Five Data Analytics & AI Use Cases in Banking & Financial Services

Digital Transformation has been on the fast track in Banking and Financial Services industry even before the pandemic. With open banking, fintech, and other financial service companies growing at a feverish pace, as more people are gaining access and customer data is generated, good utilization of such data marks the difference between the success or failure of businesses. Data transformation is the key success factor for digital transformation. However, the greatest challenge that financial services organizations are going through today is the lack of clean, consistent, and accurate data.

Consumers today are consistently showing interest in sharing their information to get personalized and innovative services. It’s up to the banks and financial institutions to use the data (extra) judiciously and ensure a well-thought-out customer-centric user experience.

As Banks, Credit Unions, Mortgage and Asset Management companies gain more customers and their data, they get direct access to massive historic and real-time data on transactions, account information, spending patterns, credit information, and more. As a banking and financial service provider, you understand their salaries, other monthly income, loans, and liabilities and where their liabilities are. Now, what’s left is to make good sense of the data. One of the biggest challenges faced today is that banks and financial enterprises are unable to leverage all this data and deliver consistent customer experiences as they are designed to provide “account-centric” services. Customers’ data becomes redundant and siloed in account-centric banking. When you have a “single view of the customer,” providing a holistic understanding of them as an individual, that’s when you can provide “customer-centric” services and grow business through cross-selling.

But how would you know what your customers’ needs are when your data is account-centric and siloed? Only 360-degree customer intelligence can help you understand your customer’s needs proactively and ensure they are served well with a great user experience. Here we discuss 5 use cases, where data analytics & AI play a prominent role, that are gaining prominence among Banks, fintech, and financial services organizations.

1. Customer-centric Financial Services – Single View of Customer

Only by building true customer intelligence can you gain a single view of customer or Customer 360. Building true customer intelligence can be quite demanding. You need several elements to be considered and done right to achieve a customer 360-degree view.

This single view of the customer helps banks and financial services firms in getting a holistic view of their customers.

When the culture of your bank or financial services firm slowly changes to the customer-first approach, you start to predict the customer churn and build engagement-based customer segmentation, in addition to an investment/liability-based customer segmentation, detecting fraudulent transactions and improving customer onboarding experience.

2. Conversational Commerce

Conversational AI can be a game-changer for banks, lending organizations, and any financial institutions to engage customers when implemented right.
Conversational commerce can help you:

Conversational AI is said to be highly popular among millennials, a cohort of 1.8 billion people that have a combined spending power of $2.5 trillion. These millennials, a quarter of the world’s population, will make up to three-quarters of the world’s workforce by 2025. Studies show that over 60% of millennials communicate via texts. Hence, if you are not empowering them, you are at the risk of losing market share!

3. Cognitively Automated Loan Origination System

Loan/Mortgage origination involves a lot of manual effort in document verification, validation, and matching.  Loan/mortgage companies appoint a maker and checker process which takes care of this responsibility. This not only results in manual errors but also slows the loan origination process down.

Cognitively Automated Loan Origination can effectively automate these manual processes of loan application forms and proof documents with document screening, validation and verification of customer data, and KYC verifications. Cognitively automated loan origination process by implementing AI/ML helps:

As you work towards automating loan origination, you may face the challenge of which data platform to go for. Snowflake or Synapse or Redshift or something else? Any of them could work for you, but Snowflake may have an edge here with its unique data democratization capabilities allowing for seamless, relevant, and immediate data sharing amongst various users.

4. Recommendation Engine – Next-best action for Customers

Recommendation engines can significantly improve customer experience, revenues, and engagement. An AI chatbot can provide recommendations that help customers with everyday financial decisions and wealth management for both individuals and corporations.

Recommendation services can help fintech companies from personal finance and crypto to real estate, and lending companies to personalize their customer experience.

5. Support Ticket Analytics

Financial Services firms have long come to realize that modern problems require modern solutions. With AI, fintech companies and other financial services firms can analyze patterns in Risk, customer, and ticket analytics. Support ticket analytics can improve ticket management drastically with 3 main services mentioned below:

  1. Insights – Providing intelligence on number of incidents and service request, customer satisfaction, customer experience, first contact resolution, average resolution time, escalations, bounce rate, avg cost per incident etc.
  2. Decision Intelligence
    • Predict resolution time for a ticket based on past ticket data
    • Predict the priority of a service
    • Predict future ticket volume
    • Suggest similar resolutions to service engineering for faster resolutions
    • Predict average cost of resolution for a specific request type
  3. Cognitive Automation
    • Automate ticket resolution based on decision intelligence
    • Rapid resolution of tickets


With all use cases of data analytics and AI discussed above, in summary, data transformation is becoming central in improving efficiency, resilience, customer experience and also reducing costs. Customers today are a lot more informed and have more choices than ever. They are aware of the influx of Data Analytics & AI in Banking and other financial enterprises for innovative products and enhanced services. It is essential for these banking and financial services firms now to embrace data transformation initiatives to stand apart from the competition and deliver above everyone else.


Anand Subramaniam

Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.

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