Intelligent Automation could be the solution to the financial sector’s current pressing need to depart from conventional, antiquated business structures on a global scale.

Intelligent automation (IA), also known as cognitive automation, is the use of automation technologies, such as business process management (BPM), robotic process automation (RPA), and artificial intelligence (AI), to scale and streamline decision-making throughout businesses.

Intelligent automation has several uses and can be used to streamline processes, free up resources, and increase operational effectiveness.

For instance, an automaker might employ intelligent automation to speed up production or reduce the likelihood of human error, while a pharmaceutical or life sciences organization might utilize it to cut costs and improve resource efficiency in situations where repetitive procedures are present. An insurance provider can utilize intelligent automation to handle compliance requirements, compute payments, and create forecasts used to compute rates.

Intelligent automation (IA) is made up of three cognitive technologies. The combination of these elements can produce a service that drives technological and commercial transformation.

  • With Artificial intelligence (AI), businesses can create a knowledge base and make predictions based on data by analyzing organized and unstructured data using machine learning and sophisticated algorithms. This is how IA makes decisions.
  • Business process management (BPM), commonly referred to as business workflow automation is another element of intelligent automation. Workflows are automated by business process management to increase the consistency and agility of corporate processes. Most industries utilize business process management to promote communication and engagement while streamlining operations.
  • Robotic automating processes (RPA) with robots is the third element of IA. Robotic process automation uses software robots, sometimes known as bots, to carry out back-office operations like data extraction and form filling. These bots work well in conjunction with artificial intelligence since RPA can employ AI insights to tackle activities and use more complicated cases.

Here are some use cases of AI in the financial sector.

Identifying Opportunities and Risks

Finding out about market opportunities after they’ve already passed might be problematic for traders.

When it comes to trading decisions, AI finance tools can be faster and more accurate than human traders. Additionally, thorough market analysis enables banks to push the boundaries of trading algorithmic performance. These days, many renowned hedge funds employ AI for these goals. The technology is fairly popular for data science because it aids in the development of a company’s trading system.

In financial services, artificial intelligence has a significant impact on risk analysis and investment management. AI can provide a precise assessment of the client’s creditworthiness and provide the important clarification, “Is this individual trustworthy?” By taking into account transaction and credit history, income growth, market conditions, etc., the AI-based system analyses risks. To decide if investments should be made, predictive analytics delivers a wealth of information about micro-activities and behavior.

Detecting Cases of Money Laundering

Large amounts of personal data are handled by banks and other financial institutions in addition to handling people’s money. The greatest risk in this sector is a fraud, which can result in unimaginable losses, issues, and liabilities with just one error. When we talk about fraud, we’re talking about financial crimes like credit card fraud and money laundering.

Fraud detection is the main objective of AI in financial services. AI helps identify questionable activity, adds another layer of security, and reduces fraud.

Simply put, AI enhances bank security.  AI techniques can identify money laundering using specialized algorithms in several ways. In essence, these algorithms sift through enormous amounts of data and raise an alert if they discover anything suspicious, such as unexpected transactions or account activity.

There are many ways AI can detect shady behavior. A customer’s transactional behavior can be analyzed by AI to anticipate future behavior from that user. This system adapts to behavioral changes, no matter how minor, and can detect any suspicious behavioral changes that conventional AML systems might overlook.

Additionally, AI can improve know your customer and customer due diligence procedures, enabling both of them to be completed more quickly and thoroughly. For AML purposes, AI can give financial institutions access to a wider variety of consumer data that can be used for risk analyses, suspicious activity reports, and investigational needs.

The capacity of AI to automatically create suspicious activity reports is one feature that makes the use of AI in AML a no-brainer. These reports go through an internal reporting procedure before being submitted to the appropriate authorities. When dealing with potentially suspicious activity, AML workers can use algorithms to pre-fill reports with pertinent data and standardize language and terminology, saving them important time.

The Processing of Loans

#1. Digitizing Customer Data Stored in Paper Documents

To accurately express the information contained in paper documents, machine vision systems for document digitization with AI need cameras that can produce high-resolution photos of the originals. A digital version of the written text can also be produced using optical character recognition (OCR). The updated digital copy of a document can then be stored in the database of the business.

The following types of papers could be digitized by machine vision and used by banks to facilitate lending and underwriting:

  • Purchase and sale contracts.
  • Paper or scanned PDF applications for mortgage loans.
  • Physical and scanned PDF checks.
  • Bank statements for customers proving their banking and credit history.

