4 banking challenges & how a better analytics strategy helps

Only 7% of banks are fully utilizing analytics to drive critical decisions, despite the fact that businesses doing more with data will see financial growth at a rate that’s three times higher compared to those who are less analytically driven. And with the amount of data that exists in the finance industry, banks and other FSIs specifically are in a better position to leverage it.

Banks already have a data strategy in place, but knowing how to do more than just answer one-off business questions is what drives growth and significantly improves performance. Even more important is to determine how to apply analytics throughout the entire organization so end users can make smarter, safer decisions.

Strategically applying analytics is what banks are struggling with today. With this, comes the challenges of efficiently meeting customer needs, effectively (and easily) managing compliance and audits, mitigating risk to their full potential, and pushing data analysis upstream to line of business.

In this article, we dive into these four challenges and how a better analytics strategy can help banks overcome them.

Banks do a great job gathering customer data. But to stand out, they need to better anticipate customer needs and quickly adapt products and services to changing preferences (e.g., the growing expectation of 24/7 self-service, access to advisors for personalized advice, or embedded banking in mobile apps). Customers want personalized digital experiences that prove the institution’s investment in their financial well-being.

What makes this difficult is when data is locked away in disparate systems, inaccessible to various business units. When teams can’t gather all the data they want, it’s harder to create a 360 degree view of the customer. It also leaves a gap because there is probably crucial information they’re missing somewhere.

With a cross-channel understanding of the customer, banks can capture and keep attention with hyper-personalized messaging, faster response times, personalized advising services, and proactive product recommendations. Finding a way to better manage customer data can even improve the performance of overall product campaigns. And when data is organized in a way that’s easily accessible across the organization, it makes it easier to manage customer expectations across teams.

One wrong input in finance can cost a business a lot of money. Spreadsheets, the de facto tool across banking departments, have empowered teams to do analysis independently. They have also introduced significant risk. Being very inefficient and manual, it takes much longer to get to the insight needed. Plus, there’s no way of controlling a typo or if data gets changed inadvertently.

Further, they don’t allow banks to track each step of their analysis. This means that when mistakes happen, it’s very difficult to fix the error because there’s no documentation of how it happened, when, or by whom. This leaves business leaders dealing with incorrect financial reporting, calculation errors, and failure to meet regulatory compliance. And from a cost and time perspective, there are additional inefficiencies with the manual effort required to extract, transform, and analyze the data.

Making the data analytics process less manual and error-prone makes it easier for teams to track regulatory compliance and run an audit. With more control over data and how it’s being handled, business leaders can avoid costly mistakes.

70 percent of banking customers who have fallen victim to fraud reported feeling anxious, stressed, displeased, or frustrated when they were warned about potential fraud to their account. This experience can forever alter their trust in a bank and willingness to use services again, so keeping them at bay from fraudulent activity is important.

The only way to stop financial fraud is to quickly identify the threat. This becomes difficult when teams don’t have a way to analyze data from multiple sources. If you can’t review every single transaction at every single touch point, fraudulent activity will get missed.

An integrated system can ensure that 100% of activity is being reviewed. With analytics like anomaly detection, text mining, and cluster analysis, teams have the power to identify unknown patterns and detect malicious activity faster, allowing banks to prevent fraud before it happens. And running these techniques in a production environment monthly can provide close to real-time insight so teams can take immediate action to prevent losses or identify theft.

Financial services CEOs are less likely than their peers in other industries to say they have made significant progress toward upskilling efforts, and most acknowledge that they haven’t made any progress at all. The amount of data banks use daily puts a lot of weight on data teams when it comes to gathering, cleaning, and analyzing it all. Then, they still have to communicate the findings upstream. Not all individuals in an organization are data literate, but providing them with the means to upskill can lessen this burden drastically.

The first step here is establishing a knowledge sharing environment where experts in data science can collaborate with non-experts. When other departments in the organization can learn and apply basic data science principles on their own, it takes the heavy workload off the data team so they can focus on more advanced analyses. What’s more, it allows these other departments to become much more self-sufficient when making evidence-based decisions.

Closing the skills gap between those who can and can’t work with data allows banks to keep the momentum going toward becoming a data-driven organization.

When it comes to data, there’s so much more value to be garnered than just doing ad-hoc analysis. FSIs need a tool that allows teams to gather and analyze data from any source, automates and documents financial work, and has the analytic depth to create advanced data science applications.

KNIME allows organizations to do all of this in a single platform. With visual workflows and automated documentation, teams can ensure transparency and explainability of the process, unlike spreadsheets. Teams can also save time and operate more efficiently by automating finance tasks like budget monitoring, end-user computing (EUC) remediation, and KPI reporting.

Additionally, data teams can share analysis with financial analysts, compliance officers, risk experts, and other business users via interactive visualizations. Insights derived from data science then become accessible to not just the data team, but end users in the organization and line-of-business individuals.

The low-code/no-code nature of KNIME Analytics Platform also allows users to save and share data science solutions with other teams in the organization for reuse or repurposing. This not only helps upskill non-experts in data science, it also increases speed of decisioning, pushing the organization to become truly data driven.

Learn more about how KNIME helps banks improve customer satisfaction, ensure regulatory compliance, improve operational efficiency, and more.