Data Analytics in Banking and Financial Services

In today’s data-driven world, data analytics in banking plays a crucial role in informing decision-making to drive organizations forward, improve efficiency, increase returns, and achieve business goals. For the uninitiated, data analytics is the process of discovery, interpretation, and conveying meaningful insights from data to help in the decision-making process…

The big data and business analytics market was globally estimated at $169 billion in 2018 and is expected to reach $274 billion in 2022. The innovations and subsequent new applications for data analytics grow daily. Approximately 13% or $22 billion of this market is projected to be revenue from banking and financial services applications where the digitization of many services has created huge amounts of big data. This data, both structured and unstructured (such as customer comments), can be cost-effectively handled using the right AI and cloud technology. It can drive new revenue opportunities, and maximize existing opportunities, by improving business practices in the eyes of your customers.

Banking and Financial Services institutions use data analytics to integrate large diverse customer data sets to monitor and create nudges to customers for personalized and customized products and services, specific to their individual requirements. A very basic illustrative example of this might be that when a customer buys a vehicle, their bank could send them promotional offers on vehicle insurance. How they respond to this nudge, and therefore the requirement for follow-up nudges, can be developed through a greater understanding of further segmented customer journey data and predictive analytics. Ultimately, analytics can trend buying patterns relating to different groups of customers, as well as go a step further to predict individual future behavior based on these trends.

Areas where banking and financial institutions are increasingly using data analytics include:

• Fraud detection – The ability of advanced analytics to understand and detect variation outside of normal buying patterns in real-time, enabling banks to respond instantly by freezing payment cards and notifying their customers.
• Risk modeling for investment banks – Machine learning and predictive technology can augment trader skills to further enhance investment performance.
• More personalized marketing – understanding and predicting customer behavior can become more effective with potential micro-segmentation of customers and individualized nudges that really resonate.
• More innovative new products – offers can be based not just on current needs but on what customers are going to need in the future so you can be first to market
• Lifetime value prediction – a more complete understanding of customer data enables more accurate predictions on customer worth and investment of effort based on more powerful and meaningful segmentation.
• Enhanced customer feedback analysis and applications – customers are able to more easily and frequently feedback and interact with intelligent systems.
• Improved customer risk assessments – More accurate credit assessments and better early warning systems for customers getting into debt.

The importance of data analytics in the banking and financial services sector has been realized at a greater scale than ever before and many established banks have already started reaping the benefits.

Data Analytics In Action

OSG Analytics worked with a leading retail bank in UAE to define actionable recommendations to improve their customer experience and develop a practical segmentation model they could use to better understand their customers identifying gaps and opportunities to improve engagement and product sales.

OSG’s unique behavioral analytics technology and powerful data integration tools were utilized to develop prioritized customer need segments for the bank to target:

OSG was able to use analytics to map customers back to the bank’s database and model the impact on growth of different strategic actions for each segment. The bank was able to identify the exact growth they would incur from different actions to enable better investment planning. Using this work the bank was able to:

• Understand and develop sales and communication plans based on their new needs-based segmentation model.
• Use their deeper understanding of Net Promoter Score (NPS) drivers to develop a budgeted action plan to improve their NPS scores. This led to increased customer loyalty.
• Develop new perfectly timed up-sell interventions for each segment.
• Better optimize product and pricing strategies across product portfolios with improved targeting for each customer cohort.

Using Technology to Map Customer Journeys

A deeper understanding of customer journeys, or path to purchase, is now possible as customers view different offers and options from competing financial service providers. Our OSG o360 technology can be utilized with a panel of customers to track all their interactions both on and offline for a complete understanding of all the factors influencing their behavior and their drivers when making financial decisions. This can be achieved whilst still meeting all regulatory and security requirements whilst undertaking data capture and observational research. OSG has specific financial sector expertise in optimizing the quality of data when sourced across several different customer databases to maintain data integrity and ensure it is accurate and secure. Surveys incorporating behavioral analytics technology are able to probe and understand the real underlying customer motivations at critical journey points and reveal what communications or nudges will really resonate with customers when making financial decisions.

Banks are focusing more than ever before on using data analytics to gain a competitive advantage. OSG Analytics is well equipped to support you with the tools, technology, and data science skills required to incorporate data analytics into your decision-making process and develop strategies based on the actionable insight from your customers’ data. This will in turn lead to improved customer loyalty and increased take-up of better-targeted product offers.