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Let's have a goals-based advice conversation.


After the recent Royal Commission into Banking and Financial Services, findings highlighted that financial advice lacks a clear and transparent pathway toward meeting consumers' goals & objectives. 

The old world financial model is broken. 

Legacy technology platforms, along with an industry built on product distribution, has played a prejudicial role in inadequately taking a client-centric approach.

This old-world has been the backbone of this industry and it is failing consumers by these factors:

  • does not measure a client's goals, ethics & neuroscience as the core outcomes for advice strategies & investment product suitability,
  • does not profile investment risk to include a client's goal achievability, risk capacity & tolerance collaboratively,
  • does not profile a client's behaviour by understanding their neuroscience that influences their money behaviours & goal outcomes,
  • does not track financial advice to the client's goals as part of ongoing service arrangements, and
  • does not comply with regulators by compliantly & proactively monitoring advice & product suitability.

This gap in the current financial services model and technology needs to be met in order for this industry to thrive again.

Chapter 01 - Goals-based advice


Financial planning, by definition, is a goals-based conversation

Goals-based advice may be a new phrase for some, but it addresses an issue that’s been around for a long time – namely, connecting a client’s life goals, ethics and behaviours directly to advice strategies and their investment portfolio.

Goals-based advice makes the advice process more engaging, relevant and meaningful, as well as improving a client’s chances of achieving their dreams. By placing their goals front and centre, clients are constantly reminded of the ‘why’ that’s driving them, giving them a greater sense of control over their future. 

This sense of connection to long-term goals is hard to achieve using any other methodology. The main problem we’ve faced? Until now there has been no financial planning technology that makes goals-based advice an easy and interactive process.

People generally don't talk about money, not even with their kids. Yet money is our "Currency for Life". People don't learn about money at school, so where do people learn about what to do with their money when there is no formal education around money? And more importantly, how do they relate money to their behaviour, life aspirations and goals?

Have you ever heard the saying “a goal is a dream written down” or "a goal without a plan is only a dream"?

Two beautiful sayings, however, there is a little more to it, so let explore goals-based advice.

Why are goals important?

Goals are outcomes-focused

Goals require clients to look forward and ask what they’d like to achieve across their projected life journey. Once they’re in this mind set, it’s much simpler to map back to optimal advice strategies and investments.

Goals facilitate scenario planning

Goals help facilitate the advice conversation, illustrating to clients the probability of what they "can" or "cannot" achieve based on their current behaviours and financial circumstances. More than that, this approach allows clients to visualise how different "trade-offs" and "what-if" scenarios impact their future, from life events to economic crashes.

Goals improve life achievements

Goals-based advice demonstrates the true value of advice. By allowing clients to directly track advice strategies and investments to their specific life goals, they’re able to identify whether they’re "on" or 'off" track throughout every step of their journey.

Did you know


Why don't people set goals?

  • They don't know how to
  • They fear failing
  • They don't realise the importance

Why don't people achieve their goals?

  • Language, self-talk and pre-conditioned behavioural patterns
  • No tools to help guide, educate, act, track and motivate

Chapter 02 - Behavioural science


The behavioural science of goals-based advice

How the brains neuroscience creates our reality!

Significant emotional events don’t just affect us emotionally, they can dictate how we make financial decisions and drive our behaviours. Not only that, money is a subject that elicits strong emotional responses in all of us; feelings that shape our actions for better or worse.

Let’s look at one of the most powerful influences on human behaviour: the brain.

The brain is responsible for our thoughts, feelings, intelligence, the nervous system, motor skills, breathing, heartbeat and sleep. It facilitates almost every aspect of our lives.

Let’s break down what happens within our brain and the biochemistry around our thoughts and behaviours.

Our experiences are made up of a series of sensory functions: things we see, hear, smell, touch and taste. Every day, we collect these experiences – and over time, we collate them into learned behaviours (such as ‘I won’t touch the stove because I know it’s hot’). As this happens, neural pathways within our brain are created and conditioned (our ‘go-to’ ways of thinking and acting) and these produce the biochemicals that facilitate our emotional responses to our experiences.

Every time we have an experience, our frontal lobe (the ‘control panel’ of our brain) sends neuron signals across the brain. These electric signals tend to follow established neutral pathways – the ones we’ve created over time. They then hit our pituitary and pineal glands, secreting specific biological chemicals – such as serotonin, melatonin and dopamine. These trigger our emotions, feelings and memories.

With our thoughts so closely connected to our emotions and moods, our brain produces electrical activity in the form of waves, sent into our outer world. (Alpha brainwaves, being our conscious mind, and beta brainwaves our subconscious.) Our unique brainwave patterns virtually connect us wirelessly to the unified (quantum) field, science links this to consciousness. Our brainwave patterns and daily experiences of the world are inseparable, they essentially form our reality.

“What you think, you become. What you vibrate, you attract. What you dream, you can create.”

