Hyper-Intelligent Automation: Simplify Finance & Accounting
SEP 16, 2024
Most fintech companies are heavily dependent on mechanical systems to conduct their operations, and the digital skillfulness of their finance and accounting (F&A) department determines where the entire firm stands in terms of finances. Ever-changing regulations and market turmoil can be a nightmare for the company’s Chief Financial Officers (CFO), let alone the changing consumer needs and geopolitical drifts. There’s a chance of so many errors due to the involvement of manual labor, which can consume your time. On the other hand, robotic process automation in finance can finish the same tasks in a short span of time with minimal errors. Because of that, there is an undeniable need for artificial intelligence and machine learning applications in finance to be incorporated into organizations to make your systems smarter and more conscious. This technology needs to be securely installed in your infrastructure, and the best way to make that happen is through hyper-intelligent automation. Let’s discuss the potential of hyper-automation in financial operations and see how it can assist in risk management, optimizing resource usage, personalizing customer journeys, and much more!
Role of Hyperautomation in Finance and Accounting
Hyperautomation technology is one of the flag bearers of the Industrial Internet of Things (IIoT) or the fourth industrial revolution, Industry 4.0. Hyperautomation in finance is a business-driven approach that requires identifying patterns and anomalies for making accurate decisions. It refers to integrating RPA in financial services, intelligent business management software, machine learning, data analytics, and artificial intelligence in finance within your systems so that they can conduct processes on their own with contextual decision-making skills. This can turn out to be a productive approach as automated systems handle end-to-end tasks tirelessly and kill the need for human intervention. In the customer-centric era, it is evident to provide end-users with errorless solutions within the defined timeline, as hyper-automation in financial operations is going to be the most agile method in financial reporting automation to fire up the customer retention rate.
How does Hyperautomation operate in Finance?
1) NLP and predictive analytics with AI/ML tech
Artificial intelligence and machine learning applications in finance use NLP for processing human language and advanced computer algorithms to mimic human decision-making skills and make systems smart enough to adapt to changing conditions. This technology uses predictive analysis to gather insights from collected data and formulate the most optimal strategy to move ahead.
2) Robotic Process Automation for mundane tasks
Robotic Process Automation in accounting is the execution phase of automation, while artificial intelligence is the brains behind it. It contributes to data fetching from accounts and processing it with AI algorithms. RPA in financial services forms the base of intelligent automation in finance and hyper-automation. The AI engine assesses the data and transactions and detects any suspicious and fraudulent activities.
3) Process mining for defining future state processes
Process mining monitors the efficiency of the existing methodology and finds out how it can be improved with its knowledge-growing ability. It models the current state of the system, assesses the improvements required with the Japanese 5S methodology, conducts deviation and performance analysis, and forecasts the required improvements in the current process.
4) Accurate forecasting with Business Intelligence
Data warehousing, data visualization, OLAP, and financial reporting automation tools are used in business intelligence for making data-driven decisions. Business intelligence in finance manages associated risks, tracks performance, creates optimal budget requirements, and predicts the change in consumer behavior while adhering to the laws and guidelines of legal compliance.
Benefits of Hyperautomation in Finance
1) Manages risks in advance and remains compliant
With accurate data collection, data analysis, and detailed reporting, hyper-intelligent automated systems create comprehensive digital audit reports and adhere to regulatory-compliant requirements. Accurate reporting, notifications, and periodic audit alerts help prevent fraud or cyberattacks that can penetrate the systems containing sensitive consumer data.
2) Improves productivity with less resource usage
Hyperautomated intelligent systems have the capacity to learn from their own mistakes as well as find faster ways to enter and retrieve data. They can respond to requests quickly and identify rule-based tasks that can be easily automated. They free up resources that can be utilized for other important tasks and cut down costs on resource allocation.
