Customer Intelligence Platform for Banks and Financial Institution | Kanini

Implementing a customer-centric approach is significant for enterprises to satisfy today’s market and customer expectations, and the Banking & Financial Services sector is no exception. To be customer-centric is to know your customer well. And for this, it is essential to extensively analyze customer data at various banking touchpoints to understand their behaviors and requirements, and thereby build “Customer Intelligence.”

“What is customer intelligence?”

Customer Intelligence is the “deep know-how of customer” acquired via different facets of customer data (reference/ transactional/social). These data points are typically collected via watching the customer actions through various banking touchpoints (mobile, online, branch, IVR, etc.) and analyzed to get actionable information about customers.

This blog will give you a broader view of the changing Banking & Financial Services (B&FS) industry landscape in terms of delivering customer-centric services, in today’s competitive financial market, amidst the rise of Artificial Intelligence (AI) and how this technology is driving operational efficiency of every business process today, transpiring customer intelligence.

What Do Banks & Financial Institutions Need to Deliver Customer-centric Services?

Gaining customer intelligence is the key to delivering enhanced customer experience – the mantra for the success of any business today. With traditional data analytics practices, it is difficult to acquire superior customer intelligence. To overcome such limitations and align their services with customer needs and expectations, B&FS organizations must embrace AI & ML technologies, combined with the following data-driven strategies:

1. Customer-Centric Banking Model

Many banks still employ an account-centric model, where they consider a customer as an “account”, not as a “human being”. This perception should change. B&FS companies must adopt a customer-centric approach, rather than being account-centric, to deliver customized services and achieve better customer satisfaction. For this, banks need to build a robust data platform, supporting their digital platform, to capture and manage customer data efficiently. They can build a “single version of the truth” for every customer – capturing every data point around the customer’s preferences, life events, and transactional history so as to gain “customer intelligence” in the true sense. This enables B&FS companies to get a comprehensive view of all touchpoints the customer has with the bank or the financial institution.

2. Robust Data Modernization Strategy

The volume of data is increasing exponentially in today’s digital-dominant world, and banks with a traditional infrastructure may not be able to mine all of that valuable customer information due to data silos (data repositories that are held by one department and not fully accessible across an organization). To seamlessly extract and make use of as much existing customer data as possible from various sources, it is essential to migrate siloed data from legacy databases to modern cloud-based databases through a robust data modernization strategy.

3. Predictive Analytics for Next Best Actions

Today’s customers expect banks to deliver a seamless service experience, tailored to their unique requirements. To meet these rising customer expectations efficiently, it becomes essential for banks and financial service institutions to embrace advanced AI technologies. AI-enabled solutions empower B&FS to augment their customer service capabilities by enabling smart, real-time data analysis tools, predictive models, and next best action strategies – an approach that gives insight into multiple solutions for a specific customer and decides on the best solution after an in-depth analysis of the customer’s profile, past actions, and requirements.

4. Data-Driven Culture to Keep Pace with FinTechs and Neo Banks

FinTechs and Neo Banks are changing the banking ecosystem. Using modern technologies, these new-gen financial entities are able to serve customers far more efficiently by leveraging AI-driven data analysis to draw insights. Neo Banks, for instance, are offering instant responses and resolutions to today’s tech-savvy customers who expect prompt service. To catch up with such advanced expectations and compete with these institutions, banks must forgo traditional processes and foster a data-driven culture to extract actionable insights. They must leverage AI to accelerate customer service processes to deliver better customer experiences and also increase productivity.

5. Smart Data + Digital Transformation Strategy

Banks may at times employ great digital transformation strategies but these strategies may be data agnostic (not managing their data platform effectively, thereby forgoing data transformation). For instance, a customer who is navigating from his savings account page to his credit card page on his online banking platform finds the UI to be seamless and attractive but realizes that the information is incorrectly depicted. This is an outcome of a good digital transformation strategy but a poor data transformation strategy. Therefore, implementing a robust data transformation strategy is essential to streamline the back-end data layer of the banking platform, which can further help in enhancing the customer experience.

6. Cloud Enablement to Capitalize on Open Banking Opportunities

Open banking, which allows regulated third-party financial providers to access consumer banking data, is a futuristic approach that can help increase revenue streams, deliver proactive solutions, and add an advanced banking layer. With the rising adoption of open banking, the data also increases. For B&FS enterprises to utilize the open banking opportunity efficiently, the adoption of the latest cloud technologies is essential to facilitate real-time data processing through APIs, raise security standards, build trust among customers, and boost process efficiency in open banking.

AI/ML Framework for Customer Intelligence

Why is it important to your business?