Jacqui Henderson



So what does neuroscience have to do with money?

As we’ve established, money is a stressful subject for most people. Many have experienced a lifetime of sensory experiences that link money to stress, survival, short-term thinking, lack or trauma. As a result, negative sentiments such as 'it's hard to save money' or 'there's never enough money' become automatic – and we program ourselves to have a negative relationship with money, without even realising it.

Fortunately, we can reprogram our brains to align to the type of behaviour that will enable positive money relationships and support in attaining our life goals.

To change these innate biochemical brain patterns, we must begin by understanding and recognising them. From there, we need to train our thoughts to focus on the exact opposite scenario.

For instance, if we believe there’s ‘never enough money’, our thoughts need to focus on an 'abundance of money'. If we live in a survival state where it’s all about ‘getting by in the now’, we need to re-frame our thinking to focus on a ‘long-term state of security’.

It may sound like dreaming, but it’s supported by science: the more we train our thoughts in a positive way, the more easily we can break free from our conditioned negativity.

Simply put, if you mentally embrace the future with clear positive thoughts, intentions and feelings, it is more likely to become your reality.

"Thoughts are the vocabulary of the brain, feelings are the vocabulary of the body."

Joe Dispenza


A deeper dive into the behavioural science that influences money and life goals

Human values, beliefs and behaviour can powerfully influence our relationship and thoughts toward money, in ways we’re often not aware of.

Let's explore the four main influences of money relationships.


Our Money Values

Money values stem from a persons ideals and beliefs around money. For example: a person may value money by perceiving that it provides them with - freedom, status or power.

Values impact our financial decisions as much as they impact our other life decisions. Research has found that our values influence everything we say and do (Coletto, Lucchese, & Orlando, 2018; Teo, 2018) and in the financial realm, our values also influence our consumption patterns (Halim & Dinaroe, 2019). 

Our money values are not dissimilar from our general life values, and the implications of the values on our behaviour is similarly widespread. For example: someone who values ‘family’ may not only spend a lot of time on the phone to close family members, however, they may spend their holidays visiting relatives, at great expense. The implications of values are thus linked with financial outcomes. 

Our values comprise of contextual beliefs and cognitive strategies (McClendon, 2019). Our contextual beliefs include; how we feel when we talk about our beliefs and cognitive strategies are what directly influence our behaviour (McClendon, 2019).

Values are generally related to intrinsic, extrinsic, and interpersonal realms (Liu, 2017). Perspectives of money can be that money brings - protection, influence, or security. The process of establishing our values into financial behaviours include: noticing; thinking; emoting; sorting; valuing; choosing; and finally behaving (McClendon, 2019) 

Boyd identified 11 core money values, these include: (Boyd et al., 2015): 

  • Certainty 
  • Freedom 
  • Faith 
  • Empathy 
  • Family 
  • Growth 
  • Work 
  • Decision making 
  • Honesty 
  • Openness 
  • Knowledge 

Money values are part of our decision making framework. They lie at the core of connecting advisers with their individual clients, and in helping them to prioritise their life goals. 


Our Money Beliefs

Money beliefs are a persons beliefs that are adopted from their environment or past experiences. For example: a belief that "money destroys lives" may have been ingrained from a childhood traumatic experience with money.

Often our beliefs about money impact the way we relate to and think about money. We tend to carry these money beliefs in our adult life, often learned in our childhood. (Furnham, 1996; Kirkcaldy & Furnham, 1993)

Unfortunately, money beliefs may not be helpful, especially if parental role modelling demonstrated an unhealthy relationship with money, which can leave a long lasting imprint around the role money plays in a persons life.

Klontz and Klontz (2009) hypothesised that money beliefs in individuals are: 

  • developed in childhood,
  • often passed down from generation to generation in family systems,
  • typically unconscious,
  • contextually bound and;
  • a factor that drives much of one’s financial behaviours. 

There's interesting research that shows that beliefs about one’s self-worth positively correlates with financial satisfaction and positive perceptions of one’s past, present, and future financial situation, and in contrast negatively correlate with overspending and financial worry (Hira & Mugenda, 1999).

The Klontz -MSI research on money beliefs provides an update to Yamauchi and Templer’s (1982) Money Attitude Scale and Furnham’s (1984) Money Beliefs and Behaviours Scale. 

The Klontz-Money Script Inventory (Klontz-MSI) assessed potential problematic attitudes and beliefs of individuals that may interfere with them achieving their financial goals. 

The analysis revealed four distinct money beliefs:

  • money avoidance,
  • money worship,
  • money status and;
  • money vigilance.

By understanding and exploring an individuals beliefs and ideologies around money, advisers can assist their clients to identify their belief challenges and encourage them to develop new positive beliefs.


Our Money Behaviours

Money behaviours are the way a person acts and responds toward money. For example: a set of behaviours such as; self-control, motivation, optimism and present bias can often influence ones relationship with money.

