Data Science in BFSI Sector - Importance and Benefits for Entrepreneurs
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Data Science is helping the banking industry become smarter in managing the myriad challenges it faces today. While basic reporting and descriptive analytics continue to be a must-have for banks, predictive and prescriptive analytics are now starting to generate powerful insights, resulting in a significant value add.
Banks are increasingly using machine learning to power their operations, but the adoption of these new technologies is not widespread across the other departments.
The road to comprehensive implementation of machine learning to solve challenging business problems in banking is fraught with technological and organizational challenges.
Moreover, banks today generate huge volumes of internal data (customer accounts, credit scoring, payments, assets, etc.) and now need to understand its linkages to external data (interest rates, macroeconomic variables, and customer preferences). The velocity of this data creation is also increasing exponentially. This is compounded by the variety of non-traditional or digital touch-points that have emerged – ATMs, Internet, IVR systems, social media, and mobile, among others. The explosion in volume, velocity, and variety of data is forcing banks to leverage advanced analytics to make sense of the huge and complex information sources and make near real-time decisions to stay competitive.
Data Science is inducing organizations to utilize the data assets available to them, regardless of whether that is client information, budgetary information or something else, in an astute way. It is also enabling the BFSI industry to reach out to fresh markets, cross sell products and services through efficient delivery channels, enhance customer loyalty, etc. Every aspect of banking be it risk management, pricing, marketing outreach, consumer outreach, product development, cost and revenue allocation – data science is applied ubiquitously.
Analytics is helping the banking industry become smarter in managing key business challenges it faces today. Some of the most common areas where AI/ML are predominantly used are:
1. Fraud Detection Machine Learning is pivotal for successful detection and anticipation of fraudulent activities involving credit cards, accounting, insurance etc. Proactive detection of fraud in banking is basic for providing security to clients and workers.
2. Customer Support The bank has fabricated its robust customer care framework and, with the assistance of data science, has the access to all the sensitive customer data and investment patterns and cycles. Subsequently, it analyses what plans the customer has and what credits he doesn’t have. It, henceforth presents the customers the offers that suit them.
3. Customer Segmentation Customer segmentation means singling out the groups of customers dependent on either their conduct (for behavioural division) or specific attributes (for example region, age, income). There is an entire arsenal of strategies in Data Science such as clustering, decision trees, logistic regression, and accordingly, they help to learn the Customer lifetime value (CLV) of each customer fragment.
4. Customer Data Management Banks are obliged to gather, examine, and store enormous amounts of data. But instead of looking at it just as a compliance exercise, AI and data science apparatuses can change this into a possibility to become familiar with their customers to drive new revenue openings.
5. Risk Modelling Risk Modelling is a high priority for the banking industry. It helps them to formulate new strategies for assessing their performance, credit risk modelling being one of its most important aspects. Stress testing has now been integrated in major banks around the world and tools like SAS, R and Python are often used to carry out the check on the financial health of the bank.
6. Operational efficiency Improvement Artificial Intelligence and Data Science can assist organizations with anticipating demand based on recorded information and future events by the means of propelled time-series analytics. With such insights, for instance, a business can envision call centre traffic, volumes to be handled by teams in back-office function, etc. These insights would then be able to be utilized to make strategic resourcing, advanced asset planning and so forth.
According to a recent report, JP Morgan Chase is helping the US government by building tools for policy making utilizing Big Data. It is consolidating the transactions of 30 million American clients with the financial measurements of the United States. It will use big data tools to analyse the public information and help the policymakers to forestall monetary fiascos.
To gain competitive advantage, banks must recognize the crucial significance of Data science, integrate it in their basic decision-making process, create techniques and build strategies dependent on the actionable insights from business data.