Top 5 Data Analytics & AI Use Cases in Banking & Financial Services

With the rapid growth of digitization in the financial services landscape, vast amounts of data is being generated. Harnessing the power of such large volumes of data by leveraging advanced technologies like Data Analytics and AI has become the key differentiator in the success of financial services businesses today.
New-age customers are increasingly sharing their information to get personalized and innovative services. It’s up to the banks and financial institutions how to use this data (extra) judiciously and deliver a well-thought-out customer-centric user experience.
As banks, credit unions, mortgage & asset management companies, and other such BFS organizations gain more customers, they get direct access to massive historic and real-time data on the customers’ transactions, account information, spending patterns, credit information, and more. They have information on customers’ salaries, other monthly income, loans, and where their liabilities are. Now, what’s left is to make good sense of this data.
However, banks and financial enterprises are unable to leverage all this data and deliver consistent customer experiences as their legacy systems are designed to provide “account-centric” services. Customers’ data becomes redundant and siloed in account-centric banking.
Moreover, in their journeys of data transformation, the greatest challenge that financial services organizations face is the lack of clean, consistent, and accurate data. In such a situation, how would a banking and financial services business know what its customers’ needs are to provide relevant services?
Only 360-degree customer intelligence can help an organization understand its customer’s needs proactively and ensure they are served well with a great user experience. When BFS organizations have a “single view of the customer” providing a holistic understanding of the customer journey, they can provide “customer-centric” services and grow business through cross-selling. It is here that advanced technologies like big data analytics and AI are enabling the BFS sector to improve the quality and accuracy of data to drive more informed decision-making and serve their customers better. In this article, let us check out a few important use cases of Data Analytics & AI that prove how banks & financial institutions can gain meaningful insights and deliver personalized solutions to customers.

5 Data Analytics & AI Use Cases in Building a Customer-centric Business Model

Here are the top five data analytics & AI use cases that show how advanced AI technologies are transforming financial services organizations into more customer-centric entities.
1. Customer-centric Financial Services – Single View of a Customer
Customer intelligence is one of the most significant use cases of data analytics and AI in the banking and financial services industry and a top priority for the financial sector. Only by building true customer intelligence can an enterprise gain a single view of customer or customer 360. When the culture of a bank or financial services firm slowly changes to ‘customer-first’, the organization starts 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 the customer onboarding experience. But building true customer intelligence can be quite demanding. You need several elements to be considered and done right to achieve it.
  • A Scalable, Reliable, Secure Data Platform that seamlessly collects, cleans, and integrates federated customer data from various source systems to create a “Single View of a Customer”.
  • A Customer Hub for customer profile data (name, social, preferences), transactional data (account level transactions aggregated at a daily, monthly frequency), customer feedback data, customer touchpoint metrics and any external information (social network) received upon customer consent.
  • “Customer First; Money Next” Culture to empower employees with a clear understanding of who their customers are.
  • Leverage big data with cloud infrastructure to bring down platform costs and scale predictably.
2. Conversational Commerce
Conversational AI is a game-changer for banks, lending organizations, and all financial institutions for that matter. When implemented correctly, conversational AI can improve customer engagement dramatically. Live Chat Apps, Chatbots, Voice Assistants, and Messaging Apps are all types of conversational commerce and classic examples of the revolutionary role of AI in the banking and financial services industry today.
Conversational commerce can help in:
  • Understanding customer behavior based on their conversational transactions
  • Recommending relevant suggestions based on peer customer interactions
  • Acting as an ‘Always-Available Virtual Agent’ or a ‘Banking Buddy’, improving customer online banking experience
  • Gaining insights on a customer’s utilization of banking features, improved spend analytics, etc.
The market for Conversational AI is projected to grow to USD 18.4 billion by 2026.
– MarketsandMarkets

Want to learn more about the role of data analytics and AI in streamlining banking operations?

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 according to a report by Catalyst. Studies by OpenMarket show that over 60% of millennials communicate via texts. Hence, if you are not empowering them, you are at 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. Lending companies appoint a ‘maker and checker process’ which takes care of this responsibility. However, such manual processes not only result in manual errors but also slow down the loan origination process.
A cognitively automated loan origination system is a classic use case of AI in banks and financial institutions. AI can effectively automate these manual loan application processes like document screening, validation and verification of customer data, and KYC verifications. Cognitively automated loan origination process driven by AI/ML helps –
  • Protect yourself and your users from fraud
  • Predict the loan eligibility of a customer
  • Predict the loan default possibility of a customer
  • Build a financial assistant to serve customers at scale to handle millions of queries efficiently
  • Carry out faster document verifications, KYC, and loan approvals
As an organization works towards automating loan origination, it may face the challenge of which data platform to go for — Snowflake or Synapse or Redshift or something else? Any of these could work actually, however, 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

When discussing the top use cases of data analytics and AI in banking and financial services, recommendation engines cannot be missed. AI-powered recommender systems can be used to identify and suggest the most relevant and valuable offers, services, or products to customers.
Machine learning algorithms can analyze customer data, such as customers’ transaction histories, online sentiments or opinions/feedback shared, demographic data, and all other types of available data to determine which products or services are likely to be of interest to them. This can help the organization drive targeted campaigns and adjust their marketing strategies.
Enterprises can also do a product association analysis to understand the relationship between different products and suggest complementary products or services. Through such tailored recommendations, banks and financial institutions can significantly improve customer experience and engagement, purchase frequency, and revenue.

5. Support Ticket Analytics

Financial Services firms have long come to realize that modern problems require modern solutions. Ticket Analytics is one such solution, enabling Financial Services firms to improve customer relationships by identifying the areas for improvement through –
  1. Valuable Insights
    • Providing intelligence on aspects such as the number of incidents and service requests, customer satisfaction, customer experience, first contact resolution, the average resolution time, escalations, bounce rate, average cost per incident, etc.
  2. Decision Intelligence that helps –
  • Predict the 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 the average cost of resolution for a specific request type
  • 3. Cognitive Automation
  • Automate ticket resolution based on decision intelligence
  • Provide rapid resolution of tickets
Studies suggest that AI-driven ticket analytics in a mortgage lending organization can reduce ticket resolution time with respect to customer onboarding by 20-40% and agent onboarding by 15-30%.

Wrapping Up

With the five use cases of data analytics and AI in the banking and financial services sector discussed above, it is evident that data transformation is becoming central in improving efficiency, resilience, and customer experience and also reducing costs. Customers today are a lot more informed and have more choices than ever. They are aware of the usage of Data Analytics & AI in the banking and financial services industry and expect 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.
At KANINI, we can guide you in your data transformation journey with our expertise in leveraging data analytics, intelligent automation, and AI for the banking and financial services sector. Contact us to know more.

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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