By understanding the different behaviours that impact our connection with money, we can develop new positive behaviours that help us to achieve our desired outcomes - our goals and life aspirations.

Below we explore the types of common behaviours that influence money relationships, often acting as barriers that hinder people from achieving their life goals.


People can generally be described as either moving toward reward, or away from pain. These underlying differences impact our entire values, attitudes, and decision-making systems. 

Money motivation systems have been described by Belk and Wallendorf (1990) and Yamauchi and Templer (1982) and include associations with: 

  • Value 
  • Utility 
  • Wealth 
  • Meaning 
  • Emotion 

Toward Reward 

Those motivated by reward or gain have been found by researchers to have higher performance (McClendon, 2019). The approach to communicating with those who are motivated by gain or reward is to offer encouragement and rewards (McClendon, 2019). When a client is identified as being motivated by reward, positive goal-achievement language can ensure the client is engaged in the communication.  

People who have been exposed to seeing money in a positive light are more likely to seek out more positive feelings and experiences. They will attract people with similar belief systems and values around money, and they will work ‘towards’ achieving outcomes and goals, as long as it provides them with positive emotions and its pleasurable. This ‘toward response’ means they’ll prefer to set goals related to the things they desire and want to have in their lives.

Away from Pain 

While those who are motivated by gain have higher performance, those who are motivated to avoid pain often illustrate lower performance. This is intuitive given that the avoidance of pain is somewhat finite, measurable, definable, and achievable. On the other hand, seeking reward and recognition can include goal-creep and continual striving for success. When a client is identified as being motivated to avoid pain, negative pain-avoidance language can engage the client in the approach and the communication. 

People who have had exposure to a negative environment when it comes to money, they will tend to dislike or even avoid thinking or talking about money.  They will tend to be driven to take action ‘away’ from pain and negative emotions, and this ‘away response’ means they tend to set goals linked to the things they’re afraid of happening (or losing).

The good news? Everyone is capable of setting goals, whether they’re motivated ‘toward’ or ‘away’. It’s just a matter of firstly understanding their motivation system.



Self-control is a persons ability to regulate their emotions, thoughts, and behaviour in the face of cause and effect. Self-control is a cognitive process that is necessary for regulating one's behaviour in order to achieve specific goals.

People with high self-control are more likely to stick to a plan and achieve their goals. They can maintain regular savings and have control over their spending. This means there is less concern around them harming their financial outcomes.

In contrast, those with low self-control have less likelihood of following a plan and achieving their goals. Although they may work hard for their money, it seems to disappear just as quickly. This type of client may benefit greatly from mentoring.



Optimism is a persons capacity to positively influence their life outcomes, like having the ability to see opportunity within adversity. 

People who are overly optimistic tend to be linked to being high risk takers, entrepreneurs, they work less, have less saving and likely to retire earlier. These people are not overly concerned with fees, costs, or interest rates, they tend to be able to see the long-term benefit of upfront expenses for long-term financial gain.

In contrast, people who have low optimism have a more realistic and a negative expectation of their future world. They tend to be sceptical of future success, unlikely to take risks, they're very hard working, good at saving towards a goal, however, worry they won't have enough and they're more likely to retire later. 

Optimism plays into a persons fixed or growth mindset. Having a fixed mindset, as the name implies, increases the limitations you have in your life. Optimists believe the glass is half full, whilst pessimists believe it’s empty. A leading Stanford University psychologist Dr Carol Dweck, found that those with fixed mindsets believe their intellect is static, whilst those with growth mindsets strongly affirm their intellect evolves. 


Present bias (time horizon)

Time horizon is closely linked with the propensity to spend versus save, because of prioritisation of the present (ie. strong present-bias) means the future (and savings) receives less attention.

People are either past, present, or future focussed (McClendon, 2019) and they are generally aware of their time preference.

Research has shown that mental time horizon correlates with more than just savings behaviour; it has the same affect on investing and debt management. People who think ahead are more likely to keep track of their personal finances and spending, and promptly pay their bills – they’re even more likely to carry low to zero balances on their credit cards. 

A person who thinks short-term may prefer to receive twenty dollars today over receiving twenty five dollars tomorrow (instant gratification). Whereas a person that thinks long-term may prefer to hold out for twenty five dollars tomorrow.

Getting people to think long-term has many empirical benefits. Our brains are hard-wired to give more weight to immediate needs, and to discount the future. The shorter a person’s mental time horizon, the more they will find long-term saving to be a challenging and painful task. The below graph shows the correlation between those with higher retirement savings, had long-term mental thinking.

Retirement savings by income and mental time horizon


With this in mind, it’s always easiest to start goals based advice by looking at the client’s short-term goals (within the next 1-2 years) and extend outward toward long-term goals (5-20 years). We must always anchor this with their retirement age and life expectancy. The further ahead a person thinks, the better their financial behaviour, and the less they’ll worry about their future.

