How Axis Bank is Driving Distinctive Advantage through Analytics
As data analytics gained momentum over the last few decades, most industries began adapting data science into their daily operations and the banking industry was no exception. As banking processes became more advanced and customer demands kept evolving, data analytics enabled banks to optimise and streamline their operations while improving their efficiency and competitiveness. Axis Bank has been a key player in transforming and delivering an enhanced banking experience to its vast customer base. In a bid to stay ahead of the curve, the bank adopted data analytics to manage risk and operational efficiency in a better way. Want to know how? Axis Bank can measure credit risk among clients by using predictive analytics to avert any default before it actually happens. Analytics has also made customer engagements more personalised by assessing the customer’s spending habits and lifestyle and thereby offering them the right products and services. Outcomes of applying analytics in Axis Bank Why Cloudera? ​​Cloudera, the hybrid data company, provided the bank with a modern data platform enabling it to analyse up to 50% more data and gain deeper business insights across its retail and commercial business units, such as savings, lending, investment, and exchange services. Remus Lim, VP of the company’s APAC region, said, “Scalability and flexibility are central to our offerings as organisations grapple with increasing data volumes and data sources while seeking to improve the customer experience. We are glad that our solutions provide robust support for Axis Bank to optimise how they better analyse their operations and predict customer behaviour. “ Financial service firms globally have relied on Cloudera to support their digital transformation efforts. The company’s hybrid data solutions are perfectly suited for Axis Bank to align its data strategy with larger business objectives. Besides business intelligence, Axis Bank is better equipped for advanced risk analytics, and real-time fraud monitoring while maintaining cost efficiencies. How Big Data does the job at Axis Bank For Axis Bank, the confidentiality and security of their customers’ personal information are paramount. Thus, its privacy policy of protecting customer information governs how the Bank collects, stores and uses customer data. The core systems collect data across customers’ lifetimes regarding application and transaction-related data to generate insights. The Bank consumes the collated information to drive decisions and strategy across commercial, risk control and operational spheres. Balaji Narayanamurthy, Head – Business Intelligence Unit, Axis Bank explained their ethos by saying, “At Axis Bank, we believe in a ‘customer-first’ approach and we aim to use these data for good.” The Bank is also setting up a state-of-the-art personalisation engine to deliver specialised customer experience via two key capabilities—a data and a recommendation engine with the following components: Illustration of how a nudge is executed in the application Axis Bank is working towards strengthening its tech stack and is currently moving to Data Architecture 3.0 to enable a more sophisticated form of analytics. Axis Bank aims to leverage these technological transformations and the power of cloud platform to augment profits: Building an alternative data platform to enable score-based underwriting for a large cross-section of the lendable population. 10,000+ nudge variants developed and deployed via custom cloud-native serving layer. Use cases deployed across credit, fraud and marketing analytics on a cloud platform. Multiple machine learning-based credit models developed; 2000 attributes considered; over 40% lift on GINI over generic bureau model. How Axis Bank adopted a data culture Promoting data culture is necessary to survive in this highly competitive and digital world where decision-making is driven not merely by past experiences or a gut feeling but backed by the underlying data. Anish Parulekar, Head of Data Science at Axis Bank, explains, “Data Mesh, AI models, real-time insights and analytics on the cloud are some of the distinctive capabilities any analytics organisation should invest in to move from small wins to significant victories.” Axis Bank encourages a strong data culture by working on two key components: Process: Axis Bank has integrated an efficient measurement and tracking system for smooth decision-making. Technology: As valuable as deeper and faster insights are, the technology has to be right to make it actionable. Axis has integrated analytics into its MB app and core systems to drive significant business outcomes. Role of AI/ML and analytics in the banking sector At present, AI/ML and analytics are applied to many facets of financial businesses, from targeting customers to identifying fraud to extending a credit line. Financial services and banks, in general, have to follow a stringent ethical code of conduct to serve the customer by adopting a customer-centric approach rather than a product-centric approach. As a result, analytics and big data plays a larger role in catering to the personalised needs of customers. Financial service firms globally have relied on Cloudera to support their digital transformation efforts. The company’s hybrid data solutions are perfectly suited for Axis Bank to align its data strategy with larger business objectives. Besides business intelligence, Axis Bank became better equipped for advanced risk analytics and real-time fraud monitoring while maintaining cost efficiencies. Opportunities and challenges of using analytics in banking Financial organisations are often drowning in data and it becomes difficult to make sense of it. These companies need help to sift through this data and determine specific business solutions.  Today, data is drawn from alternative sources, which opens up a new room of opportunities to be taken advantage of. Continuous upskilling: Since AI/ML is constantly evolving, companies struggle with hiring for specialised roles and upgrading their tech to the latest. Defining the purpose: Collecting, storing and managing data with no real agenda will not give the desired results. So, the analytics team needs to understand the business objective to achieve optimum results. One team’s job: We must understand that a good data culture can only thrive when we have all the pillars equally supporting each other, where business, analytics and technology work collaboratively towards a collective goal. Axis Bank has been ushering in innovative change, having won the award for ‘Data Engineering Excellence’ at CYPHER 2022. Needless to say, adapting themselves to these disruptive technologies has opened the playing field even wider for Axis Bank while creating an institutional framework of innovation for driving growth to the next level.
Axis Bank strongly promotes data culture to survive in this highly competitive and digital world where decision-making is driven not just by past experiences or a gut feeling but backed up by the underlying data.
Axis Bank strongly promotes data culture to survive in this highly competitive and digital world where decision-making is driven not just by past experiences or a gut feeling but backed up by the underlying data.
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["data science", "machine learning", "AI", "R", "ML", "RAG", "Ray", "Aim", "analytics", "predictive analytics"]
["AI", "machine learning", "ML", "data science", "analytics", "Aim", "Ray", "RAG", "predictive analytics", "R"]
Poulomi is a Technology Journalist with Analytics India Magazine. Her fascination with tech and eagerness to dive into new areas led her to the dynamic world of AI and data analytics.