Emerging technologies have a major impact on the business environment of today. Robotic Process Automation and artificial intelligence are the two state-of-the-art technologies that constantly transform the financial sector. New innovations create unique opportunities for banks, accounting firms, insurance firms, and investment funds. Entrepreneurs who incorporate RPA and AI technologies into their business practices benefit from automated analysis and accurate financial forecasting, personalized consumer financial services, accurate analytics, customer data management, and efficient fraud protection.
Many people perceive RPA and AI as similar technologies, yet they are distinct from each other, and recognizing the specific benefits that each technology provides is crucial. Robotic process automation is a type of software that is designed to automate repetitive and monotonous processes exclusively and does not correlate with human intelligence. On the other hand, AI software is committed to solving issues involving human cognition, such as identifying such patterns, learning, self-improving from previously processed data, and making future predictions. In some situations, irrespective of their separation, both technologies can be combined to deliver smart automation and generate maximum efficiency for financial services.
Let’s now take a closer look at how the financial sector will benefit from RPA, AI, or their combination.
Automated Financial Service Delivery
The overwhelming majority of financial institutions still rely heavily on manual processes, making them inefficient, creating unnecessary spending, and raising the likelihood of errors and fraud. Implementing RPA can rule out these issues and facilitate business operations on both the back and the frontend. A perfect example of RPA in banking is the processing of accounts payable, a monotonous task that requires limited intelligence except to extract, validate and process certain data. Recognition of optical character (OCR) reads the data, sends it to the RPA system, which then approves it, completes the payment, and notifies workers in the event of errors. In addition, RPA can optimize other types of services that are used on a daily basis by financial institutions, such as financial statement generation, account balance reconciliation. Implementing RPA may also dramatically speed up the overall data flow, requests for account closure, and processes for report automation.
Implementing RPA in finance would significantly simplify the processing of requests for credit cards. It has the ability to interact at once with different systems and verify various data forms, such as history and credit checks. Most notably, RPA works on a collection of pre-based rules, and the specifications of what can be accepted or rejected. The scope for RPA implementations does not stop with improved handling of credit cards. It can also be extended to other credit monitoring elements, such as providing services to potential lenders. In addition, RPA offers effective protection against cyber-financial risks. The platform assists fraud analysts by automating a wide range of procedures, such as blocking or reissuing infringed accounts, modifying account limitation conditions, automatically checking negative files for the latest updates and more.
Several examples of banking RPA include increasing the customer’s understanding. KYC is a mandatory practice for each bank customer, and according to the Thomson Reuters report, banks spend about $500 million on complying with KYC worldwide each year. RPA can significantly reduce the cost of manual KYC analysis and analyze customer data with improved accuracy and reduced errors. Because of its obvious advantages, RPA’s financial future looks promising. Overall revenue from RPA is growing steadily and is expected to rise over the next six years.
RPA and Accounting Professional
Accounting and asset management for any type of financial institution is of critical importance. Most financial firms are currently working on legacy systems, and employees have to process the data manually. RPA can act as an intermediary and streamline the processes of accounting. A real example of RPA in finance and accounting is the general ledger has to be constantly updated with the latest financial treasury management data; on the assets, expenses, revenues and liabilities of the company. From the legacy systems, the information is extracted and then verified by financial specialists. The entire process takes a lot of time and leads to financial errors. RPA has the ability to integrate and efficiently process data from numerous legacy systems without any faults. One of the main advantages of applying RPA in financial services is the integration and interaction of legacy systems. Integrating legacy systems is rather expensive and labor-intensive, but using RPA, financial institutions significantly improve operating speed and continuity while minimizing the number of errors. Any other type of data management also benefits from RPA in finance. In journals that need to be consolidated, different departments and divisions keep records of transactions. RPA system can assemble, consolidate and store transaction data in your business resource planning system. This creates benefits not only for accountants who can focus on more important tasks but also for executives who will receive much faster financial insights.
Personalized Chatbots and Financial Services
Chatbots are AI-based programs that, based on the organization’s rules, are able to process human language, understand user requests, maintain conversations and respond to customers. As an example, chatbots are versatile and suitable for a broader spectrum of business applications, thanks to the self-learning capabilities of artificial intelligence.
Cost-efficiency and sales expansion
AI chatbots can run fully automated and can solve a range of customer problems, depending on its implementation complexity. The need for more call-center agents is removed, and financial companies should spare their assets. Above all, banking chatbots can be configured to concentrate on personalization, allowing accurate feedback and customer recommendations. The chatbot customer recommendations can include valuable financial incentives, which may lead to increased interaction with consumers and increase the number of opened bank accounts or other services.