Learning Style - Visual, auditory & kinaesthetic

There are a number of approaches to learning style, including the visual, auditory & kinesthetic approach and the big-picture vs detail approach (McClendon, 2019). 

Some people learn by first understanding the big-picture, and after they understand that, learning about the details. On the other hand, some people learn about details first, how they fit together, and finally the big picture (McClendon, 2019). Understanding a clients learning style has powerful implications for all communication, both in digital, written, and face to face form.  

In order to get people to ‘understand’ financial advice, we have to deliver it in a way that connects to people’s preferred leaning styles.

As outlined in Howard Gardner’s Multiple Intelligences Theory, people are capable of learning in a range of ways: via words & language (linguistic), via numbers (logical-mathematical), through sound (auditory) or through images, diagrams or videos (visual).

Interestingly, if we break down peoples learning styles we find that:

  • 65% of people are visual learners, learning through visual aids - infographics, pictures, maps and diagrams
  • 30% of people are auditory learners, learning through listening, speaking, language and discussion
  • 5% are kinaesthetic learners, they need to act in a 'hands on' way

If we consider learning style alone, SoAs essentially omit 70% of the population.

This begs the question. How effective are SoAs?

Words have no power unless you know what they mean. With this in mind, it’s no surprise that traditional Statements of Advice (SoA) aren’t a powerful or appealing way to deliver information – after all, they are produced in a 'foreign language' that alienates and confuses people and they are presented in a format that most people simply don’t understand.

CoreData research shows that:

  • 24% of consumers don't read the SoA and;
  • 43% are confused by the content & language

Considering that 9 out of 10 consumers today use smartphone technology and 89% of their decision making activity is now conducted on their smartphone device - we ask ourselves; Why isn’t a financial plan delivered the way today’s consumer interacts?

Empowering clients is key

People who feel empowered in their financial lives experience more joy, peace, satisfaction, and pride in their financial lives. Those who felt dis-empowered (helpless, hopeless or stuck) were, overall, less happy.

Not surprising, really – but what is interesting is that goals-based advice actually provides a bridge to take clients from one state to another.

By enlightening people through financial literacy & education, and helping them understand and visualise what’s possible, we can empower them to play an active role in their futures. The very act of setting an achievable goal is empowering – even more so is when you give that same person a way of tracking their progress.

Rather than feeling like a victim at the mercy of their financial circumstances, they see the impacts of their actions and decisions – inspiring them to step up, take responsibility, and actually relish the improvements this brings about.

In the graph below, the blue line represented people who agreed with the statement: “I create my own financial destiny.” The orange line represents people who said instead that they: “Have very little power” in their financial lives.

Emotional well-being by income and empowerment


This illustrates that 'empowerment' is key. By transforming advice into a co-creation experience, where clients feel they are part of a journey, educated and informed along the way, they can develop a more confident relationship with money as a result.

Financial knowledge

Financial knowledge, or financial intelligence, has the potential to impact consumers financial decision making at all stages of the financial life cycle. This is because financial intelligence is not simply financial literacy, it is also the ability to monitor one’s own money motivations, money behaviours, and money cognition (Tang, 2016; Tang et al., 2018) 

These are skills which are far more difficult to acquire than simple financial literacy and even financial capability. This means that money intelligence includes the ability to reflect on one’s own motivations regarding money. In an ideal world we would be able to ask people for their motivations regarding money, but in reality this research indicates that some people will not even be aware of their own money motivations, and hence will incorrectly answer.

In addition, this research highlights that financial intelligence involves monitoring one’s own financial behaviours. We know from research that people seek financial advice because monitoring one’s own financial behaviours is demanding. Furthermore, this research emphasises the importance of thinking about money, and about the way consumers think about money.

It is our thoughts about our thoughts that can play a critical role in framing the way we approach money.

Money challenges

The way we consider money challenges depends on the frame we use to consider our financial situation, which is built up over our life from experiences and context.

Framing influences the perception of money challenges (Zamfir, 2019). In our everyday financial lives, when events are framed in terms of ‘blame, helplessness, or me’, counterproductive perceptions of money can be developed (Zamfir, 2019)

Given that so much of financial success relies on strong decision making, these counterproductive perceptions of money can be particularly difficult. 

These challenges can be overcome by putting events into a different frame, and this occurs through attitude change and clarity (Zamfir, 2019). The re-framing of challenges can help consumers to overcome counterproductive mindsets. Indeed, authors have found that digital technology can be leveraged to help people reduce how they perceive their financial challenges (Lewis & Perry, 2019). 


Our Money Risk Attitude

Money risk attitude is a persons ability, capacity, attitude and willingness to accept risk, which is the upside or downside exposure in order to achieve a desired investment outcome; as an example: a person with a high net-worth may take on higher risk due to their financial capacity.

