Machine learning, data analytics prove worthy weapons in financial services fight for loyal customers
Visit any financial services firm and you’ll find employees at all levels awash in reams and reams of data, all with the goal of striking the right balance between risk and reward. Nothing is more essential to doing their job effectively than this data, and yet it’s also become their arch nemesis.
The unabated growth of structured and unstructured data has made the job of business analysts tantamount to reading the tea leaves. And for all the potential value of said data, the job of extracting something actionable can be a formidable challenge — especially given the historical precedent of each line of business storing its own data separately.
Now, as banks seek further growth, one of the primary means for doing so is through better integration of the front-, middle- and back-office data and insights. Fortunately, advances in cloud computing, machine learning and data analytics are making it easier than ever to tear down the walls dividing the data and unlock the business value that’s hiding inside.
Exhibit A of this technology is the recent release of the Microsoft Azure IoT Suite, Cortana Analytics Suite and Azure Data Lake, all part of the Microsoft Data Platform. With the collective power of these and other offerings, financial services firms gain access to a comprehensive, state-of-the-art data analytics solution that addresses all of their needs, from unifying and managing disparate data stores, to analyzing, computing and creating powerful visualizations that can be more securely collaborated upon across the company.
The Microsoft data analytics platform has the potential to help the financial services industry address some of the thorniest of issues, such as the growth of money laundering to help fund criminal and terrorist enterprise.
The United Nations Office on Drugs and Crime estimates that the amount of money laundered globally each year is around $800 billion, or 2−5% of the global domestic product. Tackling this problem head-on requires advanced analytics capabilities that go beyond the traditional, rules-based approach that is typically used.
Ironically, it seems just as challenging for banks to anticipate the needs of customers with nothing to hide. It’s fitting, then, that the Microsoft Data Platform can be an invaluable resource here as well.
Cortana Analytics Suite is helping financial institutions better understand their end customers and make optimal customer service and product recommendations that are designed to attract and retain profitable customers. Banks can use these insights to anticipate their customers’ needs and develop digital banking strategies and services that position the company for continued growth.
Temenos, the world’s largest core banking provider, is using Microsoft technologies to build comprehensive customer intelligence solutions for its banking clients across the globe. One of these solutions is an “embedded analytics” solution called P.L.A.N (Profitability, Loyalty, Attrition Risk, Number of Products), which uses a combination of proprietary and machine learning models to calculate and predict key customer metrics, such as profitability and the likelihood of customer churn.
These analytical metrics are then integrated directly into the core banking and channels solutions, enabling actionable intelligence where it matters most — in the interactions between frontline staff and a bank’s customers. Employees at all levels can track the success of P.L.A.N initiatives with Microsoft Power BI dashboards and use natural language input to submit questions about the underlying data, effectively giving them almost limitless capability to understand customer behavior.
London-based Metro Bank also uses Power BI to monitor transactions across the institution’s retail banking branches, middle-office and back-office operations, funneling the data into more than 40 dashboards. Having access to real-time data analytics and visualization capabilities has enabled Metro Bank’s CEO and employees to keep their back office firing on all cylinders and provide an unprecedented level of service, such as processing new accounts the same day so customers leave the Metro Bank store with a fully functional debit card in hand.
Similarly, Tangerine Bank is using big data analytics and visualization capabilities to track factors such as customer feedback, public sentiment and external market factors. The combination of Power BI and the Microsoft Analytics Platform System has enabled Canada-based Tangerine to offer incentives and new services that are more relevant to its customers.
Each of these banks is a great example of how the financial services sector is responding to the current environment. As customers continue to scrutinize, those firms that leverage the strengths of data analytics, visualization and machine learning will be well-positioned to retain their most valuable customers, as well as to cull the criminal element from their ranks.
