By Dr Wieke Scholten and David Grosse
BRM acknowledges that “how we do things at work” has a significant impact on organisational outcomes. This includes desired outcomes, such as performance and innovation, and unsought outcomes, such as misconduct and control failings. Equally, it effects the long term, including operational resilience and the meeting of strategic goals, and the current position, with decisions and actions taken in the moment.
Yet how employees behave at work is driven by many aspects of their direct professional and social context, including the teams they are a member of, the direct leadership they receive and the processes they work with. Mitigating the risk of poor outcomes driven by these behaviors therefore requires understanding which of the underlying factors is playing a key role and how they can be shaped. BRM is consequently crucial to forward looking and pre-emptive risk management as it seeks to tackle issues at their root cause.
BRM work within financial services firms is hence characterized by a focus on how employees behave in reality. It is not about great intentions or stated aspirations on what we would want our organizational cultures to be like. It is about what actually happens and operational reality on the shopfloor.
Employees are often well-intentioned, but can still demonstrate undesired behaviors because they are working with difficult processes that encourage those actions and decisions, or where they observe behavioral norms that point them in a different direction.
Over the last decade, the financial services industry has taken it’s first steps in effectively implementing BRM, primarily by creating teams that have expertise in identifying behavioral risk and applying interventions to help with performance or to prevent problems.
Some great examples are Citigroup, Royal Bank of Canada, ABN AMRO, ING, NatWest and Standard Chartered Bank. Additionally, regulators are increasingly incorporating behavioral risk in their supervisory approaches, such as the Dutch regulators DNB and AFM, the Australian APRA, the Canadian OSFI, the HKMA in Hong Kong, MAS in Singapore and the Irish CBI. The New York Federal Reserve Bank is also encouraging financial institutions to improve their management of behavioral risk and are looking to enhance their own supervisory capability.
These efforts have resulted in some essential lessons, including that understanding and managing behavioral risk requires an analysis of behavioral data, and that enhancing the tools and information sets used to generate these insights is of great value.
Step 1. Use data to search for key areas of focus
The first step is to identify the behavioral risk landscape across your organisation. Where are the hotspots and green spots? Where should you focus your work? Where is it more likely that behavioral risk will materialise, or where are the areas that we can learn from or are exemplary? The classic means of identification are to join disparate dots of data in performance (i.e. outliers, fluctuations), risk (i.e. alerts, breaches, speak up, investigations) and HR (i.e. surveys, retention, attrition, etc).
In our experience, these ways to identify areas of focus may fall short because of subjectivity, poor data quaity and dubious risk reporting. There is therefore an opportunity to use advanced behavioral data technology to get a more objective, accurate and reliable outcome.
Step 2. Use data to further explore and understand what is going on
The second step is to assess behavioral risk within the identified focus areas. This often entails a deeper dive into these teams, functions or locations to reveal behavioral patterns and drivers that may be driving negative or positive outcomes. These approaches encompass triangulating qualitative and quantitative data derived from confidential conversations with employees, focus groups, observations, desktop reviews and surveys. This may reveal a sludgy process, a troublesome relationship or unexpected habits and norms.
Whilst these deep dive reviews are proven effective in revealing the behaviors and drivers that should be addressed to prevent issues, they are hard to scale. We see an opportunity here to add more sophisticated behavioral tools that will shed light on patterns of communication, influence and co-operation and also give insight into both the current state and trends over time.
Step 3. Use data to measure progress and prompt action
Based on the outcome of step 2, you will have an understanding of what areas to address to help deliver strategy or prevent future issues.
The next step is to shape the daily context that employees work in, in such a way that it makes it easier for them to excel and do the right thing, increasing the likelihood of positive outcomes.
Advanced data analysis can be used here to:
- Build self-awareness for managers and teams to help them self reflect and shape their actions going forward. In the same way smart watches encourage exercise, behavioral insights have been shown to prompt action.
- Leverage the understanding of how influence and information flows through an organization to tailor interventions.
- Measure progress against any solutions put in place, helping test efficacy and to learn what actions result in sustainable behavioral change.
In summary, BRM approaches are developing rapidly across the industry and the next step will be to incorporate more advanced behavioral data technology and AI solutions.
Companies should also be aware that key stakeholders outside of their organisation are already looking at any available big data to help them gain an understanding of cultural strengths and weaknesses, whether investors for asset selection, rating agencies for ESG metrics, or regulators to identify concerns. Having a robust approach internally is vital to not be caught by surprise an informed inquiry.
For further reading on BRM with practical examples from the financial services industry, please see this article.