If all of these kinds of documents could be converted to digital format, productivity for wealth managers might increase. This is because the bank’s intranet would provide access to all the information required to qualify and onboard new loan customers. They wouldn’t have to leave their desks or comb through filing cabinets to find the information they need to refer to while making crucial business decisions like looking up a candidate’s credit history.

To be as effective as possible, this strategy would still require that the newly digitized material be simple to search.

#2. Organizing and Tagging Data for Accessibility and Traceability

Companies may be able to search their intranets for archival customer data and information from recently digitized documents using AI technologies for text mining.

Natural language processing (NLP) technology is frequently used in applications of this kind because it can interpret text included in documents and link it to search terms. An accurate search across all document types could be made possible by text mining software once it has been integrated into a bank or financial institution’s database.

Applications for text mining could potentially link the metadata that is associated with database documents to search terms. Consider a wealth manager, for instance, who is actively searching for earlier loan agreements from particular clients. Within a specified duration, the software could remove all documents except loan agreements with specific consumers.

Optimized Sales and Customer Service

#1. Chatbot-Based Customer Support

Today’s customer service personnel are required to respond to numerous customer calls daily. They must also work to cut down on the typical resolution time for each customer. Both of these problems can be solved in part by using chatbots.

Chatbots can not only respond to consumer inquiries in real-time and with lightning-quick accuracy, but they may also lighten the workload of human customer service representatives by handling a large volume of customer inquiries.

#2. Around-the-Clock, Around-the-Year Support

Customers prefer a flexible service. All year long, brands must be accessible to consumers and responsive to their needs. This may be made possible through automated customer support.

Automated customer support enables businesses to provide always-on customer care and handle issues as they come up. Customers may now have their questions answered 24 hours a day without having to wait a long time to hear back. This would not only greatly improve customer satisfaction and customer service, but also enhance brand reputation and foster more customer loyalty.

#3. Improved Human Interactions with Customers

AI can play a significant part in enhancing human interactions with clients. AI-augmented messaging and AI email tagging are two of the major ways AI is enhancing customer service. Customer care representatives can handle a significant portion of customer inquiries with the aid of chatbot assistants thanks to AI-augmented messaging.

By utilizing AI-powered systems to scan emails, tag them, and direct them to the appropriate office, AI email tagging allows humans to save the time needed to read every customer’s email. The customer care representatives would be able to focus on the more difficult jobs that require human interaction while also saving time.

A highly accurate prediction of human behavior might be made by an AI having access to vast amounts of data and computing power. Businesses may make the most of the consumer data they have collected by utilizing deep learning. Deep learning integrated with marketing presents numerous options for marketers. Because of the precise forecasting of consumer behavior, companies can now predict what their customers will buy before they do.

Risks of Automation in the Financial Sector

Here are some of the risks associated with the misuse of automation in the financial sector.

  • Technology – Bad bot design could affect the current IT infrastructure. On the other hand, ordinary adjustments to an IT platform may impact automation solutions.
  • Regulatory compliance – Automation mistakes can lower the accuracy of regulatory reporting, increasing the risk of legal infractions and penalties such as fines.
  • Operations – Poorly designed automation systems may increase processing errors. Increased operational inefficiencies may arise from insufficiently effective oversight mechanisms.
  • Talent – If communications to employees don’t emphasize the higher level job they’ll be able to execute with RPA results, morale may suffer. To prevent and spot abuse, access to and control over automated operations must be strictly handled.
  • Financial reporting – Harm to an organization’s reputation can come from improperly implemented finance and accounting robotic process automation.

The Future of Automation in Finance

The purchasing of products and services, tax accounting, accounts receivable, invoice processing, managing cash, and managing internal bank accounts are some areas in finance that are expected to undergo considerable improvements. Not to mention the advantages that automating the quote-to-offer, source-to-pay, and order-to-cash processes could bring. If you happen to be a business-owner and haven’t already, you should consider incorporating automation in your IT strategy right away.

Amazon’s success in upgrading its finance division shows the value of putting your workers at the center of your automation strategy. By accepting a variety of learning styles and allowing everyone to own their own upskilling experience, they created the ideal atmosphere for speedy but thorough automation deployment.

 

Are you getting hyped about the introduction of intelligent automation in the financial sector? Let us know in the comments down below.