Building a Rich Set of Loss Data for Risk Analysis

The phrase ‘prevention is better than cure’ is often quoted in the context of healthcare. But this phrase rings equally true in risk management. Through the lens of a risk manager, “prevention” refers to the sum of steps taken to reduce risk and prevent losses, while “cure” refers to recovery from losses that do occur. The availability of predictive data analytics and AI-powered tools offers enormous potential to turbo-charge the effectiveness of risk reduction and loss prevention efforts.

However, the effectiveness of this technology depends on the quality of the data that is available. To take advantage of this technology, risk managers must be invested in the process of collecting and organising the relevant data. The responsibility of steering the data collection effort does not rest with the IT team, but with every business department that wants to harness it.

In this session, we discuss data that is related to losses for which insurance has been arranged. We provide our views on how the expectations of insurance stakeholders are evolving and present a solution we have designed to meet those expectations. We share a case study involving a global organisation and how our solution has helped them address challenges in consolidating loss data.

Lastly, we provide some suggestions on how risk managers can start a similar journey within their organisation.