Data analytics transforming financial services in banking - Global Banking & Finance Review
An illustration depicting data analytics in banking, reflecting the evolution of financial services through customer insights and technology, as discussed in the article.
Finance

TRANSFORMING THE FINANCIAL SERVICES ECOSYSTEM WITH DATA

Published by Gbaf News

Posted on April 11, 2015

4 min read

· Last updated: April 28, 2015

Add as preferred source on Google

Phil Slavin, ‎Head of Business Development, Financial Services EMEA, Pivotal

Digital Disruption in Financial Services

The future of high street banks remains uncertain, with self-service becoming increasingly commonplace and customers increasingly becoming digitally-led. However, despite the myriad new market entrants in this sector, banks still have a distinct advantage thanks to long-term relationships built with tens of millions of customers and, the formation of vast internal data pools as a result. With millions of daily transactions across email, mobile and online channels, today’s financial institutions have a unique, holistic view of their customers and can use this valuable information to understand consumer segments and their behaviours more precisely.

To now keep abreast of financial technology advances and stay ahead of new disruptive competitors, banks must understand the value of this data, capitalise upon the insights within it and serve their customers better with more relevant, personalised offers.

The Importance of Data Understanding

Understand data better

High street banks and private financial service organisations have a 360-degree perspective on customers. This gives them the potential to develop new initiatives and cross-marketing events, as well as loyalty programmes linked to credit card and current accounts, for instance. It also presents an opportunity to protect customers from cross-channel fraudulent activity and strengthen the bank’s reputation for security and solvency. Banks can deliver tangible monetary benefits to consumers who use their new mobile services. However, they’re only using a small portion of their available data and are therefore missing a prime opportunity for engagement with customers.

Traditionally, we’ve seen data locked away in software which is used by different groups within the organisation. On top of this, banks often use incompatible data technologies, which makes it difficult to share market intelligence across various business groups. Banks also have long development cycles -taking over a year to plan and deploy new information systems – which makes them less responsive to the changing consumer and technology marketplace. In addition, many banks are not adopting a mobile-first strategy, or drawing value from this data, which is critical in light of an ever more mobile-centric and digitally-led consumer base.

Customer Engagement Strategies in Banking

Improve customer engagement

Facing intense domestic and international rivalry, banks and financial organisations must now engage better with customers in order to maximise longevity. This is especially true in the wake of challenger banks, born from the regulatory and political efforts to inject competition into the sector. To emerge ‘victorious’ at this time of uncertainty, banks must deploy mission-critical analytics tools to provide them with access to real-time data. This will help them to improve their agility and decision-making abilities from the back office to the front desk of each branch.

Universally, business strategy is to increase market share and revenues, and banks’ underlying IT infrastructure must be as fluid and responsive as possible to achieve this. Traditionally, banking IT departments have used siloed, disparate systems and physical hardware platforms which were designed to support single applications. To speed up access to customer data and therefore, consumer satisfaction, there must now be a transition to more flexible and intelligent operating systems which harness the use of big data. This will enable banks to engage better with customers, personalise outreach based on individual user profiles and offer the right service and product at the right time.

Data-Driven Risk Analysis in Financial Services

Accelerate analysis and reducing risk

Elsewhere, by using sophisticated analytics features, and real-time customer data, banks’ risk management departments can access information on customer-purchasing behaviour. This enables them to make immediate adjustments to individual customer credit limits or lending rights for instance, if they are a concern. Taking this one step further, the correct use of data science can help operations and analysis dig deeper, enabling CIOs to find informational patterns to identify and spot different types of fraudulent activity, from card ID theft, to card-not present (CNP) fraud. Preventing fraudulent activity in this way could save the industry up to $18 billion each year.

Every industry is being disrupted by rapid, consumer-friendly advances in software and services. Banks understand digital transformation and most major UK banks currently offer their customers some form of mobile banking. But there is still a long way to go and ultimately, banks must use their available data to not only to change the way they do business, but to pioneer new strategies that build on their strengths and undermine the growth of non-bank disruptors.

Key Takeaways

  • High street banks possess extensive customer data from multiple channels but underutilize its potential.
  • Siloed systems and slow IT cycles hinder banks’ responsiveness and personalization efforts.
  • Leveraging real-time analytics enhances agility, customer personalization, and fraud prevention.
  • Modern data infrastructure—big data, mobile-first strategies, agile systems—is critical for competitive advantage.

References

Frequently Asked Questions

Why do banks struggle to use customer data effectively?
Because much of it is locked in siloed systems across business units and hampered by slow, inflexible IT development cycles.
How can banks improve customer engagement through data?
By deploying real‑time analytics and using big data to understand behaviors, enabling personalized offers and loyalty schemes.
What role does mobile‑first strategy play in data use?
Mobile‑first strategies tap into customers’ digital behaviors and provide channels for personalized, timely engagement—critical in a digitally led market.
How does real‑time data support risk management?
Sophisticated analytics on real‑time customer behavior enables immediate adjustments to credit limits or lending decisions to mitigate risk.

Tags

Related Articles

More from Finance

Explore more articles in the Finance category