Customers today have a plethora of choices when it comes to banking services and the room for creating loyalty is so limited for banks. Customers can easily shift from one bank to another if their volatile requirements are not satisfied.

A strong customer intelligence framework, integrated with modern technologies, is essential in the current business scenario to draw valuable insights from customer data and manage customer relationships efficiently. And, B&FS companies have realized the need for a powerful customer intelligence platform. They now understand that relying on conventional customer data analysis processes may not provide actionable insights and reap the desired results that today’s fast-paced financial world demands. For this reason, financial organizations are rapidly transforming their processes to embrace the latest AI-enabled data analytics.

So, to engage customers at an emotional level, predict their likes and dislikes, assist in tackling fraudulent activities, and gain a competitive edge, banks must turn towards an AI-powered customer intelligence.

5 Ways AI-powered Customer Intelligence Can Transform Banking & Financial Services

Ai powered customer intelligence platform

1. Efficient Customer Segmentation

Banks can use Automated Machine learning (AutoML) to understand customers’ banking behavior online, analyze their spending patterns, and segment them considering their preferences to predict and provide relevant banking services. These customer patterns assist banks in smartly identifying and predicting customer needs and generating opportunities for further business. With this customer intelligence, B&FS companies can identify cross-selling and upselling opportunities.

2. Smart Product Recommendations

Customers prefer that personal touch, and to satisfy their requirements, banks should strive to deliver services that are tailored to their specific needs. The customer intelligence gathered by employing AI & ML on customer data can help B&FS companies to provide accurate product recommendations to the customer at the right time. Banks can deploy ML-based recommender systems that understand customer needs and suggest personalized banking products by analyzing the data captured from customer invoices and purchases. For instance, they can identify an unforeseen expense that a mortgage payer had to go through right before paying the due amount and prompt the bank to offer him an emergency loan at a competitive rate.

3. Faster Documentation and KYC Checks in Customer Onboarding

Documentation is something that most customers dread and in today’s digital era, customers want the onboarding process to be quick and easy. AI & ML technologies are transforming the capabilities of OCR (Optical Character Recognition), which banks use to convert the hard copy of a document into its digital form. By combining AI & ML with OCR, banks and financial services companies can more accurately extract customer data from the text/documents and check for errors that may arise during the process. With such a hyper-automated customer onboarding, banks and financial institutions can conduct the necessary KYC checks rapidly and raise the customer satisfaction levels significantly. More so, it can enrich customer intelligence and enable B&FS companies to take a more customer-centric approach.

4. Accurate Aspect-based Sentiment Analysis

Sentiment Analysis, also known as opinion mining, is an integral part of customer intelligence. Aspect-Based Sentiment Analysis is another very effective AI-driven tool that uses a text analysis technique to categorize customer opinions by aspects or attributes. Banks can use the feedback shared by customers about their banking experiences on social media platforms like Twitter and Facebook, customer complaint forums, and product review platforms to draw meaningful insights. Aspect-based sentiment analysis is a great way to truly understand customer sentiments in real-time and create a more robust customer intelligence framework.

5. Intelligent Affinity Analysis

Banks can leverage intelligent affinity analysis integrated with customer demographics to uncover meaningful correlations between their products and services. Affinity analysis helps banks identify what products and services that would pair well. For instance, affinity analysis can be used in identifying products co-occurring in credit card transactions regularly. Further, banks can target these combinations for specific customer consumption and take advantage of these correlations to realign loyalty programs. All of this is to ultimately offer a more personalized customer service experience.

The Bottom Line

It is essential to know your customer really well through efficient data analysis and build a robust customer intelligence framework that will help your organization make informed and strategic business decisions for success. An AI-driven customer intelligence framework is what all banks and financial institutions today need to sustain in this highly competitive and dramatically changing business landscape.

If you are looking to build a smart customer intelligence framework to guide your B&FS organization in the right direction towards success through customer retention and new customer acquisition, contact us. KANINI helps banks and financial institutions tap into the revolutionary power of AI/ML techniques to understand customer needs and behavior and serve them better.

Author

Anand Subramaniam

Anand Subramaniam leads Data Analytics & AI practice at Kanini and is passionate about the data science domain and has championed data analytics practices across startups to Enterprises in various verticals. He is a thought leader, start-up mentor, and data architect. He brings forth over 2 decades of techno-functional leadership in envisaging, planning, and building high-performance state-of-the-art technology teams.

Author

Anand Subramaniam

Anand Subramaniam leads Data Practice at Kanini and is passionate about the data science domain and has championed data analytics practices across startups to Enterprises in various verticals. He is a thought leader, start-up mentor, and data architect. He brings forth over 2 decades of techno-functional leadership in envisaging, planning, and building high-performance state-of-the-art technology teams.

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