One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.
It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway).
And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.
The demand for personalised banking services
At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.
McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage:
- Pre-meeting/meeting prep: Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
- Meetings/negotiation: Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
- Post-meeting/tracking: Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution
Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.
The rise of AI and its role in banking
With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.
McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.
- Credit card fraud prevention: Algorithms analyse usage to flag and block fraudulent transactions.
- More accurate forecasting: AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
- Better risk assessment and modelling: More advanced analytics and predictive models help avoid extending credit to high-risk customers.
- Predictive analytics: Help spot clients most likely to churn
- Gen-AI assistants: Instantly analyse customer profiles and apply predictive models to suggest the next best actions.
Considering these market trends, let’s discuss how you can move your bank into the future.
Using analytics to minimise risk and establish a competitive edge
With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.
Use machine learning to prevent fraud
Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.
This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.
