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Alex Pui and Maura Feddersen explore the key cognitive biases and other drivers of judgement and decision-making in underwriting, and suggest methods to help underwriters overcome these factors.
From doctors to traders, behavioural economists have researched in detail what helps professionals achieve greater accuracy in their judgement and decision making. Yet, underwriting has not received the same attention. This is surprising, as underwriters are required to make decisions in the face of volatile, uncertain, complex and ambiguous (VUCA) environments, where cognitive biases tend to take effect. In particular, underwriters operating across volatile lines of business, such as large commercial risk sectors are exposed to low frequency and high severity natural catastrophe impacts. They also face data scarcity, adding to the challenge of dealing adeptly with VUCA contexts to arrive at an optimal decision1.
To detect and attribute bias as accurately as possible, we set out to do the following:
Based on our collective experience, and the aforementioned exercise, the following characteristics of judgement pose the most concern:
Optimism – “I’m sure the client’s flood defences are operational by now“
Poor calibration – “I don’t know how accurate my costing is, but I’m probably on the conservative side“
Noise – “I’m sure my colleague sees this case as I do”
Framing and confirmation bias –“Thankfully, there’s a model to rely on”
The crux of any behavioural intervention starts with an acceptance that we are all prone to cognitive biases and how these might undermine our judgement and decision making.
In response, curiosity and scepticism are helpful outlooks for underwriters to adopt and we encourage underwriters to ask questions about the information they work with as well as their confidence in their judgement at key moments in the underwriting process.
For example, if an underwriter’s assessment falls outside of the range they originally anticipated, this ought to trigger self-reflection and further scrutiny. Considering the range of possibilities rather than a single fixed outcome also reminds us of the degree of uncertainty and its drivers. A wide range might prompt us to seek more information and may alert others to take special care during the review. Furthermore, we can remind ourselves that models are not perfect – attaching a level of confidence associated with particular model outputs helps us and others remember how much reliance to place on them.
Consistent with studies3 that show outperformance associated with diversity of thought, explicitly seeking opposing evidence can help us address overconfidence and confirmation bias, while only considering one source of information (e.g. exclusively client or broker information) could be a red flag. At the point where underwriters are forming a view, salient questions can be asked: did you consider a variety of sources with different perspectives? What information is missing, and how did you deal with this? If we are proven wrong in χ years (known as ‘post-mortem’), why might it be so?
Self-awareness of whether an underwriter’s judgements tend to be overly optimistic or overly conservative can encourage self-corrective behaviours. For example, an underwriter making a ‘borderline’ decision, leaning towards writing something with fairly thin deal economics – but who knows about having demonstrated optimistic tendencies in the past, would then rightfully hold back.
Concluding, behavioural economics offers surprising insights that can boost underwriting performance. A natural starting point may involve conducting an assessment of the degree of noise and bias in underwriting, with surveys corroborated by historical performance metrics. Ancillary benefits of this approach include generating buy-in and increased underwriter engagement as it can be an eye-opening experience. Training, tools for decision-making and opportunities to receive feedback can help reduce bias and hence lead to better underwriting outcomes. This is worthwhile, particularly in high uncertainty/non-homogenous regimes, where expert judgement will continue to outperform a purely algorithmic approach but can still benefit from improved consistency in the decision making process.