Beyond Big Data – Retail Banking in the Age of Predictive Analytics

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Since 2014, the retail banking sector has undergone significant changes across multiple business verticals, in a bid to keep up with the rapidly shifting technological landscape. Access to digital and mobile banking, new offerings and strong customer focus have been adopted unanimously within the sector. But there’s one major weapon in retail banking’s arsenal which has sparked a technological arms race in the banking revolution–big data.
Six key priorities have been identified as essential for retail banks to succeed and thrive during the last few years of unprecedented technological change. Unsurprisingly, among these priorities was obtaining a significant information advantage over competitors.

Over 30% of C-level banking executives agree that implementing new technology is among their top 3 investment priorities, and analytically driven companies realize financial growth 3x higher than their competitors across all industries.

Big data and predictive analytics have already brought retail banking transformational changes to sales, risk management and nonfinancial risks such as cyber security. By 2022, revenues from big data analytics are expected to surpass $260 billion, led by banking.

But now, big data is at the brink of its own dramatic period of change, the machine learning and predictive analytics revolution, and retail banks are once again forced to evolve or face being left behind.

 

The Cost of Big Data

What’s the point of collecting big data, if nobody knows what it means? Data inference, the ability to derive meaningful information from large data sets, is integral to banks’ information advantage goals.

Until recently, big data insights have been the vestige of internet-based companies and social media giants, who have the computing power, man power and financial resources to process vast amounts of data.
However, around 2015, retail banks underwent a marked surge in big data analyst hires. Data scientists and engineers who process and build predictive models are in high demand, but analyzing big data sets can often run into months of painstaking labor.

Banking captured a 13.6% share of big data revenues worldwide by 2018, and by 2021 an estimated 2 million new jobs will be created for big data in banking, but despite this, collecting and analyzing big data remains a time consuming and expensive task.

However, the cost of big data could about to be slashed. A combination of decentralization, AI and predictive analytics are making big data more affordable and crucially, more useful.

 

Beyond Big Data – AI-Driven Predictive Analytics

Although it may seem as if retail banks have only just got to grips with big data, predictive analytics, and the rising reliance on AI are opportunities banks can’t afford to miss out on. Data science companies are already developing powerful tools to enhance the capabilities of predictive analytics for their banking customers.

Predictive analytics companies such as Endor, an AI-powered ‘predictions-as-a-service’ platform based on Social Physics science from MIT, allows businesses and banks to upload raw data sets to their interface and ask targeted business questions using field-tested models. These questions can be answered within 15 minutes; removing the need for an in-house data scientist or engineer.

AI-powered predictive analytics produces data driven predictions based on the vast amounts of big data which banks collect daily. Most importantly, AI can operate more cost effectively than data engineers and scientists, reducing costs by up to 13% per year.

Through deeper customer behavioral insights offered by predictive analytics, marketing divisions of retail banks could make enormous revenue and time savings by only contacting customers who statistically have the highest propensity to buy.

Larger banking institutions are already implementing AI across their value chain, particularly in their back-office operations. New AI systems are estimated to save over 36,000 hours of lawyer and office worker time each year, while increasing average revenues by up to 17%.

 

Future Challenges for Retail Banks

Adoption isn’t enough. Retail banks have traditionally been leading innovators, implementing new technology as it arises into their business strategy to remain competitive. However, thought leaders in the AI space believe failing to adopt and implement strong AI business strategies throughout 2019 is equivalent to neglecting a mobile strategy during 2010.

Over 80% of organizations are considering AI adoption to enhance their analysis power. Retail banking’s response to the new AI-driven big data landscape, whether they choose to form in-house departments or use expert third parties, will undoubtedly dictate their business growth and relevance in the age of predictive analytics.

One thing is certain. Retail banks which adopt AI-powered predictive analytics technology will have access to unparalleled data on which to base new products and offerings–vastly extending their reach beyond traditional big data insights.

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