the areas of risk monitoring, risk control, and risk reporting.
In tandem, AI quickly spots patterns in large and unstructured datasets. This has a huge potential to enhance the speed and accuracy of crime detection. It also works to automate and enhance data-intensive activities in regulatory reporting, thereby lowering risks whilst reducing costs.
For Risk Reporting, AI makes it possible to build comprehensive and detailed risk analytics. Its ability to add variables to the analysis process, and to compute at unreal speeds improves risk calculating systems exponentially.
Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.