The financial markets are beginning to take stock of the findings and impact that behavioural science has to offer especially in regards to behaviour change (Elliott et al., 2010). Recently, there is a growing use of the body of behavioural science literature being utilised in policy research and design in the current climate (Dolan, Elliott, et al., 2012). Financial behaviour has seen a growing interest in the last few years, with the Financial Conduct Authority (FCA) setting up their own behavioural economics and data science unit. In this time the FCA has used behavioural science and behavioural economics as a theoretical tool to inform policy and research. For instance, in one randomised controlled trial, the FCA suggest that limited attention and a present bias could explain why consumers often leave savings stagnated in their savings accounts after the interest rate ends. Consumers leaving money in these accounts are losing out on interest due to the inability to actively switch. The FCA randomised a series of letters to consumers with some not receiving any letter, as a control group. The results demonstrated an 8% increase in switching behaviour.
Future work could see the FCA and other institutions doing more to provide a behavioural diagnosis of the problem behaviour. For the above example, how well does the present bias and a limited attention explain the current lack of switching? Expressed as a formative model this could be used to provide a diagnosis of the inability to switch,
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which could be compared to alternative models. Through this approach then the utilisation of a randomised controlled trial would be cost-effective. As such, the financial sector should make full use of not only behavioural science literature but also of implementation science, such as the MRC guidelines (Campbell et al., 2000; Craig et al., 2008). Although the MRC guidelines are an iterative time-consuming process, however, even a simple policy guide framework would be extensively useful, as conducted as part of the current study, in reducing unnecessary waste of time and resources through sub- optimal policies. This could be perhaps be a sequential three-part process: in which a conceptual review is initiated, then to a behavioural diagnosis using qualitative or quantitative methods, and finally to implement some experimental work to design and test interventions. This may be through lab-based or natural experiments or even as randomised-controlled trials. For instance designing a lab-based mechanistic paradigm could be used to provide a cost-effective demonstration of potential interventions and their corresponding effects. The most effective intervention could therefore be implemented. The cost of a lab-based experiment would only be a fraction of the randomised-controlled trial and could even be implemented online, saving time and resources. Take the FCA research into consumer switching behaviour, here the researchers made no attempt of behavioural diagnosis and therefore the link between the interventions ran and the behaviour is made through top-down, goal-orientated assumptions. However, these assumptions may be valid, but not explain the behaviour as would an alternative model or hypothesis. This falls into the ISLAGIATT problem, where policy-makers often run interventions or policies test their assumptions rather than testing interventions to combat the most powerful explanation of the problem (Atkins & Michie, 2015; Michie et al., 2014).
This methodology offers an investigative, procedural format to maximize efficiency in reaching the optimal policy. Policy-makers and regulatory bodies such as the FCA should look to utilising a behavioural diagnosis, then towards experimentation. This could be done through quantitative modelling and then analysing natural experiments. For instance, using regression discontinuity designs where randomised controlled trials are not practical or even lab-based experiments. For example, stripping behaviour down to potential mechanisms, such as looking at individuals willingness to repay based upon an exemplar credit-card statement (Stewart, 2009) in which an experimental group were shown minimum payment required. The research matched the
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real-world behaviours for those who saw the minimum payment information, where this minimum payment amount predicted partial payments; furthermore the distribution of partial payments matched the real-world distribution. This behavioural paradigm allows policy-makers and researchers to make a behavioural diagnosis, and target interventions or policies based upon such outcomes. For instance, in the credit-card repayment example, removing the minimum payment information raised mean payments by 70% demonstrating a staggering effect size (d = .51) from a simple intervention which is highly-cost effective. This also reduces the chances of faulty assumptions or anecdotal evidence. This may produce an effect, but implementing a behavioural diagnosis and identifying the causal mechanisms allows policy-makers and researchers to implement more cost-effective based upon accurate and detailed evidence of causal factors.
In investigating individual differences in intervention efficacy this was able to identify how deficits in goal-directedness, as through constructs such as impulsivity, can often hinder savings behaviour. This approach could be of use within the personal finance and banking sector, where research can be used to segregate the populations more efficiently through psychological insights of behaviour. This could be done, for example, to reduce consumption in impulsive individuals by designing specific products to counteract such impulsivity. Furthermore regulatory institutions may seek to optimise policy by taking these constructs into account, in how to reduce delinquency rates of defaulting on credit-cards or loans. By segregating the population, this also offers an additional component by identifying the goal-directed fraction of the population, who would provide desirable outcomes.
8.5 Conclusion
This thesis used the MRC framework (Campbell et al., 2000; Craig et al., 2008) to diagnose and identify sub-optimal financial capability behaviours. These behavioural mechanisms were used to design interventions (under a bottom-up, design perspective) (Niedderer et al., 2016). These interventions were piloted and then tested in a fully- powered randomised controlled trial, which identified significant intervention effects for the Goal-Setting intervention, whilst diminished effects for the Habit-Based intervention. Furthermore, investigations of moderating effects of individual differences, identified deficits in goal-direction predicted poorer intervention efficacy.
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These results demonstrate how the MRC approach in conjunction with a design process can optimise the design and implementation of interventions or policy. This process can increase efficiency in policy design by highlighting causal factors to target and intervene (Atkins & Michie, 2015). This is the first time that such a process has been utilised in the financial sector, and demonstrates success in improving savings behaviour amongst a university population. Intervention efficacy was predicted by the degree to which an individual was goal-directed, producing greater savings the more goal-directed they were.
The formative research identified how behavioural motivation was a major component of financial capability, across all three areas of Keeping track, Making ends meet and Planning ahead. This finding is of paramount importance in development of household financial policy, for policies regarding financial education and financial literacy. These insights correspond to psychological theories of motivation of impulsive and compulsive behaviours (Black, 2007b). These insights, in addition to the results of the RCT and endophenotyping study, demonstrated a consistent finding that goal- directedness translates into better intervention outcomes. These results support the ideas from computational neuroscience and psychiatry which demonstrates how goal- directedness is a causal factor of sub-optimal behaviour (Gillan, Kosinski, et al., 2016; Huys et al., 2015; Montague, Dolan, Friston, & Dayan, 2012; Rouault et al., 2018), which this thesis demonstrated was responsible for sub-optimal savings. In these studies, I demonstrate how goal-directedness can offer a buffer in the maintenance of financial goals in the example of savings behaviour through an intervention. These insights map to fundamental behavioural constructs that are causally related to sub-optimal financial behaviours, and stem further across contexts (Rouault et al., 2018).