Strengthen Risk Management
Discover, Evaluate, Improve & Monitor Risk
Our technology focuses on Behavioural Risk - the type of risk related to the way things are done in an organisation and the invisible human drivers underlying those behaviours. A sample of risk related behaviours we have improved, includes:
- Align with the tone from the top
- Engender accountability and ownership
- Reinforce speaking-up and listening-up
- Prevent misconduct and unethical behaviour
Case studies of how we have improved the above behaviours are provided below.
Our technology can be applied in a completely transparent and privacy-ensuring manner to:
- Discover: Proactively identify behavioural risk hotspots, reveal hidden patterns of behaviour and benchmark better practice
- Evaluate: Automatically measure known behavioural risk issues to determine their likelihood and impact
- Improve: Rapidly enhance and ingrain the desired risk behaviour
- Monitor: Accurately predict the effectiveness of behavioural risk interventions
- Our client is a financial services organisation seeking to measure the tone at the top. This refers to an organisation’s general ethical climate, as established by its board of directors and senior management. Having good tone at the top can help prevent fraud and other unethical practices.
- We used GalaxyLens to measure and predict the degree to which the tone at the top had dispersed and been adopted throughout the organisation.
The analysis involved observing the level of awareness and interaction between teams on key pieces of communication from the top, or senior management. This identified previously unknown information flow bottlenecks and communication deterioration amongst certain teams.
This was based on understanding the invisible social networks of communication within organisations rather than just the formal hierarchical structures. Up to 70% of knowledge flow can take place through these organisational social networks.
The interventions to improve the alignment with the tone at the top focused at the aggregated team level. This involved the identification of the best social network pathways for these teams to interact with the top and others on key information.
This resulted in an average 63% increase in the dispersion of key information from the top throughout the organisation. This was not based on the level of communication volume (where an increase is not necessarily desirable), but the level of communication diffusion, or awareness.
- Our client is a diversified technology company seeking to enhance the level of accountability and ownership amongst its people.
- A combination of GalaxyLens and GalaxyScope was deployed to measure and predict the degree of accountability and ownership.
- This included the unique analysis of virtual tribes to determine the morals, ethics and accountability at an aggregated team level.
We initially deployed GalaxyScope to analyse the communication patterns of the general global public (not employees of the company) in relation to the specific morals, ethics and accountability issues relevant to our client. This involved the identification of virtual tribes on the web which are groups of people who share the same values and collective awareness. Sources include social media platforms like Twitter, Wikipedia and subject-specific forums.
This provides a very rich source of machine learning that enables algorithms to develop a much higher level of predictive accuracy when identifying the same behaviours within organisations, with naturally smaller data samples compared to the web. The communication characteristics of the identified virtual tribes were then compared to the communication attributes of our client’s workforce to identify relevant tribes within its organisation.
For example, this included the identification of internal organisational tribes on the basis of the Schwartz Theory of Moral Values (a well-known model for ten universal human values). The virtual tribe analysis conducted through GalaxyScope was supplemented with an analysis of internal client communication patterns and social networks using GalaxyLens.
- The combination of GalaxyLens and GalaxyScope identified hot spots and focus areas of potential unethical behaviour and poor accountability. This was conducted at a team level and not for individuals.
- This enabled our client to undertake highly proactive interventions to prevent teams from digressing into unwanted behaviour, whilst strengthening the desired behaviour of the better performing teams.
- Our client is a medium sized company in the manufacturing sector seeking to reinforce speak-up and listen-up culture. This is also related to psychological safety.
- GalaxyLens was deployed to identify the level of positive and negative sentiment in email communication patterns.
- The analysis focused on the use of “honest language” in email communication.
The inclusion of content related data, such as analysing the type of sentiment in communication, can understandably raise concerns regarding individual privacy. This can be effectively addressed by encrypting the data at the source (thereby rendering the content indecipherable) and aggregating the reporting at team level (typically, 10 people and above) so that individuals cannot be identified.
When these security and privacy controls are applied, the results from the analysis are reported on the basis of summarised and anonymised team patterns, which protect privacy whilst providing valuable insight into the sentiment and level of psychological safety being experienced by teams. In this case, our analysis focused on the ratio of positive and negative sentiment.
Perhaps somewhat counterintuitively, a disproportionate amount of positive sentiment can reflect an increased level of fear. When people fear speaking up or calling out bad news, the general sentiment is typically skewed towards the positive. Whilst there is nothing wrong with positive sentiment per se, an appropriate balance of positive and negative sentiment means that people are more likely to speak up because they are also in the habit of expressing concerns. This balance of positive and negative sentiment reflects the use of “honest language”.
- The use of honest language in the expression of sentiment must come from the top. If senior management embrace and encourage honest language, the rest of the organisation is more likely to do the same.
- In this case, we improved the balance of positive and negative sentiment by 15%, which increased the level of honest language and enhanced the environment for speaking up.
- Our client is a diversified financial services organisation seeking to identify areas of potential misconduct and increased behavioural risk
- GalaxyLens was deployed to proactively detect hotspots and potential areas of focus at the team level.
Our technology is not intended to function as a strict surveillance tool. It's real value is in providing highly accurate measurement and prediction of areas of the business that may be more susceptible or predisposed towards misconduct. This enables the organisation to proactively intervene in these areas before material problems arise.
In this way, our technology is designed to prevent misconduct before potential bad actors have the opportunity to engage in unwanted behaviour.
In this case, we used a unique combination of social network analysis to identify hidden pathways of potential collusion, dynamic analysis of communication patterns to reveal behaviour consistent with misconduct and content analysis to demonstrate language reflective of unwanted behaviour.
This included a back-test of known bad actors within the organisation. The intention of the back-testing was to help calibrate our algorithm for this particular client's environment and demonstrate the predictive accuracy of our analysis.
- Our analysis enabled the organisation to target potential hotspots with laser-like accuracy in a highly proactive basis.
- The predictive accuracy of our back-testing on known bad actors was over 90%.
- Email Network Patterns and Body Language Predict Risk-Taking Attitude
- Silence is golden: the role of team coordination in health operations
- Heart Beats Brain - Measuring Moral Beliefs Through E-Mail Analysis
- Measuring Workload and Performance of Surgeons Using Body Sensors of Smartwatches
- Predictions for Employee Turnover with Gated Recurrent Neural Networks
- Resilience Through Collaborative Networks in Emerging Economies
- Communicate or Perish. Predicting Innovative Behaviors Via Email Communication Analysis
- One in Four Is Enough - Strategies for Selecting Ego Mailboxes for a Group Network View.
- Ethical Issues in Virtual Communities of Innovation
Data Sources, Privacy & Security
Our technology can securely capture and analyse any text, voice or video data stored by an organisation on its servers, whether on premises or in the cloud. This includes email, calendar, collaboration and chat apps and voice and video recordings.
Our analysis strongly respects individual privacy. Only aggregated information is available to management, while individual results are only accessible by the relevant individual. This ensures full GDPR compliance whilst continuing to achieve the optimum level of analytical insight and accuracy.
All data is fully encrypted on the server side, while the analysis takes place on anonymized and encrypted data to ensure maximum information security. This means that data is also protected from potential internal abuse.