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Staying relevant in today's digital technological world means every company must be technology first and innovation-driven. The future of such innovative solutions involves Chatbots and Conversational AI. While enterprises are quick to adapt solutions to build an engaging experience, backend processes and capabilities are often neglected. Valuable time is spent parsing through reports and dashboards. Cloud, AI, and Data have taken the traditional way of doing business to the next frontier of strategy and business intelligence.
But with all the offerings around still a lot of questions are left to be answered:
Why invest in a Chatbot/ Conversational Interactive Assistant?
Difference between a Chatbot and an AI-based Chatbot?
What is their intended purpose? And how 'smart' are they?
By 2022, 70% of employee interaction will be through Conversational AI.
Chatbot v/s Conversational AI-based Interactive Assistant
Chatbots are primarily rule-based, linear-driven interactions that can be navigated by predefined flows. Rule-based chatbots have limited processing capabilities and must be pre-trained to handle any variations in questions. In technical terms, they are referred to as flow-charts run by If-else statements lacking higher-level cognition. Customers often feel the frustration of limited chatbot solutions and their narrow scope.
Traditional chatbots are:
Rule-Based and Keyword Driven
Regularly updated manually to optimize bot performance
Having Navigation- focused interface with no capacity of learning
Conversational AI bots provide a more human-like experience, carry on a natural conversation, and continuously improve over time. It helps people interact with systems faster and helps businesses deliver customized, unique solutions and support at scale. Consumer spending via AI-based Chatbots is expected to reach $142 billion by 2024.
Conversational AI works by separating sentences down to their root level, dealing with human language, and recognizing that there is data to be parsed. It identifies statistically significant patterns, what the user is trying to achieve, and trains on them. This can be a single word or a combination, or a complex series of patterns in a single phrase. The goal then is matching a user's intent to a predefined task or question.
Features of Conversational AI bots:
Fast response time, highly accurate, and easily customizable.
24/7 availability, which significantly reduces the need for tickets and monitoring queues.
Self-service is provided across multichannel, including IVRs, and offers new data sources on customer behavior, language, and engagement.
Minimal upfront investment required deploys rapidly, delivers responses in seconds, eliminates wait times, and quickly reduces support costs.
How is Conversational AI leading the Fintech Innovation?
The banking sector had the highest market savings (77%) using Conversational AI
Anomaly and Pattern Detection: Sifting through large amounts of data and detecting patterns is crucial for an AI. To improve in areas like Fraud Prevention, pattern recognition, financial service providers integrate chatbots into websites and mobile applications. Gaining insights from customer's behavior, grasping the unordinary, performing high-value data analysis to prevent frauds, notifying customers of suspicious activities are few applications where the use of an AI chatbot has proved to be successful.
Data-Driven Recommendations and Process Optimization: Conversational AI can analyze financing statements, risk-levels, and recommend investments. Fintech chatbots can be used to notify users of recurring payments. Using machine learning and image recognition, companies can integrate chatbots to scan and parse loads of documents and take further actions based on laws and regulations.
Increase in speed of Financial Services: Traditional payment solutions that take several days for execution are already seen as too slow and unacceptable. Fintech companies are looking to integrate innovative financial technologies and enable global transactions in real-time. Companies have introduced AI-based chatbots with payment options to allow economic interactions. An enhanced feature like voice-recognition has helped in omitting repetitive actions and saving users' time.
Streamlining Regulatory Compliance : Constant adoption of new rules and standards for regulatory compliance has forced many fintech players to look for time and cost- efficient ways to conform to these changes. Bank cost savings to reach $7.3 billion by 2023 as automated customer experience evolves. Artificial intelligence can recognize and predict hidden security risks, make compliance with eKYC easier and faster. NLP technology can help examine new regulation documents and highlight the required obligations to help companies improve ROI and minimize human intervention.
Customer Service Automation: 24x7 availability and NLP and AI make conversational chatbots help financial businesses resolve complaints and issues instantly. Taking on mundane tasks and answering simple questions allows employees to focus on more challenging requests and strategic activities. By 2022, Banks can automate 90% of their customer interactions through chatbots. Implementation of chatbots increases savings and improves the level of service in terms of response time, consistency of query resolution.
AI chatbots cause disruption and lead cost savings of almost $1.3 billion by 2023 in the insurance claims management sector.
To stay at the center of innovation and advancement, your innovation journey should align with your corporate goals and be a part of your high-level strategy. Fintech’s that introduce machine learning and AI-based Chatbot technologies into different company processes will have a strong competitive advantage in the long run.
Want to know more about this, you may read through our related blog based on this topic. NSEIT is amongst the early adopters of the intelligent solutions as we understand the BFSI industry closely and have been empowering them to be future ready. Go to Business Transformation and understand how you can emancipate from conventional business process approach and step ahead in your your Digital Transformation journey.
Assistant Manager, Presales and Solutioning
Shikhar is a part of the Presales and Alliance team at NSEIT - working with senior leadership on identifying and growing the strategic alliances in digital transformation practices disrupting the Banking, Financial Services, and Insurance space. He’s an MBA from the Great Lakes Institute of Management, Chennai, and takes a keen interest in new-age digital and analytics developments.
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