At the turn of the 21st century, the IT sector witnessed an unprecedented growth in RPA and AI. The main reason for this was because of the steep rise in demand for RPA solutions that were being deployed across various industries and verticals. Today, most businesses look to automate tasks that are repetitive, time-consuming, and manual, thus speeding up their business processes.
With the number of BFSI opportunities growing exponentially, it is crucial that organizations analyse their business processes, leverage technology, and adopt machine learning to streamline operations.
The banking and finance sector has a lot of large-scale tasks to complete, which are slow and archaic. These processes take up massive amounts of time and manpower, therefore leading to lower productivity and efficiency. Luckily, there is a solution that can help change this scenario, namely using Robotic Process Automation (RPA) combined with Artificial Intelligence (AI). In this blog we'll discuss the benefits RPA and AI can bring to the BFSI industry.
Benefits of Implementing RPA in BFSI Industry
Banks and other financial firms can use RPA in their front, middle, and back offices. The appropriate implementation partner must be chosen based on the company's needs.
Even though most financial institutions rely heavily on manual processes, these types of operations are inefficient and create unnecessary expenses. RPA can eliminate these issues by automating back-office functions such as accounts payable processing. OCR reading data, sending it into the RPA system and approving payments; Optical character recognition (OCR) reads the data sent from OCR and creates reports for workers to review if errors occur during processing. Moreover, RPA can also accelerate documentation flows by automating processes like generating financial statements, reconciliation of account balances and closing or suspending accounts.
RPA has the potential to increase efficiencies in credit card processing by automating many of the processes that were previously performed manually by humans. RPA has the ability to communicate with many systems simultaneously and validate a variety of data kinds, including background and credit checks. In addition, RPA functions on pre-defined rules and can accept or reject an application based on their findings.
RPA also provides strong defence against online financial risks. It automates a broad spectrum of processes like blocking or reissuing breached accounts, changing account restriction criteria, and scanning negative files for latest updates.
Additional instances of RPA in the banking industry include Know Your Customer improvement. KYC is a mandatory procedure for every bank customer. RPA will significantly lower the cost of manual KYC processing, and it will increase the accuracy and decrease errors in the evaluation of customer data. As a result, RPA has the potential to increase revenues for banks in the future.
Accounting and asset management are crucial to any financial institution. Currently, most financial enterprises process data manually, but technology could streamline this process by replacing humans with machines. One example of how technology has revolutionized an industry is through its ability to integrate multiple legacy systems and generate reports that are accurate and complete.
Financial institutions often have trouble integrating legacy systems, but by using RPA they can significantly improve the speed and continuity of operations while minimizing the number of errors.
RPA can help speed up the process of consolidating transaction data. RPA systems can assemble and consolidate data from different sources, which enables accountants to focus on more significant tasks that require more time. Executives can also receive financial insights much faster by using an RPA system.
Benefits of AI in BFSI Industry
Accurate Financial Forecasting
Financial institutions, including investment companies, are using data scientists to determine market development patterns. However, they are often hindered by inaccurate forecasts performed manually and by the time it takes for them to change their methods. AI can provide accurate forecasts because it learns from past patterns and predicts future changes based on those patterns. It also helps financial institutions predict future sales levels, claim rates and demand predictions as well as long-term revenue predictions.
Artificial intelligence can be used to improve social media analysis and forecasting of customer behaviour. AI can be used with cognitive computing to gain insights about the customers' behaviour, including feedback and comments, preferences, and dislikes.
By processing received data on the customer's behaviour, AI can generate personalized advice, exclusive offers, and recommendations from marketing departments. Marketing departments often use this approach before launching new ad campaigns and forecast best possible offers for clients.
Cyber Fraud
Cyber fraud is currently a significant issue for the banking sector. The fast pace of technological development, coupled with the ease of transmitting threats through social media and the internet, are driving this type of fraud to new heights. Banks are increasingly becoming targets for various kinds of cyber-attacks, including fishing, malware, spam attacks, credit card, and identity fraud.
This type of attack is especially favoured by organized criminal groups and cyber terrorists using malware and spam. Banks must also contend with ransomware attacks--which often result in extortion attempts--and other types of ransomware. The overall harm from cyber fraud leads to significant financial losses; these losses result in reduced assets for banks which can ultimately harm their ability to pay salaries as well as disburse suppliers on time.
Some measures already exist to prevent these types of attacks, but they require a high level of investment in terms of time and money; Artificial Intelligence could be used to help create effective solutions.
Artificial intelligence in banking may be efficiently implemented to enhance cybersecurity departments while safeguarding corporate assets and customer data via preventive measures such as zero-day detection technology or encryption algorithms.
Data Analysis
By leveraging Artificial Intelligence, banking operations can be greatly improved. AI enables the identification of questionable transactions in real-time, which allows the organization to respond efficiently to any advanced patterns that might be discovered. For example, if a customer is using multiple devices to make purchases in different locations, AI can identify this pattern and alert authorities. This type of fraud detection is important because it allows organizations to protect customers' privacy while simultaneously providing them with more accurate fraud disclosures.
False Positive and False Negative
False positive and false negative are terms used in the financial industry to describe the difference between valid and fraudulent transactions. Both terms refer to situations where financial institutions treat authentic transactions as fraudulent, decline them, and then suspend the customer's account. Artificial intelligence can be applied to financial services because it can analyse large sets of data, including connections between various entities; it outlines vague fraud patterns that may remain unseen by data scientists; and it allows allocating employees into other tasks that are more important than processing declined transactions or falsely rejected accounts.
Final Thoughts
Robotic Process Automation and Artificial Intelligence are two revolutionary technologies that are revolutionizing several business sectors. They offer businesses many advantages, including the ability to accelerate certain processes, streamline accounting and improve customer service, eliminate manual work, and consolidate data, significantly reduce expenses from different business branches and create a customer experience that is second to none. In combination, these benefits create a prominent competitive advantage that inevitably results in the growth and prosperity of your enterprise.
CIGNEX has extensive expertise in working with a range of large-scale AI and Data Science projects, including HR AI solutions, virtual assistants, personalized chatbots, RPA solutions and precise financial forecasting tools. Contact our team for help with the adoption and development of efficient artificial intelligent solutions to help you achieve your business goals.
