Banks must refine and modernise their approach to data management to be effective in the era of the Quantified Self
The world is undergoing a data revolution. We are in the midst of the Quantified Self and real time personal data and performance measurement.
With smart devices we can now track our movements, monitor food intake and measure changes in physical performance. With measurement comes insight (and a change in behaviour). We can better understand what drives us, modify our eating and sleeping patterns and enhance our performance in any given activity.
How can the banking industry apply the concept of Quantified Self to their conduct risk management and performance improvement?
Admittedly, banks are infinitely more complex than the average consumer. But they also have more sophisticated systems and armies of analysts and data experts to review and interpret both customer and internal data. Moreover, much of the data collection and analysis is already undertaken for internal strategic reviews, as part of incident de-briefs or for inclusion in investor and shareholder reports.
Banks would only need to refine and modernise their approach to data management to make it as effective as the Quantified Self.
To do this, we must understand what makes the Quantified Self such a powerful concept:
- Real-time data: The true power of the data used by the average consumer and the Quantified Self is that it is available in (near) real-time. This enables the user to understand issues and compare and analyse trends immediately, which accelerates and improves decision making.
- Data relationships: By combining seemingly unrelated data sources, the user is in a better position to understand issues and make decisions. The combination of this data and appreciation of its previously unidentified relationships between different sources may yield new insights, inspire solutions for complex challenges and identify key trends.
For example, we can gain new insights into trader behaviour through a comparison of different data sources, such as the nature and timing of trade cancellations / amends, login patterns and analysis of training records (modules completed, missed and pass marks). Furthermore, analysis of inter-team communications, such as email traffic, may shed light on relationships which pose a conflict of interest risk, for example, between the Front Office and Control Functions.
- Data visualisation: As well as being real time, the data is often presented in an easy to digest and visually stimulating way. This helps to further enhance the user’s understanding, and enables vast quantities of data to be presented succinctly. Uptake of visual analysis is greater than ever - no surprise since most people prefer data presented visually. Digital banking apps are experiencing a similar change with data analysis presented in graphs and graphics that inform the user of their spending habits. It is no great leap to apply the same principles of design to in-house risk reporting.
The application of these three principles to conduct risk management could drive a marked change in behaviour and management decision making within a financial services institution. Further down the line, this will culminate in a change to a bank’s wider culture and behaviour, with HR and management better positioned to understand what is and isn’t working.
Ultimately, the banks armed with composite datasets and real-time trend and exception analysis will be more proactive and risk-aware than their peers, avoiding the risk-related failures headlines that are all too common in recent times. These banks will be able to make evidence-based decisions in response to conduct issues and will be empowered to modify their approach to people, process and technology with greater awareness and knowledge of how their workforce and business models operate.
Speed and efficiency of implementation is dependent on the bank’s ‘openness to change’ and the senior management’s commitment to change. A proactive approach to conduct risk management must therefore become embedded in the culture and values of the firm, with strong direction from those in management positions.
Moreover, the old adage that ‘you get out what you put in’ has never been truer. Banks must clean up their data, ensuring it is accurate and MECE (mutually exclusive, collectively exhaustive) so as to draw the right conclusions. Trust is paramount between banks and their customers and ‘watertight’ data is a must to maintaining trust and efficiency. Finally, a coherent and adaptable set of KPIs must be developed. It is important that the management know what they are measuring and why and that the data exists to provide relevant insight. Even more important is the evolution of these KPIs to keep pace with new regulations and customer needs.
With management’s commitment the Quantified Bank can match the expectations of 21st century customers whose consumer experiences are increasingly shaped not just by smartphones, but the panoply of wearable devices now available.