Understanding a persons risk attitude, comprises of two main components; risk capacity and risk tolerance. We take a deeper dive into these within the Goals-based investing section of this white paper.


The neuroscience of goal setting and achievement

One inherent problem with goal-setting is related to how the brain works. Recent neuroscience research shows the brain works in a protective way, resistant to change – therefore, any goals that require substantial behavioural change, or thinking-pattern change, will automatically be resisted.

The brain is also wired to 'seek rewards' and 'avoid pain' or discomfort, including fear. This means that, when fear of failure creeps into the mind of the goal setter, it works as a ‘de-motivator’, encouraging them to return to known, comfortable behaviour and thought patterns. Conversely, when excitement and elation is in the mind of the goal setter, it acts as a ‘pro-motivator’, driving them to pursue those positive feelings of reward and goal achievement.

This means that unless the goal-setting process can be made to feel positive in some way, it will be an uphill battle.

Another challenge around goal-setting is the fact that it needs to be tangible in some way. In order to set a goal, a person needs to have a tangible ‘outcome’ in mind. This may be something they can physically see, feel, hear, or sense as an experience; a combination of Visual, Auditory and Kinaesthetic (VAK) stimulation.

Why a clear mental picture of goals is so important (Reticular Activation System - RAS)

The RAS is a filter in the human brain, to all our receptors, our vision, hearing, touch, taste and smell. This system sits in the centre of the brain, lighting up the entire neurology of the brain, and continually determining what input is important to us. Essentially, the RAS enables the brain to focus on what is necessary, and to delete what is not necessary. 

During the goal-setting process, the RAS becomes activated as we seek clarity on what matters most. The brain will then delete, distort and generalise information, so we can begin to hear, see and feel our goal around us. One example is that, if your goal is to buy a black Mercedes car, you will start to see them everywhere! 

When we can experience a goal with our senses; see, hear and feel the goal in a three-dimensional way, our ability to achieve the goal becomes more successful, taking it from an idea to a clear reality. The more abstract the goal, the less likely the goal will be achieved; the more clarity of the goal, the higher chance of success. 

Uniting goals and the financial advice process

According to goal-setting theorist, Edwin A Locke, goal-setting is experienced within five human psychological stages. These can be directly related to the goals based advice process.


There are several factors advisers need to be aware of to help their clients to make their goals more achievable.



Profiling Technology 

a.i.'s goals-based advice technology has an in-built behaviour profiler to assist advisers in determining their clients values, beliefs and behaviours toward money. It also determines their learning style, money personality and risk profile, which includes risk tolerance and risk capacity.

The profiler takes clients through a series of questions that provides valuable tips and insights into their personality types and behaviours.

Get a demo of our profiler feature to find out more.

Book a demo today


Chapter 03 - Multi-dimensional modelling


Goals-based advice is multi-dimensional

Modelling a household’s financial circumstances and future scenarios has always been an important tool for financial planners. It is a critical element in developing a robust and fact-based foundation around the advice financial planners provide – to ultimately help clients meet their life goals.

However, the traditional linear approach to this type of analysis often asks more questions than it answers. This leads to gaps that advisers fill through assumptions, rules of thumb and a host of other behavioural biases over facts.

Until recently, limited computation power meant that providing true asset liability and cash flow calculations was nigh impossible. Most financial planning software and excel spreadsheets, of what forms the basis of advice modelling in this industry, are linear, not multi-dimensional. In the age of algorithms, artificial intelligence and big data, there is a genuine opportunity to reduce the size of these gaps to create a more quality, meaningful and focused conversation between advisers and their clients.

Unsurprisingly, financial planning is a complex business, but at its heart lies a simple mathematical equation: assets and income need to meet an investors changing life goals, life events and expenses over time. 

What is multi-dimensional modelling?

Multi-dimensional modelling is the practice of prioritising client's goals over a time horizon. Projecting a series of goal achievability's, funding sources (assets, lump sum cash amounts and cash flows), to be available at specific dates, at specific times, in the future. In particular looking at liquidity, risk capacity, risk tolerance and future value. Example goals include; funding for retirement, setting aside funds for children's education or a planned home renovation. 

The intelligent design and power of multi-dimensional modelling, is it's aptness to produce thousands of simulated scenarios, economic stress tests, permutations and combinations of strategies, evolving the industry into more interactive complex problem solving – all within a live client-facing experience.

Multi-dimensional modelling behind the planning customer experience, visually demonstrates to a client, in real-time, how advice bridges the gap from their current position, to better outcomes in their future – aligned directly to the achievement of their life goals. This interactive “what if” and "trade off" analysis can deliver an incredibly engaging financial advice experience.

Machine Learning in planning will continue to enhance optimisation capability, by learning over time the combinations of strategies that self-determine an optimal scenario, one that puts the client in a better position in relation to their goal achievability.

It sounds simple, but in reality it is incredibly complex.

We will see more robust financial modelling flow into financial planning technology, to further add to the confidence and trust in the analysis that forms the basis for advice in this industry.


