Banks have realised the importance of data from accelerating growth to enhancing productivity
Banks are already reaping benefits by implementing the use of data in their business
An astronomical amount of data is getting generated after the rise of digital payments and banks in the Indian market look at it as a valuable asset to steer through unpredictable environments.
Kerala-headquartered Federal bank was facing business problems and had to pay huge interbank charges as the ATMs as they are getting consolidated. The bank, with the help of analytics was able to determine where new ATMs need to be opened.
IndusInd bank, on the other hand, built an Enterprise Data Warehouse on Azure cloud to achieve its vision of being a digital bank underpinned by a data and analytics strategy. While dealing data in the volumes of terabytes, the warehouse, in a matter of few hours, manages and integrates 50 banking systems, generates business dashboards and reports. The infrastructure, at the same time, is maintained to be used in ‘on-demand’ mode, which regulates the costs.
The reports are accessible from mobile, which allows users to access dashboards on the fly and even in offline mode without the restrictions of connectivity to the bank’s network.
To ensure that the right products are targeted to the right customer through analytics, DBS Bank India Limited invested in analytics and customer propensity assessment. Also, to build a central data team and enterprise data hub, the bank has partnered with Cloudera.
The bank has instrumented the customer journey over its app with analytics which helps to understand the functions/tabs travelled to by the customer, better predict and customize his needs and assess reasons for drops (both to help the customer to complete the transaction and to reduce such instances).
The bank’s digital platform allows a fully intuitive digital on-boarding (both self-service and assisted), while its advanced analytics integrates multiple data sources and analytics to profile customers.
Axis Bank, which has an in-house analytics team specializing in customer segmentation, need-based analysis and hyper-personalization of offerings, is maximising analytics for hiring employees. For various roles, algorithm-based videos interviews are conducted to hire candidates and the decisions are taken on the basis of a model that predicts the performance based on the candidate’s expressions, tone of voice and a written aptitude test.
Also, to predict the best products for the customer and also gauge the possibility of a customer defaulting a loan or card, prediction models have been deployed.
The next version of the bank’s mobile app will be personalised, wherein different customers will see different things on the app depending on the offers they have frequently used, functionalities, actions they need to take, among others.
The entire data lake of Yes Bank is on the Hadoop suite, bundled with Cloudera, which has been created using a three-tiered architecture comprising Raw, Gold and Smith (an analogy to how gold is mined, made malleable and then carved to make jewellery).
Raw is the area where both structured and unstructured data is taken from the source system without being tempered. After this, the Gold team, which is the second tier, runs internal knowledge on the business application and enriches the data. In the final tier Smith, the team creates the models that will be readily used for analytics. In this layer, a lot of automated rule-based plus machine learning-based algorithms are built.
Also, to service the customers, there are multiple use cases that have been built in the data warehouse. The bank is also aware about the channel channel (online, mobile app, and chatbot), a customer interacts the most.
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