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Decoding Data in Banking Using Predictive Analytics
March 2015 by Marc Jacob
They say the customer is king. In today’s highly fragmented and competitive markets across sectors, garnering mindshare, especially when it comes to customers who vary greatly according to gender, demographics and purchasing power, to name but a few parameters, is an uphill task. And the key to setting up shop in the customer mindspace is to enhance customer experience, improve products and discover needs of the customer even before they know it themselves. Predictive analytics can help here. Its effects are far-reaching, ranging across industries such as retail, telecom and e-commerce. But it is often at its effective best, when used in banking.
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Banks have now gone beyond basic mining of data. Using predictive analytics and smart data, they can, on the one hand, gain valuable customer insights and maintain regulatory compliance on the other. All with the aim of personalizing the experience for the customer, and the long- term twin goals of customer loyalty and brand reputation.
With personalizing experience a priority, banks can now use smart data and analytics to create customized and relevant products for customer segments, thereby ensuring better response rates and greater customer delight. Past buying behavior can aid greatly in such a case, for example, historical data on credit card transactions can be used to tailor special offers on particular products using the credit card. Apart from the revenue impact, this also achieves the most important element of making the customer feel well taken care of.
Payment history, credit limits and other information are potential gold mines of information and can collectively be used to predict almost any transaction. However, a word of caution may be prudent as it is necessary to monitor the type of information being captured, the frequency of capture and most importantly, the security of the data captured. Cyber criminals and their sophisticated methods of perpetrating financial crimes are very often a pain area for banks, one that only the most robust and intelligent analytics software tool can ward off.
Using an analytics tool to structure seemingly disconnected data from a variety of channels is another promising area for banks. Picking up snippets of information from emails, chats or enquiries and predicting behavior is only the tip of the iceberg. Banks can use this to predict complaints, gauge market response on products and put steps in place accordingly. Fraud risk management is the logical next step. Unusual or erratic behavior can not only be spotted but decoded in very little time, giving banks valuable minutes to decide whether it is fraud being perpetrated or simply an impulse purchase. The key element is the ability of an analytics tool to make a decision and not just be a set of rules.
Data analytics can also be used to cross-sell and upsell products, offering the most relevant products to the customers most likely to purchase. This also has an impact on retaining customers who are potentially vulnerable in terms of defecting to competition. Offering the right product at the right time or responding in the right manner to a possible complaint can go a long way in retaining customer base. On another note, it can also help identify potential defaulters based on irregular behavior.
However, any analysis conducted by banks must be completed keeping data security and privacy in mind. Being aware of lines that cannot be crossed is also a top priority for banks across the world today. The balance between great customer experience and intrusion of privacy is one that has to be navigated by banks and their predictive analytics tools with utmost care.
Learn more about enhancing customer relationship management, determining customer risk and detecting fraud at the Smart Data Summit 2015, to be held on 25-26 May, 2015 in Dubai.
Visit www.smartdatadubai.com for event details.
