Saurabh Saxena
Data analytics has become very important in any business’ decision-making process. With digitisation coming into picture, huge amount of data is being churned out and companies often find it complex to turn it into meaningful information. Most organisations have embarked on using this data for dashboards and trackers, but this is just the preliminary stage - future of analytics lies in multiple axes of evolution.
In today’s BFSI industry, increasing regulatory and compliance requirements, the mandate to improve ROI, and the imperative to harness digital technologies to improve the customer experience, are all intensifying the drive to transform. As such, the BFSI industry is making use of Big Data to boost organisational success and ensure risk management with profitable growth and performance. Typically, banks have legacy systems, due to which interlinking different types of data from various sources is a huge challenge – with technological advancements, banks/consumers are now used to fast, personal, safe and always-available portable solutions.
In this data driven world, performance is dependent on Big Data technologies which can store and manage semi-structured/unstructured data in real time. Thus, banks have been optimising operations across all functions with an aim to improve efficient systems, enhance service-delivery models and customer engagement and protect its systems against cyber threats. By using analytics for deep customer insights and risk quantification, automation to streamline back-office processes, and deep financial domain experience, IT giants now help businesses adapt to an ever-changing environment.
Data Analytics, backed by AI & ML is transforming the BFSI sector
Today, organizations worldwide rank digital transformation as a top, strategic IT priority - this is especially true in the case of BFSI industry. With the invent of new entrants like FinTech, Digital Payment Banks, etc., new regulations and change in customer behaviour plays a critical role, hence the sector is making constant efforts to enhance existing/new customer experience. As such, they explore, experiment and invest in Data analytics use cases backed by Artificial Intelligence (AI) and Machine Learning (ML), focusing on growing top line by adding custom services and offering better channel experience to its customers.
AI enables banks to not only be alerted about potential fraud but also gives them a percentage that depicts the likelihood of an account ever becoming compromised. While technologies such as Big Data, AI and ML are disrupting the BFSI industry, hyper-personalisation is the next big thing that incorporates the virtues of non-banking rivals – it will make banks more customer- centric. The sector is embracing immersive technologies such as AI & ML to enquire customer’s banking needs, credit scoring models – uses ML to speed up accurate lending decisions, regulatory compliance and supervision, capital markets, customised financial services, cost and profit analysis, claims processing, etc.
New applications of Data analytics
The amalgamation of an increasingly complicated world, the vast proliferation of data and increasing desire to stay at the forefront of competition has prompted corporates to use analytics for driving strategic business decisions. A recent report by Nasscom revealed that growing adoption of Big Data, Analytics, AI and IoT is expected to push Cloud market in India to grow 3-fold to $7.1 billion by 2022.
IT has emerged to be a typical adopter of Big Data, although all departments, including finance, are considering future use. This is an indication that Big Data is becoming less an experimental endeavour and more of a practical pursuit within organizations. Today, Data analytics is being used in various fields – digital advertisements, recommender systems, image/speech recognition, gaming, airline route planning, fraud/risk detection, delivering logistics, etc. We can also use Predictive Analytics in testing - to predict what can be the possibility of user reacting to a specific event, based on the pattern that they have followed previously. And this can be further utilized to predict the possible areas of bugs, series of events leading to those bugs, possible reasons of encountering these bugs.
As you know that data is an esteemed resource in the present world. It is developing in volume more than ever. With growing number of SMEs in India, Data analytics market is facing a major boost - owing to increase in public demands - as SMEs, too, aim at running parallelly with market needs.
In the coming future, we expect Indian organizations to fully transform themselves from traditional, tactical and tool-centric Data analytics projects to strategic, modern and architecture-centric Data analytics programs – and they will continue to experiment and adopt smart data discovery.
The author is Country Director, Micro Focus India
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