Intelligent Automation in Action

Intelligent Automation is a key asset in improving the customer experience. It can transform the way businesses operate internally, elevating the customer experience. According to Forrester, 25% of the Fortune 500 companies will add Artificial Intelligence building blocks to their RPA efforts in 2020.6
In the following four use cases, we explain how Intelligent Automation can improve customer communication, smart system integration and mailroom and claims management.

Customer communication

Consumers expect to communicate with companies across a variety of channels ranging from social media to text messages. But conventional channels such as phone calls and paper-based forms are still going strong. Manual processing is no longer enough to deal with the increasing volume and velocity of customer requests. Companies are also struggling to cope with a hybrid environment where customers may switch between physical and digital channels, yet still expect the same seamless experience across all formats.

This situation is made more difficult by the fact that customers are used to interacting with digital platforms like Amazon or Google in their everyday life. They now demand the same levels of convenience and flexibility whether they are talking to their bank, arranging a doctor’s appointment or ordering insurance.

IA can help companies to meet these challenges and to find the balance between cost and performance in their customer service operations. IA takes customer communication from an array of unstructured sources. It automatically scans the digital documents and identifies the language and the customer need. It can then extract relevant data, such as the customer or transaction ID, and enrich it by pulling it from back-end systems such as enterprise resource planning (ERP) and customer relationship management (CRM) applications. Finally, it filters and routes the cases to the right people. IA can present these queries in an easily digestible format for human operators, or can even use RPA to automate information processing still further, minimizing human intervention and adding yet more speed to the process. By centralizing this extracted information, companies can also respond across a range of channels, choosing the most appropriate one for an individual customer’s preferences.

This solution is ideal for companies that receive a large volume of customer communication via email or other digital channels and are having trouble responding in a timely manner. By combining automation with a manual workforce (located in onshore of offshore locations) companies can maximize operational efficiency. The key is a well designed process whereby automation handles the majority of communication requests, with exceptions and complex cases routed to skilled operators.

Figure 3: IA powered customer communication

Figure 3: IA powered customer communication

Smart system integration

Legacy information systems are a burden for companies, weighing them down and hindering innovation. But they are also a reality. Many companies are working on mainframes that are decades old. It’s expensive and time consuming to re-platform, and resources are often limited. RPA can act as the bridge between legacy systems and new, modern software applications. Rather than having to build out new interfaces, RPA can extract information from one system and move it to another. It’s fast to implement and – crucially, does not involve replacing the legacy systems. Instead, RPA can sit on top of the current architecture.
RPA is well suited to tasks such as customer registration and address changes by automatically categorizing data-change requests before forwarding them to the appropriate mechanism. It enables faster data updates at lower costs, and can manage master data properly. By breaking down silos, it ensures a ‘single source of truth’ for enterprise information. When Artificial Intelligence is added into the mix, tasks that are more complex can be automated, such as changing customer credit limits, which may require integrating and analyzing data from multiple sources, as well as third parties.
Before IA, companies would struggle to build customized interfaces that could exchange data between these older systems and newer applications. Or, they would rely on employees to manually extract and reformat data in spreadsheets. Both approaches are costly, slow and prone to mistakes. The ideal approach combines automation, skilled workers and process optimization in a single solution.

Figure 4: Smart system integration

Figure 4: Smart system integration

Mailroom automation

Businesses running large omni-channel mailrooms, such as banks, insurance companies, telecommunications operators and government institutions, deal with massive volumes of incoming communication each day. Clients use multiple channels, such as paper mail, web forms and emails. These volumes are irregular, peaking according to seasonal trends, increasing the complexity of managing unstructured information.

IA can solve this processing problem by applying Artificial Intelligence to extract and process data around the clock. Automated scanning of postal mail, combined with digital processing of email and web communications, enables IA to classify incoming documents and extract data from them. This eliminates manual intervention at least 80% of the time.

This rapid sorting and classification enables the mailroom to forward different kinds of requests such as applications, transactions, claims and customer care issues to the right people quicker than manual processes can. RPA can move information between different systems automatically, without the need for employees to access a variety of different databases. This creates more efficiency and improves performance, enabling departments to meet their service level agreements more readily.

Crucially, the physical and digital channels are not treated as separate silos. Both are fully integrated into the solution, so the customer receives a consistent quality of service even if they change between channels. For example, you could email your phone company asking for a hard copy of the latest bill and receive it by post.

Figure 5: Intelligent mailroom automation

Figure 5: Intelligent mailroom automation

Claims automation

Claims management is a very difficult process to automate because it involves processing documents with complex and nuanced information such as pictures of damaged cars. Insurance companies face large volumes of claims each day from customers that demand fast results. It’s a top-priority process that has a profound effect on the customer experience. And with each customer making claims rarely, insurance companies often only have one chance to get it right and maintain the customer relationship.

All too often, employees become bottlenecks for claims processing due to high volumes and seasonal fluctuations. Artificial Intelligence can constantly analyze and categorize claims far faster than human employees, filtering each case into the right sub-process. In many cases, it handles claims without human intervention, running through a completeness check to ensure that all the necessary information is there. A full fraud detection process using third-party data to filter out malicious claims can follow this. Once a claim has passed all of those checks, back-end systems can process it instantly, providing a faster refund for the customer and a better experience overall. IA may have to hand some claims off to a human operator, but even in those cases it often extracts enough data to forward it to the right individual, reducing the cost of manual processing for the few cases that still require it.

Figure 6: Intelligent claims automation

Figure 6: Intelligent claims automation


Today's customer experience challenge

Chapter 1

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Discover More about Today's customer experience challenge

Introducing Intelligent Automation

Chapter 2

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Discover More about Introducing Intelligent Automation

The benefits of Intelligent Automation

Chapter 3

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Discover More about The benefits of Intelligent Automation

Overcoming Implementation Challenges

Chapter 4

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Discover More about Overcoming Implementation Challenges

Intelligent Automation and SPS

Chapter 6

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Discover More about Intelligent Automation and SPS

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