Amidst the ever-present big data buzz, some global banks have mastered the art of using data science and are already reaping benefits. Riding on big data, they have managed to improve customer engagement, revamp products and optimize marketing outreach, risk management, pricing and ongoing cost reductions. Meanwhile, others are still trying to make sense of where these emerging technologies and techniques fit in. At some point, banks of all sizes, shapes and forms need to incorporate data science into their operating models.
The future of banking will be determined by how well banks use technology to maximise their accumulated wealth of transactional and interactional data to better understand hidden patterns of customer behaviour and make necessary service improvements by customising existing offerings to properly align the right products with the right customers.
Analyse, Utilise, Maximise
To successfully implement data science, banks need to have a strategic and structured approach. Banks that can analyse the collected data and utilise it for strategic decision-making will maximise their competitive advantage; those who don't, are placing their profitability, if not their survival, at risk.
Companies such as Google, Pandora, Netflix, Amazon -- and many others -- are winning decisively in their markets because of their refined ability to mine insight from the wealth of digital information surrounding people, organisations and devices, or what we call a Code Halo. When properly harnessed, code halos contain a treasure trove of business value. By understanding these valuable digital records and applying insights gleaned from data from customers, partners and employees, banks can more effectively compete on code halos and gain incredible edge over competitors.
Application of Data Science
To accurately ascertain how customers prefer to be served, banks can apply such data science techniques as hypothesis testing, crowdsourcing, data fusion and integration, machine learning, natural language processing, signal processing, simulation, time series analysis and visualisation.
For example, using a mobile app, banks can analyse individual consumer behaviors and spending activities and combine that data with credit bureau information. When analysed,the resulting insights can lead to better targeted messaging around a potential offer, such as a pre-approved home loan to a customer who is qualified based on analysis of the data contained in his transactional files and interactions on social media.
Data science can help banks recognise behavior patterns, providing a complete view of individual customers and segments. For example, when a customer enters a bank, customer representatives can be better equipped to offer the right products and provide a quicker resolution to customer queries by analysing their code halos. Data science can also be used by banks to analyse the average cost for each channel (e.g., call center, branch banking, etc.) and design strategies to migrate customers to low-cost channels.
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