3) Personalizes customer journey with CRM tools
Customers look for more personalized and convenient transaction processes while banking. CRM tools improve their money lending process by offering competitive loan terms, and that enhances customer retention rates as they benefit from repayment plans. Through transaction history and feedback surveys, they spot trends and align their policies to consumer needs.
4) Reduces errors through accurate decision-making
Decision-making is more about accuracy than going with an expert’s gut feeling. Due to quick access to data in real-time with data analytics, it becomes easy to track the performance of the system and make the entire process more transparent with hyper-automation in financial operations. Hyperautomation lays out a true reflection on what improvements need to be made through detailed reports.
5) Scales as per the evolving market demands
Your business may need to keep up with the changing market trends. With the shifting market changes, hyper-intelligent automated systems have the properties to stay in sync with transformations and scale efficiently. By monitoring and auto-remediation tools, you can keep track of your scaling configurations and take the necessary steps directly.
Use Cases of Hyperautomation in Finance
1) Approves the loans quickly
Hyperautomation tools reduce manual errors by using user-intelligent OCR and machine learning to extract and validate data. From data collection and credit report analysis to income verification and final approval, hyper-automation automates the entire loan process. It involves no use of manual paperwork that can elongate the process.
2) Handles claims cognitively
Hyperautomation speeds up reconciling tasks and data verification to provide a pleasant experience to the clients. It consolidates information from multiple systems and manages communication happening through emails. It analyzes data in various types, namely audio, images, and text, using cognitive automation to extract useful information.
3) Safeguards data with KYC
KYC Portal CLM simplifies the integration by providing a secure API that connects systems with third-party applications. The dynamically synchronized data makes it easier to monitor and manage risks. Hyperintelligent systems can automate background operations and conduct them across internal and external systems to detect and respond to cyber fraud.
4) Anti-money laundering
The traditional AML framework is tricky and involves labor work where they track transaction details and review historical alerts. Hyperautomation uses robotic process automation in accounting and artificial intelligence in finance to automate alert generation and transaction reviews. Apart from that, it validates data accurately as well as personalizes compliance training.
Challenges for hyper-automation in Finance
1) Initial investment
In the initial weeks, organizations have to spend time and money to understand how the technology works. They will have to set up workshops and training sessions to make their staff familiar with hyper-intelligent automation tools that can temporarily halt their in-house work.
2) Data security
Customers are doubtful that automation can’t keep up with the evolving techniques of cyberattacks and may stall authentic user traffic. They still need to be convinced that intelligent automation in finance can cope with advanced persistent attacks and protect their data.
3) Lack of expertise
Due to the talent gap, artificial intelligence cannot be successfully integrated into existing systems. The lack of knowledge makes it difficult to install automation tools, develop AI models, and use data to its full potential. It also hampers the return on investment in AI technology.
4) Data quality
Managing inconsistent formats from multiple sources, filling gaps in datasets, resolving duplicate records, and dealing with inaccuracies are common hurdles for automation tools. Maintaining data quality requires the collaborative efforts of human experts and technology.
Top-class AI and advanced automation expertise for smooth integration
With hyper-intelligent automation systems, fintech businesses can check all the boxes of the payment lifecycle, like extracting data, anomaly detection, preventing fraud, solving discrepancies, and staying compliant with regulations. They can understand where their business stands and where it is headed with hyper-intelligent automation. But the real trouble lies in the initial phase, where this technology needs to be integrated within the legacy systems. It requires an expert team with finesse in artificial intelligence and advanced automation.
Being one of the best artificial intelligence and machine learning service providers, Webelight Solutions Pvt. Ltd. provides advanced automation services that redefine the fundamentals of software development while meaning quality assurance at its best. Robotic process automation in accounting, intelligent process automation (IPA), business process automation (BPA), automated data processing, workflow automation, custom automation solutions, AI automation, automated testing, and QA are some of our advanced offerings. We will ensure a smooth integration of automation with a sprinkle of AI and ML into your existing infrastructure.
