Evolution of customer assistants in BFSI
Background
Technology is reshaping our world in all possible manner across all sectors and industries. The more manual processes exists in a given business process the more we have scope and opportunity of automation and innovate. Talking about manual processes, probably the Banking and Financial Services Industry is ahead of everyone in this regard and very rightly so because there are workflows which are individualistic and not generic for eg- when a given customer raises a query and expects a quick resolution from a specialist and truly this specific use case is not restricted within BFSI but really across businesses.
The Trigger
When we talk about assistants it can be built for just about anything say — invoice generation, ticketing processes or customer assistance. Lets focus on the banking and financial sector only and only on customer assistants as of now. A bank or any wealth or financial institution can have millions of customers subscribing to a wide variety of services with multiple combinations. And we all have our own set of questions, queries, doubts, complaints and suggestions. On top of it, the customer base is a diverse dataset hence it is only obvious that their approach will be differently spread as well. In other words, any financial institution needs to hire highly experienced set of experts for various lines of businesses and geographies to address and resolute the customer issues timely and efficiently. It is now plain and simple to understand that any reasonable bank has to allocate a significant amount of budget just to satisfy a tiny part of their client servicing business function and hence finding a potent and pertinent solution was the need of the hour for them.
The Journey
The below figure talks about a general progression of the customer assistance framework within banking and financial industry.
In the earlier days, it was all done in-person then as per the technological development took place, the evolution happened. However, all the communication channels still exist and available to customers based on the complexity of the issue. So far it is all straightforward and maybe more or less known to everyone. The real story maybe starts maybe with evolution of instant messaging technology in the late 90s or early 2000s or rather with the digital revolution.
- All of a sudden, companies and individuals are willing to have a digital gateway for continuous communication and engagement with simple to use approach. So instead of queuing calls for customer cares or sending lengthy emails or travelling to a bank branch isn’t necessary anymore as long as customer avails for their digital channels with the only prerequisite of a stable internet connection.
- Then the next improvisation comes with the amalgamation of Robotic Process Automation with the instant messaging server. Although we are not discussing the intricacies of RPA but need to explain the concept just a little. In a layman’s language connections get created with databases and various other data sources and then formulate actions based on events. In other words, RPA works on Divide & Conquer mechanism. Let me take a simple example —
a) Customer message in chat-bot => RPA checks account balance -> (rule:- if <50K redirect to Desk X, if >50K redirect to Desk Y, if >100K redirect to Desk Z)
b) Customer gets prompts with bunch of possibilities — check account balance, forgot pin, reset existing pin, lost credit card etc.
c) Customer selects forgot pin => forgot pin task gets triggered.
In a nutshell, RPA works on scheduled tasks based on inputs and corresponding data repository. - Last but not the least, with the advent of machine learning and Natural Language Processing, banks are designing intelligent chatbots where the experience is human-like. Chatbots are now intelligent enough to understand the customer linguistic and intent, accordingly perform action and return with a response. Let me give you an example.
A customer would type in say — “I want to check my account statement”, the chatbot is intelligent enough to understand the intent, retrieve the required report and returns to the customer. This is just a simple example of a use case but the scope is vast. With AI in place, the reduction in dollar value for any Financial Institution is immense as mass hiring or training of SMEs isn’t a monetary overhead anymore for the firms.
Conclusion
The global chatbot market was valued at more than $3.5 billion and is expected to grow further a CAGR of 30.29% by 2027. Chatbots are also not restricted with texting, voice notes are being introduced. Banks are also gauging the customer sentiment analysis to identify negative possibilities upfront. Before I close, only can say that all banks and financial institutions are investing heavily in AI/ML developments and thus it is almost assured to witness innovative solutions down the line which will further disrupt the status-quo in Banking and Financial Services Industry.
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