Risk management has become increasingly complex – and increasingly high-stakes. Regulatory requirements have grown in a wide range of industries, including banking and financial services. However, in many cases, businesses have not updated their systems and processes to match the new requirements. They are still using legacy systems – or even operating by hand or on paper-based systems, in a world that increasingly demands automation and real-time responses.

Generating stress in the system

The result of this is stress. Business risk users are struggling to manage slow systems that many do not fully understand. Data from different departments often gives different answers. And nobody knows which data is more accurate or reliable. Multiple sources of data mean that it is hard to get any definitive answers to questions – and that nobody trusts the answers anyway. Users want to bring their data together and generate answers across functions, but they don’t know how.

IT teams cannot keep up with the demands from users and executives for information. As the people responsible for data governance, they are under huge pressure to generate insights from data that they suspect may not be fit for purpose. The number of models in use continues to increase all the time. And IT teams are also responsible for good model governance, which is a heavy responsibility if your systems are not fit for purpose. IT staff want to understand what level of risk is posed by each model so that they can mitigate it. More, they need and want to move from model governance to model risk management, but they cannot do it alone.

Reliable information needed

Meanwhile, executives are short of information. They are unable to get what they need, when they need it, to make good business decisions, and the business suffers as a result. Many executives also find it hard to understand why information and insights are not available when they know that so much data is collected by teams across the organisation. They are putting increasing pressure on both IT teams and business users – but you cannot generate answers without reliable information.

All three groups can see that risk management needs to change. It needs to become more efficient, and more fit for purpose. Fundamentally, insights need to be available when and where they are needed – and they need to be reliable. Only then will businesses be able to comply with increasingly stringent regulatory requirements.

Changing the game in risk management

What precisely, then, is needed in risk management? Users want a user-friendly interface so that they can find what they need. They want a simulation environment with drag-and-drop advanced analytics so that they can run their own analyses without having to rely on the IT team for everything – a desire which is very definitely echoed by IT teams. A self-service analytics environment can save huge amounts of IT time and reduce frustration among business users. It also helps to ensure that data is more accurate because users know their data best and can see where there may be problems. And this, of course, also pleases the IT teams because it improves governance.

Executives want automatic production of reports on demand. They want to be able to visualise results from any location. They also want to be sure that those results are generated using data that they can trust, which means better governance of both data and models. IT teams want to be able to support relatively independent business users and deploy new programs and apps relatively easily without creating governance problems.

All this is possible. A good analytics platform can bring all these elements together and also provides other benefits. For a start, it makes it easier for users to collaborate. Bringing together risk and finance, for example, can have huge benefits for the business in both basic ways of working and also in generating crossover projects and new insights.

Moving into the future

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These are likely to be increasingly applied to risk management because they are so much more sophisticated. There is also an increasing need for real-time analysis of risk, and immediate decision processes, for example, for instant credits. I believe that the only way in which this can happen is by increasing automation and digitalisation of risk management.