Chapter 04 - Goals-based investing


Goals-based investing

In the traditional approach to asset allocation, advisers would combine their client's goals into a single-bucket and match this to an asset allocation based on a single risk tolerance score (a questionnaire that determines their attitude towards risk, excluding risk capacity). By definition, this is more backward looking, with the outcome being an expected return or expected performance benchmark over a certain time-period. 

While the foundation of the industry is built on this traditional theory, many modern behavioural & financial economists see flaws in it – as it assumes that people act rationally, and makes assumptions around their predisposed knowledge of financial markets. This theory is inaccurate and we must take into consideration many other factors.

What is goals-based investing?

Goals-based investing is different. Rather, it takes a single or multi-bucket approach to investing client’s funds for specific purposes – their goals. By playing with key levers including; goal priority, time horizon, funding source, risk tolerance and strategies – goals based investing helps maximise a clients chances of success. By using this flexible portfolio design methodology, clients can use single or multiple; investments, assets, cash flows and debt, to fund individual or multiple goals.

The primary focus of goals-based investing is to maximise a client’s chances of achieving their specific goals, whatever they happen to be, not outperforming markets by a percentage or benchmark. Advisers can more accurately monitor a client’s progress back to their goal achievement, and asset allocation becomes a more flexible and tailored approach to individual or buckets of goals.

Goals-based investing also considers both 'risk tolerance' and 'risk capacity' together. Risk tolerance determines the clients' behavioural willingness (upside/downside), that they are prepared to accept in order to achieve an individual or bucket of goals. Whereas, risk capacity is the client's 'financial capital' (net wealth, time horizon and goals) and their 'human capital' (income, income volatility, years to retirement) – the risk they can actually afford to take to achieve their goals.

Calculating a client's risk capacity and tolerance are foundation to a goals-based investing framework, we go deeper into the academic understanding of these two main elements of risk.

Risk Capacity

Some authors consider risk capacity to be the most important component of a client’s tolerance for financial risk (Davies, 2017). This is despite the fact that risk capacity is an objective calculation determined by a client’s actual financial situation, rather than their ability to ‘sleep at night’ with their investment portfolio risk-return trade-off.

Risk capacity is the amount of risk that a client can take in their investments, depending on their situation. The critical components of risk capacity calculations are: liquidity, time horizon, net wealth, debt, net wealth calculation, and potential considerations for the risk capacity calculation.

Risk capacity is broadly comprised of human capital (our ability to generate wealth eg: our income or level of education), and financial capital (our current net wealth). While our human capital generally decreases as we age (as we have fewer earning years ahead of us), financial capital generally increases as we age.

Financial Capital

Net Wealth

A critical component of risk capacity is net wealth, which (when combined with liquidity etc) provides an indication of financial capital. While our current Western society is primarily concerned with financial wealth as an indicator of social status, thus net wealth is a critical indicator of risk capacity. This is because of the impacts on economic status and access to choices (Henretta & Campbell, 1978).

While the primary contributor to net wealth is traditionally the family home (Costa-Font, Frank, & Swartz, 2018; Pfeiffer, 2017), this is generally not converted to income in retirement (Poterba, Venti, & Wise, 1994), and thus should be excluded from new wealth calculations for the purposes of portfolio allocation.

Home ownership is on the decline, as a result of changing family socio-demographics, particularly those who are married (Drew, 2015; Mundra & Uwaifo Oyelere, 2018), as well as declines in taxation incentives (Rosen, Bank, Eckstein, Stern, & Tcheau, 2017), gender, race and education challenges (Bourassa & Shi, 2017), and access to credit (Bourassa & Shi, 2017; Spader, McCue, & Herbert, 2016).

Property investment, even for a family home, is a very risky asset class according to the academic literature (Costa-Font et al., 2018; Teye, Haan, Elsinga, Bondinuba, & Gbadegesin, 2017; Wei, Zhang, & Liu, 2017).

Net wealth needs to be considered alongside actual historical behaviour to fully account for risk capacity (Sarlija, Bensic, & Zekic-Susac, 2009).

The net wealth calculation needs to measure investible assets (versus Jetski’s and plasma screen TV’s) as a proportion of net wealth (Davies, 2017). This is because it ensures that in a financial shock, lifestyle is not jeopardised (Davies, 2017).


When a client holds more liquid assets, this contributes positively to risk capacity because they are able to utilise their assets if needed (Davies, 2017), for either risk management or taking advantage of opportunities (Green, Melzer, Parker, & Rojas, 2016). Both a qualitative (eg. the holiday home is a family heirloom) and quantitative (eg. cash is more liquid than property) aspects of liquidity need to be considered, although it’s calculation may pose some challenges.

Time Horizon

Investment time horizon is unique for each client, and each goal the client holds. Hence, goal specific time-horizon is also unique for each client. It is intuitive that the investment time horizon for each goal would impact on the clients capacity to take financial risk (Roszkowski & Grable, 2005).

