For years, banks have had to base lending decisions on credit scores, despite the fact that they don't paint a full picture of borrowers' liquidity. That's because liquidity is still mainly understood from an accounting perspective: current assets minus current liabilities.
Real-time payments analytics enable banks to fill in the blanks and transform lending in the process because they integrate real-time data about cash flow into the liquidity assessment.
By monitoring receivables and disbursements in real time, banks can track net cash, as well as where it's coming from and where it's going, and create a clearer portrait of the account holder’s real-time financial health. Whether a potential borrower is more or less creditworthy or whether a business is sustainable can now be determined via instant, objective considerations — all thanks to real-time payments analytics.
At least, all this can be the case — when the payment data is actually manageable.
After surveying banking executives across 10 countries last year, McKinsey & Co. discovered that nearly every respondent listed advanced analytics as one of its top three priorities. Yet the same survey revealed that most banks are struggling to reap the rewards that such data capabilities promise to offer — even after investing in data infrastructure and experimenting with advanced analytics procedures. The truth is that most banks are only using data analytics for simple operational purposes, such as reporting on volumes of payment types or tracking rejects or rehandles.
It's not altogether surprising. Collecting, storing and tracking data are huge undertakings, but even then, the effort is not over. The crucial last step is turning data into actionable insights, which is where some financial institutions struggle.
Data that exists as static facts and figures on a spreadsheet often has little in-the-moment value. But when it's real-time and expressed as a dynamic visualization, it becomes instantly digestible and increasingly actionable. Crucially, the data gains context through integrating past and present information together with future predictions. Decision makers can identify trends and anomalies and generate advanced guidance based on more dynamic information and improved modeling.
For these reasons, real-time data visualizations are a superior final form for managing, analyzing and leveraging massive amounts of data. Banking executives must acknowledge and embrace this fact and begin preparing for a revolution in data tracking and visualization.
