RPA integrated with chatbots
to offer exceptional customer service

Client Profile

A 15-year old mid-sized banking and financial services company headquartered at Richmond and operating in the Southeastern United States.

The Backstory

Unlike most other players in the market, the client didn’t have an efficient customer service system in place. It was leaking revenue and customers were losing confidence, setting the alarm bells ringing in the company.


Substandard workflow caused manual errors and the inefficient processes involving countless steps demanded urgent attention. Chatbot and email support topped the priority list and were zeroed in on for the first phase of the corrective operation.


Chatbot integrated in RPA improved Resolution accuracy, and Reduced TAT and Human involvement.

The traditional chatbot being used was inefficient with an accuracy rate of just around 30%. The rudimentary functionalities were not enough to perform tasks like data pulling from the database or CRM due to the unavailability of APIs. This rendered the system unsuitable for simple queries like application tracking or information on financial products. To provide a resolution, the bot had to escalate the case to an agent for asynchronous communication, resulting in long TAT and poor customer experience.

The old bot was replaced with an AI chatbot with natural language processing capabilities and integrated with RPA for delivering more transactional service. Now, if a customer starts a conversation with the chatbot requesting for, say the interest rate of savings, the chatbot asks for their details such as account number and login credentials and verifies the same. On successful verification, the chatbot triggers an RPA bot, acting as the back office assistant.

The RPA bot, on receiving the command, ascertains the location of the information, be it a database or CRM or any other back-end enterprise system, retrieves the information, compiles it in a predefined format, and shares it with the customer either via the chat window or an email. A consolidated report of the daily activities is sent to the stakeholders at the end of the day. For suitable cases, the bot also redirects the individual to the appropriate knowledge repository that assists in customer self-service.

In 10% of the cases (down from the 30% six months previously) when escalation to an agent is the only option, the bots route it to an agent to facilitate synchronous communication.

  • Accurate resolution – Customer satisfaction and cost savings
  • Ability to handle queries at scale – Shorter TAT
  • No human agent involved – Employee focus on core areas
Pre RPA Post RPA
Manpower 4 1
Case resolution accuracy 30% 97%
Average TAT 3 hours 6 minutes
Customer support operating costs High Reduced by 22%

Email support

RPA implemented in mailbox accounted for Precise email classification, Reduced operational footprit, and Auto-assigning of tasks.

For queries and complaints through emails, the lack of automation necessitated the deployment of a dedicated human resource for monitoring the mailbox at regular intervals, retrieving emails with customer requests and complaints, classifying those as per the query type, and forwarding them to the respective departments. It resulted in high costs and too many human errors.

RPA was implemented to check the mailbox for customer emails after every 60 minutes. On receiving one, the bots call a Machine Learning module that can read the mail body text, identify the query type and category, and assign a number (from 1 to 5) depending on the case priority and severity (1 for the lowest and 5 for the highest). Based on this number, the RPA bots triage the email to the concerned individual. The bots assign a ticket number to every email and share the same with the client for future correspondence. For case follow-ups, bots read the ticket number to pull case history details from the database to further actions.

For queries needing no judgment (such as account statement), the bot performs the steps as listed under problem 1 and shares the information with the customer over the email.

  • Precise email classification – Better triaging of queries
  • Reduced operational footprint – Low operating costs
  • Auto-assign tasks to employees – Quality resolution
Pre RPA Post RPA
Accurate email triaging 67% 94%
Average TAT 24 working hours 15 minutes
Manpower 2 NIL

Technology Stack

  • UiPath Robots
  • Google Tesseract
  • .Net
  • ML

The primary benefits of RPA include substantial savings on operating costs, process efficiency, and reduced dependency on human workforce. Additionally, the application of RPA doesn’t require the setting up of APIs and it doesn’t interfere with the core coding of your pre-existing legacy system.

Businesses across industries have leveraged the potential of RPA to scale to the next level. If you aren’t sure of the applications of RPA in your organization and its enormous benefits, connect with us for a revealing discussion on the possibilities.