Scalability and 24/7 service for customers
An advanced AI chatbot can work on multiple platforms and be available around the clock to assist customers. In addition, any scalability can be achieved by simultaneous conversations with the users. According to a recent survey by LivePerson, due to their fast and efficient problem solving, 67 percent of customers choose to communicate with chatbots that provide customer support. One of the key advantages is that chatbots in the banking industry can be configured either as voice assistants for customers who prefer to call, or as customized information messaging assistants who will respond to the customer’s request immediately. The main advantages of chatbots that users love most are:
- Round-the-clock customer support
- Immediate response to the user’s problems
- Quick response to simple questions that can be easily solved
- Easy communication
Erica and the Bank of America
Recently, Bank of America began using a chatbot called Erica. The AI bot is accessible through a mobile application and is designed to assist clients in their day-to-day banking services. Erica’s most commonly used features include transfers between customer accounts, transaction processing, bill payment, or even blocking the credit card of a customer if appropriate.
The multilingual chatbot of HSBC Hong Kong
Amy is an advanced multilingual AI chatbot on HSBC Hong Kong’s customer service site. Bank customers can access Amy through the official website and use the bot for various types of financial services or request mobile banking support. Amy can speak both Chinese (Cantonese and Mandarin) and English. Thanks to sophisticated Natural Language Processing (NLP), she can identify different types of dialects and paraphrases.
Data Scientists and Financial Intelligence
Financial institutions, particularly investment firms, rely on data scientists teams to determine the possible trends of market growth. Nonetheless, most financial firms are forecasting through Excel spreadsheets and need assistance from other divisions, such as sales or finance operations, which decreases their performance. Another important issue is that methodologies of prediction are variable, and manual forecasts are often biased and subjective, meaning that forecasts are often inaccurate. AI is able to observe past patterns in financial services and predict their future development. In addition, AI can learn the causes that cause pattern deviations, further improving forecast accuracy. The ability to forecast AI financially is very diverse, ranging from investment predictions to stock prices, financial assertion, demand prediction, as well as long-term and short-term sales forecasting.
Artificial financial intelligence can also be used to enhance the study of social media and the actions of customers. In conjunction with cognitive computing, AI in finance can be used by analyzing their reviews, tweets, interests, and dislikes to gain insights into customers ‘ social media actions. AI can produce personalized advice and exclusive deals by analyzing the data received on the actions of the customer, which are more likely to be accepted by a specific customer. For example, before launching a new ad, AI can analyze inputs from previous marketing campaigns and predict the best possible offers for customers.
Cyber fraud is a major issue in today’s financial industry, especially for the banking sector. Digital theft attacks are becoming more complex, easier to execute, and harder to detect due to rapid technological development. Banks are now subject to cyber threats of various kinds, such as fishing, ransomware, spam attacks, credit card fraud, and identity fraud. In fact, the financial TMS and ERP applications are strongly predisposed to various types of attacks on ransomware. The overall damage done by cyber fraud leads to substantial financial losses, inability to pay salaries, disburse vendors, and loss of customer loyalty. Artificial banking intelligence can be successfully applied to improve departments for cybersecurity and protect corporate assets and customer data.
One of the most prominent advantages of using AI in banking is that it can process huge amounts of information in real-time and track questionable transactions. This feature greatly enhances operational efficiency, as it is often a difficult task for data scientists to identify advanced patterns. Similar use cases of artificial intelligence include the analysis of various factors, such as the location of the customer, the device used, and numerous other contextual data to create an accurate picture of a particular transaction. This approach improves the detection of fraud in real time and increases data protection for the consumer.
A false positive is now a more common phrase for financial firms, which signifies cases where genuine transactions are regarded as fraudulent, are rejected, and the financial account of the consumer is then suspended. On the contrary, where a fraudulent transaction is verified and confirmed as valid, a false negative is a situation. Artificial intelligence is capable of analyzing large data sets, including connections between different entities, and outlining vague patterns of fraud that data scientists may remain unseen, thereby significantly reducing both false positive and negative. Artificial intelligence deployment in financial services avoids the time-consuming cycle of reworking rejected transactions and misrepresented accounts and enables workers to be assigned to other priority tasks.
Automation of robotic processes and AI are the two cutting-edge innovations with the ability to change the financial services environment entirely. We offer excellent incentives to speed up various business processes and exclude manual work that takes time. Through leveraging AI and RPA entrepreneurs can streamline accounting, effectively compile and integrate data, significantly reduce spending from various branches of business, generate outstanding customer experience, help round-the-clock, and significantly reduce cyber fraud. The advantages of AI and RPA combine to create an influential competitive advantage that will inevitably lead to your business ‘ growth and prosperity.
