'Aarogya Setu' app launched by the Indian Government is well-known on account of its ubiquitous presence during the Corona pandemic, but data analytics that played a major role in its function would be known only to a few. Similarly, data analytics and AI have crept into our everyday life even without our knowledge. To quote an example, online shopping sites start to display products based on earlier searches or browsing history.
AI and data analytics are disrupting every sphere of life, whether it is spending or saving, travelling or for that matter, work environment, healthcare, entertainment, or shopping. You name any sector, AI and data analytics have made an inroad impacting our lives in a big way.
Banking is no exception where AI and data analytics are making inroads in every sphere from customer acquisition to recovery.
AI refers to the ability of machines to exhibit intelligence just similar to humans. It encompasses cognitive functions that were believed to be in the exclusive domain of humans, which are now possible on account of advancements in computing.
AI and data analytics enable the processing of voluminous data leading to managerial information, thus facilitating decision-making. This aspect is particularly useful in banking as it involves huge complex transactional and non-transactional data, the processing of which becomes a herculean task.
Data analytics, as the name suggests, involves the analysis of data leading to meaningful information that aids in better decision making. Banks gain enormous data as part of their business which can be leveraged to retain and augment their market share. The present business environment, marked by stiff competition, makes the product offering by all banks appear to be similar.
AI and data analytics can be big differentiators. Banks that can leverage this aspect will have a competitive edge over their competitors. On the contrary, players who will undermine its importance will miss the bandwagon offered by these technological tools. Data analytics and AI are augmenting traditional business acumen for enhancing growth. The potential of application of AI and data analytics is immense in the banking sector, right from enhancing customer experience, resulting in greater satisfaction to monitoring and compliance. With the aid of these tools, banks can tailor products catering to the needs of individual customers instead of a one-size-fits-all strategy.
This aspect ensures a win-win situation both for the banks as well as the customer as it increases the better conversion rate for marketing effort on the part of the bank and a better value proposition for the customers. Customers get offering in tune with their needs.
Analysis of data in a bank is a daunting task on account of the complexity of data, which could be done easily through tools made available by AI and data analytics. We classify the application of AI and data analytics in six segments.
1. Business generation and retention: Generating new business is essential as it ensures steady income, which is necessary for the growth and survival of an entity. AI and data analytics come in handy for generating leads and their conversion. With the aid of these tools targeting customers for products become easy as the sales team can get access to customer preferences, buying behaviour through online habits exhibited by the customers.
Based on customer preferences, these tools make customisation of the product positioning at the individual level also. For example, most banks of a loyalty programme on credit cards can augment card usage through the use of data analytics. Banks can offer loyalty programmes based on customer preferences like bonus miles for frequent travellers, additional discounts for online shopping for online shoppers, discount on hotels for leisure and travel preferring customers discount coupon for a particular retailer based on the customer buying behaviour and so on. Targeting senior citizens for a home loan is not a good proposition as only a few would be eligible for the product.
Data analytics can be handy for selecting product categories based on customer demographics. This not only increases the strike rate but reduces sales effort also. Such matching of customer's demographics to product offering leads to better need assessment which is the first step of any sales process. With the use of data analytics, information regarding customers availing credit facilities from other banks or third-party products can be mined very easily through the transaction history of the customer. This can be harnessed for taking over loans from other banks. Similarly, this can provide leads for targeting insurance and mutual fund products.
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