The introduction of Artificial Intelligence to humankind has made our lifestyle a lot different for the past few years. From self-driving cars to treating patients in the healthcare industries, AI has proven that it can mimic human actions potentially with minimal errors. This element of AI has attracted bankers and now, AI is an in-demand technology in banking. A bank can stand the test of time when it has the strongest AI framework as it ideally serves the best for the customers. However, the question is- which is the best AI framework? And the answer is

What is

Banks are beginning to realize that their focal point needs to shift from being “finance-centric” to “customer-centric.” The obvious reason is the rising competition and volatile customer expectations. To increase customer intelligence and hence, customer engagement, the best bet for banks is an AI/ML framework. Acquiring Customer Intelligence after achieving client centricity, results in banks gaining true knowledge of a customer. is that AI/ML data-driven framework with the centre of focus being data. Be it collecting useful customer data from all resources to providing insights for better customer engagement, does it all.

Customer Intelligence Focussed: Functional Components of

Having “One truth version” of the data, customer intelligence concentrates on providing useful insights for future enrichment and better ROI of the banks. Inclusion of the Machine Learning algorithm helps analyse a large amount of data and return with accurate recommendations and customer-specific actions. Banks can leverage such insights to make key decisions on product-offerings, cross-sell and upsell campaigns and help improve customer engagement.

Recommended read: Building True Customer Intelligence for Banking with Machine Learning and AI

The three components of that reflects Customer Intelligence are:

1.Data discovery

In today’s competitive financial marketplace, customers are usually overwhelmed with a multitude of choices that usually leaves them confused and undecided. This is where Data Discovery can help your bank get the upper hand. With Data Discovery you can carefully analyse the customer’s financial needs and send across offers and services that can be custom-designed for your customer’s current need. This way you earn yourself a loyal and a very happy customer.

Hence, the main pillar of Imagyn framework lies in the discovery phase. The rest of the components depend on this stage, as this is where all the data is gathered. Hence, it only makes sense to collect intricate details, analyse the structure of the core banking system, understand the data journey, tech stack, etc., to provide your customer with a delightful customer experience.

2.Data optimizer

Once the customer data is available, it is imperative to optimize the data for better customer intelligence. At this stage, the data is extracted, cleansed, and standardized to create a unique profile. The data available in the discovery stage is gathered through several data touchpoints like bank statements, feedbacks, enquiries, engagement history, etc. In such cases, there are high chances of data duplication, presence of structured, unstructured, possible incorrect data. Therefore, such data needs to be cleaned to create a standard record which is unique to the customer and covers all the touchpoints efficiently.

3.Customer 360

For efficient financial planning, one needs to calculate the expenditure as opposed to the income. From basic spending to future planning, it involves a lot of strategies and is time-consuming. On top of it, we have to continuously keep track of expenses which is a strenuous activity, isn’t it? Instead, how about having an app which ensures all the above activities for you?

This can happen only when banks understand the financial needs of their customers completely. Therefore, Customer 360 is a component that creates the “one truth version” of the customer by providing a holistic view of the extracted data. Practically speaking, the collected data is virtually available everywhere and one needs to look in all possible direction to understand the customer, and hence, their needs effectively. This job of breaking and analysing the customer data is carried in this stage along with monitoring and managing the data.

Areas of Banking where can prove worthy

The introduction of AI in banking is mainly to increase the overall customer experience in the front-end and here are some of the areas where can prove its worth:

1.Customer Segmentation

Customer Segmentation is a crucial part of Customer Intelligence as it helps to easily analyse a group of like-minded customers, hence allowing targeted marketing. This results in reduced marketing costs, value-added marketing, handling fraudulent cases, etc. Thus, one of the main pillars of is categorising the customers into several segments for better understanding of the needs and preferences.

2.Customer Analytics

An AI framework is considered to be superlative when it can provide useful insights about customers which in turn help to build a trustworthy and robust bank. A research report from Everest* stated that implementing AI in Customer Analytics has increased the data coverage by double or even triple. ensures to get a comprehensive view of the customer and provides the ultimate “Customer Golden Record.”
Recommended read: Customer Analytics – The Neobank Gamechanger

3.Customer Onboarding

One area of banking in which AI has impressed bankers and stakeholders is Customer Onboarding. The financial industry has had its share of prolonged struggle in finding the best platform for Customer Onboarding which is fast, seamless, and self-sufficient. The report from Everest stated that inculcating AI in Onboarding has reduced operational costs by 20-30% and reduced turnaround time by 50-60%. can help your bank fast-forward customer onboarding by collecting essential data and helping you understand the requirements of the customer in a faster and effective way.

4.Customer Retention

Most banks struggle to have a long-term relationship with the customers mainly due to lack of knowledge about customer requirements., on the other hand, helps you retain the customers by providing a lot of useful insights by collecting data from various touchpoints.


Here are the benefits of

  • Entirely data-driven where data is the ‘oil’ to run your ‘fuel’ (bank.)
  • considers your bank’s existing data journey to get a holistic customer view.
  • has loosely coupled framework which means simple code, easy testing, and increased scalability.
  • effectively predicts the ever-changing customer needs and provides real-time actions accordingly.
  • handles data in a way to power exceptional customer experience.


Artificial Intelligence has slowly started to secure its place in banks and other financial enterprises. The increased competition and the ability to expand the offerings have made banks hunt for the best possible framework, which helps them to constantly grow. Therefore, is an ideal framework for banks to have sustainable growth and authentic customer experience.

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