Achieving customer centricity has become a pivotal factor for success in the banking industry. “Customer centricity is a crucial aspect of banking that focuses on providing a personalised experience to customers. It requires banks to re-evaluate what they know about their customers and to better understand who their
customers are, what interests them, what they value and what drives them,” said Rahul Otawat, Vice President, Strategy, Analytics & Data Science – Wealth, Neobiz & Business Banking – Mashreq Bank.
The current status of the banking sector
Customers’ expectations for conventional banks and challenger banks (digital banks) differ significantly. Customers look to digital banks for innovation, expecting a wide range of inventive financial products and customisation options. These distinctions reflect customers’ evolving preferences in an increasingly digital and cost-conscious banking landscape.
“Studies have shown that customer’s expectations from conventional banks are factors like transparency, reduced cost/charges of products and services, and improved customer support services, which are at times overlooked by retail banks. Challenger banks, on the other hand, prioritise an improved user experience, appealing to those who want to be able to bank from their phones instead of visiting a retail location. They offer user-friendly interfaces and low fees. However, they might not offer a wide range of services and products like traditional banks do.”
For instance, Mashreq Bank focuses on data for decision making. It is one of the top leaders in MENA for the usage of data science and analytics within the BFSI domain. “AI is changing the way the world works, and banks are no different to that. We move forward in exploring the usage of conversational AI, blockchain, and NLP Models to drive best in class customer experience and be the best bank in the region,” said Otawat.
Technology for deeper insights
A growing trend is the integration of cutting-edge technologies like AI and ML, and advanced analytics to gain insights into customer preferences and requirements. Through the analysis of data from a myriad of interaction points, spanning websites, mobile applications, social networks, digital transactions, and even in-person branch visits, banks can anticipate customer needs proactively, leading to the delivery of highly customised experiences.
Usage of predictive modelling techniques
Predictive modelling techniques and algorithms have emerged as indispensable tools for anticipating customer needs and behaviours. Among these, a few have demonstrated exceptional effectiveness. ML algorithms, such as decision trees, random forests, and gradient boosting, have excelled in identifying patterns within vast datasets, enabling banks to make more accurate predictions about customer preferences and actions.
According to Otawat, there are four significant techniques:
- Classification – This has a wide range of applications from propensity modelling to customer churn prediction.
- Clustering/ Segmentation – This technique can be used to segment customers into groups based on similar behaviour for targeted marketing.
- Forecasting – This technique can be used to predict customer’s future profitability, expected revenue, etc.
- Time series – This technique will help predict time sensitive data, such as expected transaction volumes during a festival season, etc.
Furthermore, neural networks, a subset of deep learning, have been instrumental in processing complex, unstructured data from sources like social media and web activity, providing valuable insights into customer sentiment and behaviour.
These predictive tools empower banks to proactively offer the right products and services, ultimately improving customer satisfaction and loyalty in an increasingly competitive financial landscape.
AI/ML plays a significant role in identifying opportunities in the wealth management space as well. “It enables banks to provide specific recommendations about investment strategies, analyse portfolios, change asset allocations and offer other proactive support based on customer’s investment goals and risk profile. Potential risks can also be detected by analysing data from various sources such as financial statements, credit reports, transactional data, etc., to unearth patterns and anomalies that may indicate fraudulent activities or other risks,” said Otawat.
The Conversational AI customer connection
The adoption of conversational AI, which employs ML algorithms to enable human-like interactions between individuals and machines, holds the promise of revolutionising the manner in which banks engage with their clientele. Otawat believes that this innovative technology’s capacity to offer tailored and convenient services, marks a pivotal transformation in the banking and financial services sector.
This shift is propelled by the growing appetite for personalised and streamlined services and the advancing capabilities of AI technologies. “Conversational AI can help banks provide a seamless and convenient way for customers to interact with their financial institutions, eliminating the need for in-person visits. It enables users to perform tasks such as checking their account balances, making payments, and managing their finances, using simple voice commands or chatbots.”
This journey towards enhanced customer experiences, made possible by AI technologies, is setting the stage for the future of banking in the Middle East and beyond. As the industry continues to evolve, staying at the forefront of technological innovation will be the key to success for banks striving to meet the diverse expectations of their customers in a rapidly changing financial landscape.
Catch Rahul Otawat speaking at the CX NXT Summit, at Address Dubai Marina, Dubai on 15 and 16 November.