At AU Bank, we are harnessing the power of data for effort optimisation, quick decision-making and customer delight through a 360° analytical approach. We are using automation, digitisation and artificial intelligence to expand the scope and scale of our business.
PILLARS OF DATA ANALYTICS
In the last few years, we have built a strong framework to support our scale of data analytics and today use data-driven insights to achieve mass personalisation and innovative tailormade products and solutions. Our data analytics infrastructure is driven by scalable warehousing, better visualisation, enhanced mobility and best-in-class security.
Our robust processes help us eliminate duplication, enhance quality and improve governance of data. This will eventually help us cut costs in the larger scheme of things.
We have the best-in-class tools to support our data analytics team. Some of the tools we use are SAS, R, Python and SQL applications.
Our 20-member team consists of people from top-tier engineering and management colleges with the skills to manage reporting, modelling, mining and machine learning. They are experienced in handling customer analytics, risk analytics, people analytics, digital analytics, data distribution and advanced analytics.
UNDERPINNING OUR DATA ANALYTICS ARCHITECTURE
We are strengthening our data analytics infrastructure by focusing on:
Getting the data right
The first step in developing a best-in-class data analytics department is to collect correct information across a spectrum of subjects. During the year, we emphasised on improving our data collection systems through the following steps:
Simplifying data processing
We have developed a mechanism for easy consumption of raw data with a centralised repository that ensures fast processing of large customer information. Further the data is mined by an expert team proficient in all world-class analytical tools like SAS, R, Python and SQL.
Campaigns, propensities and probabilities
Post mining the data, we use the findings to identify financial requirements of our customers and reach them as and when they need our services. This is helping us increase our wallet share and generate more revenues while reducing risks.
Learning on the go
We are tracking and monitoring the success of these campaigns that leads to centralised allocation and campaign tracking. We are also using our learning from the first-generation campaigns to build stronger second-generation campaigns with detailed tracking and monitoring of risk models and portfolios for proactive action.
We are gearing up to implement several data analytics programmes around acquisition, cross-sell, credit decisioning and underwriting, collections etc.
ACHIEVEMENTS IN FY 2019-20
− Instant credit decisioning
− Minimal paperwork
− Customer delight
− High portfolio internal rate of return (IRR) with <0.5% NPAs
− Geo-location-based strategy
− Existing customer relationship based strategy
− Quick implementation of
− Target application agnostic