Making big leaps in banking with big data

By Jaya Vaidhyanathan

Recent banking reforms are forcing the industry to relinquish age-old techniques of data analysis and modelling, and adopt more rewarding technologies. With digitization, the volume of data has increased in leaps and bounds. The technology available with most banks is no longer adequate to process all of this data. Not only is an alarming amount of data going to waste, along with it, valuable insights are being lost to the industry.

Enabling the paradigm shift
Luckily, technology advances have facilitated the creation of tools that could handle previously unimaginable amounts of data. There is no longer a dependence on data that is “clean” and computationally manageable. The banking industry is adopting such new and emerging technologies – like big data, AI/ML and analytics – to:

• Make sense of unstructured data: Massive volumes of unstructured data are being generated every day. Traditional risk management practices that rely heavily on structured data are rendered inefficient. Using advanced technologies like big data, data analysts can make order out of the chaos and extract critical insights.
• Enable a single source of truth: The influx of data from various touch points – the news, social media and so on – begs the question of data duplication. Big data products allow large volumes of data to be stored at a single repository, open to query and analysis by different departments within the same organization.
• Ensure customer-centricity: Traditional banking practices relied on small data sets that were customized to specific data models. Big data can go beyond the restrictions of such information silos, pulling data from multiple channels. This enables layered perspectives and 360-degree analytics, and is immensely valuable in areas like customer profiling for risk mitigation.

Rise of the private banking sector
When it comes to big data adoption in India, public sector banks are yet to pick up pace. In stark contrast, private players were quick to catch up, rolling out new products and initiatives at par with even some of the global counterparts:

• Self Service BI: As we engineer systems that learn from experience, fraud risk management takes on a new perspective. Deep-learning systems can identify patterns in ways that cannot be coded through rule-based algorithms, while detecting potential risks faster than any human expert.
• Integration of big data with Blockchain:  As a universal and immutable source of truth, Blockchain is an authority in curbing financial frauds. When combined with big data analytics, permission-based Blockchain networks could become the future of risk management.
• Tackling cyber risks: Big data can be used to flag systemic security gaps, detect intruder attacks, launch defensive manoeuvres and so on – serving as a highly effective threat monitoring and mitigation mechanism.

Big data, big questions
With the advent of big data, there are growing concerns of data security and authenticity. The industry raises some pertinent questions:

1. Volume: With the impending data explosion, can the volume and variety of data become too much for big data systems to handle effectively?
2. Veracity: As most big data and AI algorithms are black boxes, can regulators really determine their fairness?
3. Volatility: Can all analytical data be retained indefinitely, and if it cannot, will this impact the results of the analytics?

How much of this concern is real? Are users today really in control of their data?
The transformative power of big data and AI needs to be taken with a pinch of salt. When used prudently, big data is revolutionary in its ability to solve specialized problems. It can discover solutions to problems that would take the average human intelligence years to solve. This allows manual efforts to be focused where they matter most – in strategizing and driving growth.

Secondly, every country follows certain set standards for security and privacy. Data regulation prevents big data and AI algorithms from discriminating (fairness) or revealing identifiable information. Meanwhile, pre-set techniques analyse data without revealing unique information. Stringent regulations like that of EU’s General Data Protection Regulation (GDPR) enforce this practice.

And last, every technology deployment is rooted in the belief that if caution and prudency are compromised, things are bound to go wrong. Right now, banks, including PSBs, are steadily moving towards customer centricity. For PSBs to enter and beat private players at their own game, a paradigm shift is needed in status quo. And it takes the right technology and the right service provider to make this shift happen.

(The author is the CEO, BCT Digital)