How RPA and AI Revolutionize the Banking & Finance Industry

Emerging technologies wield major influence on the contemporary business environment. Robotic process automation and artificial intelligence are the two state-of-art technologies that steadily transform the financial sector. These technologies create unparalleled opportunities for banks, accountancy companies, insurance enterprises, and investment funds. Entrepreneurs, who leverage RPA and AI software into their business practices benefit from automated processing and precise financial forecasting, personalized financial services for the customers, accurate analytics, customer data management, and efficient fraud protection.

What is the Difference Between RPA and AI?

The majority of people perceive RPA and AI as identical technologies, yet they are distinct from each other, and it’s crucial to understand which specific benefits each technology provides. Robotic process automation is a type of software, which is designed exclusively for automation of tedious and monotonous functions and does not correlate to human intelligence. On the other hand, AI software is dedicated to solving problems that would require human cognition, like recognizing certain patterns, learning, self-improving from previously processed data, and making future forecasts. Regardless of their divergence, in some cases, both technologies may be combined to produce intelligent automation and create the utmost efficiency for financial services.

Let’s have a closer look at how RPA, AI, or their combination can benefit the financial sector.

The Key Benefits of Implementing RPA in Financial Services

Automated Processing of Financial Services

The overwhelming majority of financial institutions still heavily rely on manual processes, which makes them inefficient, creates unnecessary expenses, and increases the probability of errors and fraud. Implementing RPA can exclude these issues and facilitate both backend and frontend business operations. A perfect example of RPA in banking is accounts payable processing, which is a monotonous task and does not require any intelligence, only extracting, validating and processing certain data. Optical character recognition (OCR) reads the data, sends it into the RPA system, which then approves it, completes the payment, and notifies the workers in case of errors. Moreover, RPA can optimize other kinds of services that are used by financial institutions on daily basis, like generating financial statements, reconciliation of account balances. Implementing RPA may also significantly accelerate the general documentation flow, account closure requests and report automation processes.

Implementing RPA in finance can notably optimize credit card application processing. It has the ability to interact with various systems at once, and validate different types of data, like background and credit checks. Most importantly, RPA functions on a set of pre-based rules and is able to accept or reject the application. RPA application potential does not end at better credit card processing. It may also be applied to other aspects of credit management sections, like underwriting services for potential borrowers.

Furthermore, RPA offers effective protection against financial cyber threats. The technology benefits fraud analysts by automating a broad spectrum of processes, like blocking or reissuing breached accounts, changing the account restriction criteria, automatically scanning negative files for latest updates and more.

Other examples of RPA in banking include Know Your Customer optimization. KYC is an obligatory procedure for every bank customer, and according to the Thomson Reuters survey, each year banks spend approximately $500 million on KYC compliance globally. With RPA, the cost of manual KYC processing will be dramatically reduced, and customer data will be evaluated with improved accuracy and minimal errors. Due to its obvious benefits, the future of RPA in finance looks promising. The overall RPA revenue is steadily growing and is expected to increase in the course of the next six years.

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RPA and Advanced Accounting

Accounting and asset management is of critical importance for any type of financial institution. Currently, most of the financial enterprises function on legacy systems, and employees have to process the data manually. RPA may act as an intermediary and streamline the accounting processes. A good example of RPA in accounting and finance is the general ledger that has to be constantly updated with the latest treasury management financial data concerning the firm’s assets, expenses, revenues, and liabilities. The information is extracted from the legacy systems, and it is then verified by financial specialists. The whole process is very time-consuming and results in financial errors. RPA has the ability to integrate data from numerous legacy systems and efficiently process it, having no faults. The integration and interaction of legacy systems is one of the key advantages of applying RPA in financial services.

Integrating legacy systems is rather costly and labor-intensive, but by applying RPA, financial institutions significantly improve the speed and continuity of operations, while minimizing the number of errors.

RPA in finance is also beneficial in any other type of data management. Different departments and divisions keep the records of transactions in journals that need to be consolidated. RPA system can assemble and consolidate the transaction data and store it in your enterprise resource planning system. This creates advantages not only for accountants who can focus on tasks that are more significant, but also for executives who will receive financial insights much faster.

How Does Artificial Intelligence Disrupt Financial Services?

Personalized Financial Services and AI Chatbots

Chatbots are AI-based programs that are able to process human language, understand the user’s requests, maintain a conversation and respond to the customers on the basis of the organization’s business rules. Due to the self-learning capabilities of artificial intelligence, chatbots become more sophisticated and suitable for a wider range of business applications. In terms of financial services, AI chatbots can enable the clients to check their balance, transfer funds, make online payments, allow customers to find profitable investment options, and more. Apart from these functions, what are the most significant benefits that chatbots provide, and why do financial enterprises rush to adopt this technology?

Cost-efficiency and Extended Sales

AI chatbot may function in a fully automated way, and depending on its development complexity, it can solve various kinds of customer issues. This excludes the need for more call center agents and allows financial enterprises to spare their funds. Most importantly, chatbots in banking can be programmed to focus on personalization, which allows making accurate recommendations and proposals for the clients. The proposals may include valuable financial offers, which may result in increased customer engagement and raise the number of opened bank accounts or the use of other services.

