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Redefining the banking and financial industry with predictive analytics

Anneliese Schulz, Vice President of Asia, Software AG | Oct. 23, 2017
Why is data analytics on the frontlines of change and is set to be the cornerstone of the banking and financial industry?

This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.

In today's digital age, data analytics remains neither a prospect nor an opportunity. Against the backdrop of rapid digitization, it is fast becoming a pre-requisite for players in the financial space to remain sustainable. Digital Banking Report's research revealed that the top two priorities for banks in 2017 are to enhance data analytic capabilities, moving away from historic data crunching to (traditional BI) improve the digital banking experience. 

Legacy IT systems and siloed applications will continue to crumble under the weight of change, wrought on by rapidly evolving needs and infrastructure demands. From enabling personalised services for clients to forecasting risks and cyberattacks, data analytics is on the frontlines of change and is set to be the cornerstone of the banking and financial industry.

 

Predictive analytics as a powerful medium of change

Predictive analytics uses probability to forecast potential outcomes of what may happen in the future through analysing recent and historical data. After which banks and financial institutions will have better visibility of what could happen and what needs to be done. 

Armed with the capacity to analyse scores of historical and transactional data and extract relevant patterns, banks would be able to identify potential fraudulent behaviour in real-time analysing incoming events (streaming data analytics) and strengthen their cybersecurity ecosystem by re-acting immediately to the risks identified through a set of actions triggered and executed automatically. Predictive analytics is fast becoming prevalent across a spectrum of industries and business functions. While it is often perceived through the lens of risk management, predictive analytics' increasingly vital role in consumer relations is a harbinger of the evolving dynamics between banks and customers. 

 

Predictive analytics as a platform for customer management

According to Gartner, banks are under intense pressure to create dynamic customer experiences that enables a more seamless process through the personal mobile devices that customers use. The next decade will witness the erosion of traditional branch banking services and banks will seek to remodel customer engagement. Consumer interests and needs are increasingly diverging and banks will need to leverage predictive analytics to capitalise on the wealth of data and draw deep insights into consumers' spending habits and transaction patterns. 

A study by Capgemini revealed that 2016 saw more financial service providers leveraging predictive analytics to improve customer retention and acquisition. Backed by actionable information, wealth managers would be able to better understand market behaviour, identify client-specific opportunities and offer tailored financial services to high net worth clients. The capability to track each customer's transaction will endow banks with the ability to provide personalized services that meets a customer's financial needs, stretches their cash, and saves costs to a prudent degree. 

 

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