Banks are rapidly transforming themselves into “data-driven” organizations, treating data as a corporate asset, underpinning their business strategy and day-to-day decision-making. They are investing in Big Data platforms that combine structured and unstructured data and leveraging analytics to obtain powerful insights to drive enriched customer experiences, improve operating efficiencies and reduce risk. The convergence of machine and human intelligence is disrupting traditional decision-making by equipping organizations with knowledge and insight to predict and prescribe business outcomes. Advances in Big Data and Analytics are leading to new products and differentiated services making Banks smarter, agile and more competitive.
There are multiple areas that Banks can explore to drive enhanced value and growth:
Consumer and Commercial Banking
• Customer lifetime value analytics, customer call center analytics and deposit growth analytics
• Voice of customer analytics to measure customer sentiment in the social media and help that influence the strategy
• 360 view of customers to enable cross-sell and upsell
• Democratizing customer servicing leveraging Artificial Intelligence
Marketing
• Real-time personalized offers by analyzing customer profile and historical purchase behavior
• Analyze multiple service delivery channels to uncover consumer behavior patterns and understand channel profitability
• Measuring campaign effectiveness to continuously refine the marketing strategy
Fraud and operations
• Reducing financial losses through real time fraud detection and prevention
Governance, risk and compliance
• Supporting new regulatory and compliance requirements through stronger policies, procedures and governance practices leveraging newer technologies
• Predictive credit risk models that tap into large amounts of payment data to prioritize collections
• Optimizing delinquency models that can predict the probability of loan default
Capital markets, cards and payments
• Augmenting card and customer data with new-age parameters to derive competitive product pricing models, innovative loyalty schemes, assess creditworthiness for underwriting and recommend optimal lines of credit
• Deriving deeper insights into portfolio performance, liquidity positions and working capital requirements
However, there are some key challenges that Banks need to proactively remediate to get the most value from their Big Data investments:
• Unreasonable Expectations: Thinking of Big data as a solution for all problems. Expectations need to be pragmatic and managed continuously.
• Insufficient Planning: Many Big Data projects are executed without adequate diligence in terms of a business case and detailed plan. “Start small, fail fast” should be the preferred approach. Big Data technologies are continuously evolving. Taking a ‘rapid prototyping’ approach to trying out things in an iterative sprint model to assess business value that can be delivered, will increase the chances of success.
• Data Silos: Vendors are pushing ‘Data Lakes’ and ‘Data Reservoirs’ but in reality, many organizations end up with ‘Data wasteland’ or ‘Data puddles’ of non-integrated siloed datasets. Taking a consumption specific approach that identifies the different consumption patterns and have that drive data provisioning and integration will help in value realization.
• Collaboration: While collaboration is important for all data projects, it is all the more important for Big Data initiatives that touch every part of the organization. A strong operating model that facilitates many different teams working towards a common goal will aid in smoother execution.
Big Data initiatives at Banks can further benefit from tapping into newer global trends such as Cloud adoption, Democratization of Artificial Intelligence, Insights Marketplace implementation and Data Monetization. By adopting Big Data at scale across the enterprise, Banks can transform into “insight” rich organizations to drive growth and enrich customer experiences
(ETtech)
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