Leading with Analytics Systems for Big Data in Banking

The vast applications of data analytics and rise in DevOps teams are a result of improving data science approaches

Banks that tap into big data trends and brief their data scientists on essential strategic advances are leaders in the truest 21st-century sense. But, identifying trends and aligning them is not the only task at hand. One pressing requirement is – a bank’s current infrastructure needs to be modernized quickly to take on future challenges.


For current-day systems, big data analytics is a business’ catalyst in better understanding consumers and the lengths of operational possibilities. Essentially with infrastructure upgrades like cloud computing becoming mainstream, and real-time access to big data becoming a reality, strategizing has evolved ten-fold.


Multiple analytics providers have ready-made solutions to tackle the immediate need. But, for Banks looking to ace analytical functions in the now and the near future, business leaders need to ready system architecture for trending capabilities in other businesses too.


Which Trends Should Banking Analytics Systems Be Ready for?

The ability to make data intelligible and processable, to extract quality research can be termed data science. Add big data to the equation, and the need for faster data science processes becomes a priority. Real-time data processing can be leveraged in multiple scenarios, and since everything is going digital henceforth, knowing the extent of transformation helps with strategizing better.


The vast applications of data analytics and rise in DevOps teams are a result improving data science approaches for big data generation. In maximizing results from data, here are a few trends system upgraders need to look out for –


Actionable Data for Faster, Smarter Decisions


Actionable data is data outcomes produced from a large number of data records that allow a bank’s systems themselves to make certain choices, thus making intelligent automation (IA) smarter.


Data as a Product (DaaP) with Data Subscriptions


Data as a Product (DaaP), facilitated through Data Subscriptions, helps banks with pre-sourced good data without the hassle of revamping data collection infrastructure.


Hybrid clouds for the Next Step in Cloud Computing


Hybrid clouds are private/public clouds that unify multiple cloud storages for banks and implement advanced security. For banks, using a hybrid cloud reduces security and up-gradation hassles.


Banking Functions that Leading Analytics Systems Tackle

Leading analytics systems enable banks to draw conclusions about the segmentation of customers, better understand transaction channels, collect feedback based on reviews, and assess possible risks to prevent fraud. Currently, future-ready leading analytics systems for big data are –


Early Warning Signals are offered by new-age predictive analytics systems that help banks predict defaults long before they happen. These systems require data subscriptions for quality inputs on borrowers. The advanced systems use artificial intelligence (AI), machine learning (ML), and neural linguistic programming (NLP) models to collect and process the information on potential defaulters, helping banks strategize for the best recovery practices.

Risk-Based Supervision is a prescribed model for regulatory supervision of Financial Institutions around the world. Reported data is expected to be quantitative as well as qualitative, and is broadly expected to cover categories – capital, credit, market, earnings, liquidity, business strategy, operational risks, internal control, management, and compliance risks.


An RBS System equips Banks with enhanced compliance and risk management practices, with the added advantage of a comprehensive MIS system.


Management Information Systems with Automated Data Flow (ADF) capabilities offer the highest quality of data to business leaders as well as regulators, with a single system. New-age MIS systems are for Banks and Financial Institutions are benefitting from the regulator-mandated automated data flow because of the increasingly-adopted idea of ‘golden datasets’.


Golden datasets are clean, validated, integrated datasets, which lets businesses identify function-specific use cases for data and put processes in place to join or reconcile, and ultimately use the same data for multiple functions.


Insight Generation systems for IRAC Norms, Ind AS Reporting, and IGAAP Reporting with AI systems to utilize actionable data. Automation of multiple data collection processes is possible only when actionable data is generated and available systems can use the data for evolved analytics. New-age systems for insight generation utilize integrated solution architecture, multiple data sources, and data lakes, and also deploy advanced AI, ML, and NLP functions for higher-quality data.