Enabling Bank of the Future with Modern Analytics

Solomon Madadha, VP - Data & Analytics, Renasant Bank

Over the years, Analytics played an important role in harnessing key business functions in Banking such as Marketing, Fraud, Compliance, Risk and Customer Service.

Technology advancements combined with pandemic disruption in the year 2020 are pushing business boundaries into a new territory which are going to heavily depend on a modern analytics eco system.

Increasing share of Digital Transactions

Customers are leaning more towards online or mobile channels instead of branch transactions unless it's required. We see an increased number of branches closing every year due to reduced in-branch transactions.

Due to the pandemic, most of the banks are only allowing customers inside their branches with scheduled visits. As a result, customers are trying to get their business done as much as possible through digital channels avoiding branch visits.

Virtual Teller Machines

Interactive teller machines (ITM) are a leap from traditional ATMs which offers branch teller experience to customers with a face-to-face video interface.

ITM tellers are location independent, can serve more number of customers using digital queue and provide most of the teller services available at a branch.

"Without having the capabilities of modern analytics in the next few years, banks would not be able to serve customers in meeting their needs and expectations"

ITMs still operate during banking hours and service after hours may not be viable during this time.

A virtual teller machine with self servicing capabilities powered with Artificial Intelligence digital bot could be made available very soon.

Customer identification through new avenues such as facial recognition, location tracking multi–factor authentication could create touch-less and friction-less experience at an ATM, ITM or a POS check out.

Reliable, Scalable and Secure Data Framework

Reliable, scalable and secure data framework is the key to an insightful Analytics platform. With the explosion of data growth through variety of sources, a data framework that can ingest various data formats at a rapid pace is essential for faster discovery and consumption.

Cloud data lake offers centralized repository to store various forms of data with scalable performance than traditional on-premise systems.

For a multinational business with global operations, cloud data infrastructure provides instant replication through all zones around the world.

Global cloud computing market size is expected to cross over 800 billion USD by 2025.

Enhanced security, reduced infrastructure and operational costs, scalability and performance are some of the key factors that are making it attractive for businesses to move towards cloud adoption.

Artificial Intelligence

Artificial Intelligence is on the rise in 2020with several use cases in banking:

• Customer Service
• Credit Approvals
• Risk Management
• Fraud Detection
• Network Security
• Compliance

With growing number of organizations testing and implementing Artificial Intelligence applications, AI is going to become more matured, operationalized over the next few years playing a critical function in driving revenue.

Augmented Analytics

The ability of acquiring, cleaning and integrating data to make it available for analytics discovery in an automated process.

Data preparation takes majority of the time for data science activities, Augmented Analytics helps accelerate the process for faster delivery.

Natural Language Insights

Traditionally Analytics were made available by pre-defined set of KPI requirements through dashboards.

Natural Language Query (NLP) and Natural Language Processing (NLP) features are now taking Analytics to whole different level. By simple language text or speech, dynamic data is made available to both business users and end customers. These queries can be context based and offer security to deliver permissible data to configured user roles.

Banking customers are looking for quick and easy insights into their balances, expenditure, and transactions without going through a rigorous search of their historical data.

Decision Intelligence

Business functions such as credit approvals involve extensive decision-making process with manual inputs and thirty party data sources. Commercial loan or Consumer mortgage applications involve lengthy processing time for most financial institutions with manual processes and approval committees.

Decision Intelligence offers faster and automated process following compliance guidelines and rules set by the business.

Decision Intelligence could improve customer experience by offering recommendations to improve their portfolio.

Conclusion

Technology advancements disrupted several industries during the recent times, Banks and Financial Institutions are no exception.

Customers are moving towards digital platforms that offer hassle free interactions, lesser overheads and enriched customer experience.

Without having the capabilities of modern analytics in the next few years, banks would not be able to serve customers in meeting their needs and expectations. 

Artificial Intelligence

Financial

Customer Experience

Risk Management

Cloud Computing

POS

Natural Language Processing