Historically, researchers considered which life phase a client was in to determine time horizon (Cordell, 2001), and indeed the default superannuation funds generally still follow this approach. We now follow a much more sophisticated approach which considers the unique time horizon for each goal.

The literature on investing according to the life-cycle is very thorough (Basu & Drew, 2007, 2009; Chai, 2009; Li & Yao, 2007; Shefrin & Thaler, 1988; Viceira, 2007).

The time horizon for each goal, combined with the importance of each goal to the client, will impact the risk capacity calculation. Generally, a longer goal time-horizon will result in a higher risk capacity for that goal.

Human Capital

Human capital is our ability to generate wealth through income. In societies with high education standards and consistent wages across occupations (or marginal taxation structures to generate this effect), most wealth is stored as human capital rather than financial capital (Hanna et al., 2011).

In general, calculations of human capital include income volatility, education, occupational level (eg. manager), and years to retirement (more years to retirement, means higher human capital).


In the West there is a global trend of increasing over-indebtedness (Amadi, 2012; Gathergood, 2012; Nottage, 2013; Ottaviani & Vandone, 2011). Debt is risky because the result of financial stress, such as job loss, can have significant drops in consumer consumption (Baker, 2018). The financial implications of debt are that it can result in higher exposure to external negative contexts such as increasing interest rates or falling property prices (Kim, 2016).

Risk Capacity Calculation

Risk capacity is a scale (Cordell, 2002), which takes into account the equal contributors of financial capital and human capital (Hanna et al., 2011). Desired financial returns and psychological risk tolerance components should not be included in risk capacity calculations, but rather be stand-alone considerations (Davies, 2017).

Insurance needs are included in this calculation, as it is the wealth protection component of the clients financial situation (Cordell, 2001). As does the relative size of each goal as a proportion of existing wealth (Davies, 2017).

Risk Tolerance

Individuals have preferences regarding tolerance for risk, and this is generally consistent across different contexts, including investments. Historically, personal preferences for risk (or risk tolerance) has driven most portfolio allocation within financial advice. While considerations of risk capacity may be equally important, it still is valuable to ascertain and apply individual preferences for risk tolerance to investment portfolios, as it is the indicator of ensuring the client is able to ‘sleep at night’ with their investments.


Historically, demographics alone were used to determine tolerance for risk, and thus asset allocation. However, older people, and women, are not always less risk tolerant than others (Kannadhasan, 2015). These types of generalisations can result in clients becoming disempowered from the advice process.

Households that are younger male-led, more educated, who hold shares, with higher net wealth, are more likely to take financial risks (Huang, Xu, & Chiang, 2016). However, this is likely more a result of human capital and financial capital (covered in the risk capacity section), than actual demographic trends.

It is these structural inequalities which often compound the impact of individual preference for risk on decreasing net wealth relative to others.

Risk Tolerance Components

The measurement of risk tolerance has existed in the psychological literature for decades, and covered a range of personal contexts.

While economists have often bundled risk tolerance with time preferences (Hanna et al., 2011; Claudia R. Sahm, 2012), psychologists see tolerance for risk as a personal and individual preference which may be impacted by context.

Components of risk tolerance calculations include the ranking of goals, emergency savings, emotions elicited during decision making, preferred risk-return trade-off, betting odds, and required rate of return (Cordell, 2002). The challenge is that people are not rational, and so each of the above components is different for everyone. The irrationality of investors has been studied extensively by authors who have found that we place excessive weight on small losses and highly value avoiding uncertainty (Tversky & Kahneman, 1992). We can also get addicted to risk taking behaviour, resulting in a positive feedback loop (Buckley, 2015). Our tolerance for risk also changes with our perception of economic conditions (Claudia R Sahm, 2012).

Risk Tolerance Questionnaires

Investment risk tolerance questionnaires apply psychometric survey design principles to reliably and validly predict individual risk tolerance preferences (Hanna et al., 2011). Authors have found that the responses to the surveys correlate strongly with investors perceptions of their own risk tolerance (Hallahan, Faff, & McKenzie, 2003). This means it is likely there is methodological error introduced into historical methods to ascertain risk tolerance, as it is not actual risk tolerance that is measured, rather, beliefs about one’s own risk tolerance. The most accurate measure of assessing risk tolerance is of course to assess actual behaviour.

Historical Actions

What we actually do is a more accurate indicator of our beliefs than what we say we believe. This is also the case in assessing risk tolerance. It is possible to assess the responses to the risk tolerance survey with actual historical behaviours, and authors have found consistency in this analysis (Roszkowski & Grable, 2005; Yook & Everett, 2003).

