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The Role of AI in Indian Banking and Retail
The future of banking and retail holds many possibilities but none are as inevitable as the
widespread adoption of AI (Artificial Intelligence). Revenue would be expected to open up as
new business horizons which haven’t been explored earlier will be open to business insights.
Personalisation will follow, with businesses saving on operational costs by not having to invest
in many resources for detailed follow-ups and interactions. This personalisation will lead to new
innovations, products, services and features that can be driven by and personalised to
customers, even for a particular user.
Unexplored
Rosy as the future may be, AI is still in an initial pre-production stage in Indian banking and
retail despite people working on it. Case studies exist to give an idea about how we can go
forward but it is still in an exploratory stage.
The challenge right now is in collating the data into a single source and deriving usable insights.
The current focus on the AI front is firmly on fraud detection and prediction but down the
line, businesses will move towards areas like customer retention and other business
possibilities.
Why do we need AI
Being in its nascent stages, some may pose the problem statement, ‘Why do we need AI?”. The
answer to this lies broadly in improvements in customer satisfaction, and audience targeting.
It is able to identify potential customers for investments. It does this by looking at the user data,
possibly identifying new business opportunities that weren’t explored earlier. Based on the
information, we can suggest a better product range, track customer delight, location or
preferences; leading to an overall improvement in customer satisfaction.
Often, communication and promotions are generic with the same mail sent regardless of age
groups or domain. AI separates itself from offering generic products by making personalised
offerings. It also excels at finding out which would be the best new product, banking or retail, for
consumers and the target customers for the said product.
Golden data
When using AI, we look at what enhancements are needed to refine products. Data is obtained
from different sources, undergoes a cleaning and clearing process and once it is in a unified
format, we decide if it is valid for a particular customer or demographic.
It is essential to Identify data that is key to AI. This ‘Golden Data’ is cleaned, transformed and
made into a unified format for creating reports. Processes like these allow us to confirm the
identity of individuals and other situations that may arise before going ahead with the algorithm.
Data streams
Data might be collected from social networks or through third party data. It is then collated with
an algorithm to arrive at a product to identify a particular customer. The sources for this data
continue to grow, as we see integration in an ever increasing number of products from digital
personal assistants to televisions. While it might be considered more an upgradation than an
evolution, this integration gives us more sources to collect data from and a tool for better
consumer interaction.
Learning
These days, AI has moved to systems that learn through trial and error. Our aim is to find out
how the AI learns from historical data, its behaviour and approach to a current situation. That
kind of backfilling or need to understand the usage pattern of the customer allows us to learn
about the customer and when or who might require a product.
As an AI product matures, we can usually formalise the rules from historical data and try to
apply it and keep it learning. If the learning does not accelerate then we minimise or recant it. To
be successful, we need to make sure that all historical events are captured properly to give
correct insights of the past.
Win-Win
Let’s take a look at an example of a personalised banking product that leverages AI. If a user
only uses 10 percent of his salary and keeps the 90 per cent unutilised, the system could
intimate them and ask whether they want to go in for a fixed deposit for 5 days or 10 days as
per their history and preferences.
While it is a product-driven for the benefit of the customer, it is also a revenue generator for
banking companies. Similarly, AI would be able to drive features and on-demand services,
helping customers and banks see great dividends.

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The role of ai in indian banking and retail

  • 1. The Role of AI in Indian Banking and Retail The future of banking and retail holds many possibilities but none are as inevitable as the widespread adoption of AI (Artificial Intelligence). Revenue would be expected to open up as new business horizons which haven’t been explored earlier will be open to business insights. Personalisation will follow, with businesses saving on operational costs by not having to invest in many resources for detailed follow-ups and interactions. This personalisation will lead to new innovations, products, services and features that can be driven by and personalised to customers, even for a particular user. Unexplored Rosy as the future may be, AI is still in an initial pre-production stage in Indian banking and retail despite people working on it. Case studies exist to give an idea about how we can go forward but it is still in an exploratory stage. The challenge right now is in collating the data into a single source and deriving usable insights. The current focus on the AI front is firmly on fraud detection and prediction but down the line, businesses will move towards areas like customer retention and other business possibilities. Why do we need AI Being in its nascent stages, some may pose the problem statement, ‘Why do we need AI?”. The answer to this lies broadly in improvements in customer satisfaction, and audience targeting. It is able to identify potential customers for investments. It does this by looking at the user data, possibly identifying new business opportunities that weren’t explored earlier. Based on the information, we can suggest a better product range, track customer delight, location or preferences; leading to an overall improvement in customer satisfaction. Often, communication and promotions are generic with the same mail sent regardless of age groups or domain. AI separates itself from offering generic products by making personalised offerings. It also excels at finding out which would be the best new product, banking or retail, for consumers and the target customers for the said product. Golden data When using AI, we look at what enhancements are needed to refine products. Data is obtained from different sources, undergoes a cleaning and clearing process and once it is in a unified format, we decide if it is valid for a particular customer or demographic. It is essential to Identify data that is key to AI. This ‘Golden Data’ is cleaned, transformed and made into a unified format for creating reports. Processes like these allow us to confirm the identity of individuals and other situations that may arise before going ahead with the algorithm. Data streams Data might be collected from social networks or through third party data. It is then collated with an algorithm to arrive at a product to identify a particular customer. The sources for this data continue to grow, as we see integration in an ever increasing number of products from digital personal assistants to televisions. While it might be considered more an upgradation than an evolution, this integration gives us more sources to collect data from and a tool for better consumer interaction.
  • 2. Learning These days, AI has moved to systems that learn through trial and error. Our aim is to find out how the AI learns from historical data, its behaviour and approach to a current situation. That kind of backfilling or need to understand the usage pattern of the customer allows us to learn about the customer and when or who might require a product. As an AI product matures, we can usually formalise the rules from historical data and try to apply it and keep it learning. If the learning does not accelerate then we minimise or recant it. To be successful, we need to make sure that all historical events are captured properly to give correct insights of the past. Win-Win Let’s take a look at an example of a personalised banking product that leverages AI. If a user only uses 10 percent of his salary and keeps the 90 per cent unutilised, the system could intimate them and ask whether they want to go in for a fixed deposit for 5 days or 10 days as per their history and preferences. While it is a product-driven for the benefit of the customer, it is also a revenue generator for banking companies. Similarly, AI would be able to drive features and on-demand services, helping customers and banks see great dividends.