Leading with Analytics Systems for Big Data in Banking

See what's needed for banks to improve big data analytics systems and its increased scope

Banks that tap into big data trends and craft strategic advances are leaders in the truest 21st-century sense. But, identifying trends and aligning them to business goals is not the only task at hand. One pressing requirement is – a bank’s current infrastructure needs to keep up with the pace of advancement.


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 current requirement. But, for Banks looking to ace analytical functions in the now and the near future, business leaders need to ready system architecture for capabilities trending in other businesses too.


Which Trends Should a Bank's Analytics Systems Ace?

Add big data to the equation, and the need for faster data processing becomes a priority. Real-time data processing can be leveraged in multiple scenarios, and since everything is going digital henceforth, knowing the heights that transformation will reach help prioritize.


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


Increasing the Role of Alternative Data in Analytics


Some major applications gaining ground are data enrichment to enhance underwriting, smart lending and credit scoring, and historic data for forecasting and predictive algorithms.


Data as a Product (DaaP) with Data Subscriptions


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


Hybrid clouds for Improved Interconnectivity


A hybrid cloud is an IT infrastructure that connects public clouds and/or private clouds to offer orchestration, management, and application portability while creating a single, optimal cloud environment.


Banking Functions that Leading Analytics Systems Tackle

Leading big data analytics systems offer actionable insights. The 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, leading big data analytics systems for Banks and Lenders are –


Early Warning Signal Solutions enable predictive analytics to help banks predict defaults long before they happen. These systems require data subscriptions for quality inputs on the borrower's financials, transactional behavior, market position, and other business factors.


The 'advanced analytics' system uses 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 for internal decision making.


Management Information Systems with Automated Data Flow (ADF) for Regulatory Reporting offers the highest quality of data to business leaders as well as regulators, with a single system. New-age MIS systems for Banks and Financial Institutions are benefitting from the regulator-mandated automated data flow with the creation 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 exact data for multiple functions.


Insight Generation and Research Tools for Regulatory Reporting offer actionable data for IRAC Norms, Ind AS Reporting, and IGAAP Reporting. Automation of multiple data collection processes uses data available systems for analytics. Systems for insight generation utilize integrated solution architecture, multiple data sources, and data lakes, and also deploy advanced Artificial Intelligence (AI) and Machine Learning (ML) for higher-quality data.