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In charge of a financial services business? Can you sleep at night? 

The top 20 global banks paid more than £250bn in fines for misconduct over the last five years. In the UK the £38bn in combined industry provisions for PPI could have funded the London 2012 Olympics - four times over. And the new Senior Management Regime places individual accountability for misconduct firmly on the shoulders of an organisation’s leaders. Knowing all this would certainly keep me awake.

So, the consequences of misconduct are clear. What’s far less clear is how to minimise the risk of it happening. Not least because there isn’t a clear regulatory definition of conduct risk. It’s up to you to decide ‘what good looks like’ but one thing’s for sure, the definition of ‘good’ will start with knowing what data to use and where to get it from. Once that’s done, predictive risk data analytics are the answer. You need to combine internal and external data to be able to prevent behaviour that could get you in a whole heap of trouble. What’s more, predictive analytics can improve outcomes for customers, cut costs, improve decision-making and establish clear accountabilities.

Here are five things to do to put data analytics at the heart of your strategy for managing conduct risk.

  1. Break down silos
    You’ll need to be able to pull data from across Risk, HR, Finance and the front office. Both cultural and technical factors may have created silos, which you need to break down.
  2. Know your employees (KYE)
    It’s as important as KYC. HR data is critical to understanding and predicting conduct risk. It often provides the missing link to surface potential conduct risk cases. For example, combining data on sales targets and staff turnover with redacted employee information such as performance data could identify the profiles of people who may be vulnerable or under pressure to break ethical lines.
  3. Big data methods will make your life easier
    You might need to invest in new analytic tools capable of turning vast data sets into valuable insight, and improve data capture, storage, analytics and visualisation.
  4. Unleash the data scientists
    You’ll need specialist data scientists who can combine automated learning techniques, AI and statistics to manage the analytical toolkit. The insights they generate, both positive and negative, can then be fed back into the organisation: embedding awareness of the importance of good conduct.
  5. You can handle the truth
    To prevent insights from being compromised by poor data, set up an effective data governance environment that promotes stewardship and moves towards a single source of truth for key conduct risk information.

Those are the big picture things that can underpin your strategy. In the meantime, if you haven’t done them already, there are some quick ways to bolster your defences. Have you agreed the format, content and KRIs in your Conduct Risk MI dashboards? Have you defined clear lines of accountability for conduct risk data? Have you set up appropriate quality control measures for key conduct risk indicators? The list goes on…


Done? A full eight hours is on the cards. Sleep tight.

Contact the financial services team