Cloud, platforms, and open APIs are creating a world of opportunities for financial institutions to improve how they manage and use the wealth of data they possess – both to benefit customers and to grow their business.
There are also many challenges banks and credit unions need to overcome before they can start making the most of their data. Many struggle to bring together and make sense of the huge volumes of data they process and they don’t know how to best use it for
competitive advantage. A few other challenges include managing data security, privacy, and customer consent.
Maximizing the potential
In speaking to financial institutions of all sizes, they all reveal a desire for an ‘open banking’ model where multiple systems can communicate. To achieve this, they need to aggregate different data sources together, impose a common data structure, and move the
most important data into a secure, centralized data center in the cloud. From there, adopting a platform-based approach makes it much easier to access and start innovating on top of the data, for example, using third party out-of-the-box analytics solutions
to process the data and deliver insights, without the need to build solutions in house.
It’s also worth emphasizing that financial institutions can gain a lot of value from anonymized data. Third parties that are able to aggregate anonymized data across institutions can build a much more accurate picture of where processes are succeeding or
failing. By anonymizing the data it cannot be associated with individual customers so there is no operational or legislative risk. For example, dynamic peer group benchmarking enables financial institutions to compare their performance across key metrics with
their most similar peers, determined by a machine learning model.
The successful aggregation and analysis of masses of data will give financial institutions a deeper and broader view of performance and help them understand how customers are interacting across channels. For example, a better understanding of customer engagement can
be uncovered, including any points where the customer becomes disengaged or where bottlenecks occur. Insights can also help financial institutions better understand customer segmentation and how best to target different individuals. With the appropriate customer
consent in place, financial institutions can leverage advanced analytics to link anonymized data to an individual customer to personalize interactions and make specific recommendations to improve the customer experience.
Financial institutions can also use their own aggregated data to provide better results. For example, by identifying the customer behavior that precedes churn, financial institutions can spot the warning signs and intervene before a customer switches to
In a traditional model a financial institution would offer a customer improved terms only upon realizing that the customer had plans to leave, which is usually too late for customer retention. But imagine being able to reach out to a customer as soon as
they start to feel dissatisfied. A churn model can do just that: examining hundreds of different data points, such as the frequency of online logins, transaction volume, and spotting when engagement is falling. The model can also look out for lengthy customer
service interactions, which may indicate dissatisfaction, or a change of address or name, that could indicate a circumstance change.
Innovative financial institutions are regularly using these and other forms of data aggregation and peer comparisons, as well as making use of cutting-edge open banking solutions developed by leading fintechs.
Five tips for success
For those financial institutions starting out on their data journey, here’s a five-step approach for how to start leveraging data to maximize its potential:
Start small. Define what data you need to deliver against a business goal. My advice is to address one business area or set of siloed data at a time. Next, identify which systems the data resides in and how it’s captured and formatted.
Structure your data and move it to the cloud. Work to ensure the data is stored in a common format and start moving it to a secure data center in the cloud.
Be open. Integrate with platform providers and fintechs through open APIs. A platform-based approach makes it easier to innovate on top of the data and provides access to data analysis tools from partners that banks and credit unions couldn’t easily access
Experiment with anonymized data, AI and ML tools. Use AI and machine learning to analyze data and detect patterns to help tackle specific business problems. Leverage anonymized third-party data to compare your performance in specific areas against your peers,
and use the insights gained to make improvements.
Rinse and repeat. Implement learnings and replicate the process across other parts of the business. Once you’ve tackled a data challenge in one area, learn from that experience, iterate, and build up gradually. For example, maybe you start with data in one
system or line of business, and as you grow, create a more omni-channel view.
Data presents a huge opportunity and challenges can’t be resolved overnight. But it’s important to start taking steps on the journey and build experience and expertise. If not, other players with a more evolved approach to data and a greater ability to personalize
the customer experience could start to tempt customers away.