Scalability and 24/7 Customer Support

An advanced AI chatbot has the ability to function on multiple platforms and be available to assist the customer around the clock. Moreover, simultaneous conversations with the users allow achieving any scalability. According to a recent survey by LivePerson, 67% of customers prefer interacting with chatbots that provide customer support, due to their fast and efficient issue settlement. One of the key advantages is that chatbots in the banking industry may be designed as both voice assistants for customers who prefer to call, or as messaging assistants with personalized content that will instantly respond to the client’s request.

The major benefits of chatbots that are most appreciated by the users are:

• Round-the-clock customer support;
• Getting an instant response concerning the user’s issues;
• Quickly answering simple questions that can be easily resolved;
• Simple way of communication.

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Successful Use Cases of Chatbots in Banking

Erica and Bank of America

Bank of America has recently begun using a chatbot named Erica. The AI bot is available via a mobile application and it is programmed to support customers in their daily banking services. The most used functions of Erica include transfers between the customer’s accounts, transaction processing, bill payment, or even blocking a customer’s credit card if necessary.

Multilingual HSBC Hong Kong Chatbot

Amy is an advanced multilingual AI chatbot in a customer service platform at HSBC Hong Kong. Clients of the bank can access Amy via the official website and use the bot for various kinds of financial services or request support in terms of mobile banking. Amy is able to speak Chinese (both Mandarin and Cantonese) and English. She is able to identify different types of dialects and paraphrases due to sophisticated Natural Language Processing (NLP).

Artificial Intelligence and Accurate Financial Forecasting

Financial institutions, particularly investment companies, are reckoning upon the teams of data scientists who determine the possible market development patterns. Yet, the majority of financial companies perform forecasting via Excel spreadsheets and require assistance from other branches, like sales or finance operations, which decreases their efficiency. Another significant issue is that prediction methodologies are variable, and manual forecasts are often biased and subjective, which means that the predictions are frequently inaccurate. AI in financial services can observe past patterns and anticipate their future development. Furthermore, AI is able to learn the triggers that cause pattern deviations, which improves the forecast precision even more. The AI financial forecasting ability is very diverse, ranging from investment predictions up to stock rates, financial claim, demand prediction, and both long and short-term revenue anticipation.

Artificial intelligence in finance can also be used to improve social media analysis and customer’s behavior forecasting. In combination with cognitive computing, AI in finance can be used to gain insights about social media behavior of the customers, by analyzing their feedback, comments, preferences, and dislikes. By processing the received data on the customer’s behavior, AI can generate personalized advice and exclusive offers, which are more likely to be accepted by a specific customer. Marketing departments often use this approach, for example, before launching a new ad, AI can analyze inputs from previous marketing campaigns and forecast the best possible offers for the clients.

Using AI to Combat Financial Fraud

Cyber fraud is a significant issue in the contemporary financial industry, specifically for the banking sector. Due to rapid technological development, digital fraud attacks are getting more sophisticated, easier to execute and harder to detect. Banks are now a subject to various kinds of cyber threats like fishing, malware, spam attacks, credit card, and identity fraud. Moreover, the financial TMS and ERP systems are highly predisposed to various kinds of ransomware attacks. The overall harm from cyber fraud leads to significant financial losses, the inability to pay salaries, disburse the suppliers, and the loss of customer’s loyalty. Artificial intelligence in banking may be efficiently implemented to enhance cybersecurity departments and safeguard corporate assets and customer data.

Innovative Approach to Data Analysis

One of the most prominent advantages of leveraging AI in banking is that it’s able to process immense amounts of information and track questionable transactions in real-time. This feature greatly improves operational efficiency, because identifying advanced patterns is often a hard task for data scientists. Similar uses of artificial intelligence include the analysis of different factors, like the customer’s location, the device used, and other numerous contextual data to create an accurate picture of a specific transaction. This method enhances real-time fraud disclosure and improves the customer’s data protection.

Profound Reduction of False Positives and Negatives

A false positive is now a common term for financial enterprises, which denotes situations when authentic transactions are treated as fraudulent, are declined, and then the customer’s financial account is suspended. On the contrary, a false negative is a situation where a fraudulent transaction is verified and confirmed as valid. Artificial intelligence is capable to analyze large sets of data, including connections between various entities, and outline vague fraud patterns that may remain unseen by data scientists, thus significantly reducing both false positives and negatives. Application of artificial intelligence in financial services excludes the time-consuming process of reworking declined transactions and falsely rejected accounts, and allows allocating employees into other prioritized tasks.

Conclusion

Robotic process automation and AI are the two cutting-edge technologies that have the potential to utterly transform the sphere of financial services. They offer exceptional opportunities to accelerate numerous business processes and exclude time-consuming manual work. By leveraging AI and RPA entrepreneurs can streamline accounting, efficiently assemble and consolidate data, significantly reduce the expenses from different business branches, create an outstanding customer experience, round-the-clock support, and substantially reduce cyber fraud. In combination, the benefits of AI and RPA create a prominent competitive advantage, which will inevitably result in the growth and prosperity of your enterprise.

Infopulse has a strong expertise in working with a broad spectrum of large-scale AI and Data Science projects, which include HR AI solutions, sophisticated virtual assistants, personalized chatbots, RPA solutions, and precise financial forecasting AI tools. Contact our team for further guidance in terms of adoption and development of efficient artificial intelligent solutions that will help you to achieve your business goals.