Leveraging AI in Kotak Securities

AI in FinTech: A Powerful Combination

AI, a branch of computer science that simulates human intelligence, encompasses machine learning, natural language processing, and data analytics. In the financial domain, AI technologies are leveraged to enhance efficiency, improve decision-making, and deliver exceptional customer experiences.

Use Case 1: Sending Automatic Statements using RPA

One of the notable applications of AI in finance is the automation of routine tasks through Robotic Process Automation (RPA). In this use case, AI-driven RPA plays a pivotal role in streamlining customer service processes. At our financial institution, we have implemented a solution that automatically responds to customer inquiries requesting statements, such as Client Master List (CML) or Demat Account Holdings.

How it Works

• Machine Learning Identification: AI algorithms analyze incoming customer emails to determine if they pertain to statement requests. By understanding the context and intent of the email, the system identifies whether the customer is seeking specific financial statements.

• Customer Segmentation: The system further categorises the request by associating it with the customer's unique identifier and the type of statement requested, such as CML or DP Holding.

• Handover to RPA: Once the email is categorised, the AI system seamlessly hands over the task to a Robotic Process Automation (RPA) bot. The RPA bot retrieves the requested statement and generates a personalised response, including the attachment.

This AI-powered automation expedites customer service and reduces manual workload, resulting in faster query resolution and improved customer satisfaction.

Estimated Impact

• Reduces the query for agents by 30%

Use Case 2: Internal Chatbot for Customer Service Agents

Another significant application of AI in finance is enhancing the capabilities of customer service agents. At our organisation, we have developed an internal chatbot that acts as a virtual assistant for customer service representatives.

Key Features

• Information Retrieval: The chatbot empowers agents to quickly access essential information, including pricing plans, product details, and escalation points. Instead of searching through extensive documentation, agents can simply chat with the bot to obtain the required data in real time.

• Training Acceleration: New agents benefit from the chatbot's training capabilities, which provide guided onboarding and access to knowledge resources. The bot can answer queries related to products, services, and company policies, reducing the learning curve for recruits.

• Efficiency Boost: By automating information retrieval and simplifying training processes, the internal chatbot allows agents to focus on more complex tasks and deliver more efficient customer service.

Conclusion

AI's role in reshaping the financial industry is undeniable. From automating customer service responses to empowering agents with instant knowledge access, these real-world applications are just a glimpse of the transformative potential of AI in finance. As the FinTech landscape continues to evolve, embracing AI-driven solutions will be the key to staying competitive, enhancing customer experiences, and driving innovation in the financial sector.