Tolerance for Risk Studies

Individual preferences for risk has previously been studied in the Psychology literature as ‘sensation seeking’, which relates explicitly to risk taking behaviour (Arnett, 1994; Zuckerman, Kolin, Price, & Zoob, 1964; Zuckerman & Neeb, 1980). Since this ground-breaking research was done there have been repeated tests and re-tests of tolerance for risk using empirical methodology.

There are a number of explicit behaviours which are associated with a high tolerance for risk such as smoking, driving fast (Zuckerman & Neeb, 1980), investing in stocks (Keller & Siegrist, 2006) and adventure tourism (Gilchrist, Povey, Dickinson, & Povey, 1995). However, authors have found that it is the responses to actual lotteries (with the client’s own money) that elicit the best predictors of actual market responses (Pennings & Smidts, 2000).

Below is an example of using 'risk capacity' and 'risk tolerance' within a goals-based investing framework.



Dr Katherine Hunt (BPsychSc, B Comm (Financial Planning), Honours in Finance (University Medal). PhD)

Dr Katherine has been working with a.i. to develop a digital Risk Profile Questionnaire within the a.i. software platform, consisting of extensive academic research that enables advisers to have a robust foundation in determining risk for their clients, aligning their clients to the right asset allocation in line with their goals and life aspirations.

Dr Katherine has a Bachelor of Psychological Science, Bachelor of Commerce (Financial Planning), and Bachelor of Finance (First Class Honours, University Medal).

You can also read one of the most extensive research reports carried out in Australia on Investment Risk Profiling: Lessons from Psychology by Dr Katherine, published in the Financial Planning Research Journal

Research references as part of Dr Katherine's work:

Amadi, C. W. (2012). An Examination of the Adverse Effects of Consumer Loans. International Journal of Business and Management, 7(3), 22-31.  

Arnett, J. (1994). Sensation Seeking: A New Conceptualization and a New Scale. Personality and individual differences, 16(2), 289-296.  

Baker, S. R. (2018). Debt and the response to household income shocks: Validation and application of linked financial account data. Journal of Political Economy, 126(4), 1504-1557.  

Basu, A., & Drew, M. (2007). Portfolio size and lifecycle asset allocation in pension funds. Paper presented at the The 15th Annual Conference on Pacific Basin Finance, Economics, Accounting and Management, Ho Chi Minh City, Vietnam.  

Basu, A., & Drew, M. (2009). The case for gender‐sensitive superannuation plan design. Australian Economic Review, 42(2), 177-189.  

Bourassa, S. C., & Shi, S. (2017). Understanding New Zealand’s decline in homeownership. Housing Studies, 32(5), 693-710.  

Buckley, R. C. (2015). Adventure Thrills are Addictive. Frontiers in Psychology, 6(1915).  

Chai, J., Horneff, W., Maurer, R., Mitchell, O.S. (2009). Extending Life Cycle Models of Optimal Portfolio Choice Integrating Felxible Work, Endogenous Retirement, and Investment Decisions with lifetime Payouts. NBER Working Paper Series, No. 15079.  

Cordell, D. M. (2001). RiskPACK: How to evaluate risk tolerance. Journal of Financial Planning, 14(6), 36.  

Cordell, D. M. (2002). Risk tolerance in two dimensions. Journal of Financial Planning, 15(5), 30.  

Costa-Font, J., Frank, R. G., & Swartz, K. (2018). Access to long term care after a wealth shock: Evidence from the housing bubble and burst. The Journal of the Economics of Ageing.  

Davies, G. (2017). New Vistas in Risk Profiling. CFA Research Foundation Briefs, 1-32.  

Drew, R. B. (2015). Effect of changing demographics on young adult homeownership rates. Joint Center for Housing Studies Harvard University.  

Gathergood, J. (2012). Self-control, financial literacy and consumer over-indebtedness. Journal of Economic Psychology, 33(3), 590-602.  

Gilchrist, H., Povey, R., Dickinson, A., & Povey, R. (1995). The sensation-seeking scale: Its use in a study of the characteristics of people choosing ‘Adventure holidays’. Personality and individual differences, 19(4), 513-516.  

Grable, J., Lytton, R., & O'Neill, B. (2004). Projection bias and financial risk tolerance. The Journal of Behavioral Finance, 5(3), 142-147.

Grable, J., & Lytton, R. H. (1999). Financial risk tolerance revisited: the development of a risk assessment instrument☆. Financial services review, 8(3), 163-181.

Grable, J. E., & Lytton, R. H. (2003). The development of a risk assessment instrument: A follow-up study. Financial services review, 12(3), 257.

Green, D., Melzer, B. T., Parker, J. A., & Rojas, A. (2016). Accelerator or brake? cash for clunkers, household liquidity, and aggregate demand. Retrieved from Gilliam, J., Chatterjee, S., & Grable, J. (2010). Measuring the perception of financial risk tolerance: A tale of two measures. Journal of Financial Counseling and Planning, 21(2).

Hallahan, T., Faff, R., & McKenzie, M. (2003). An exploratory investigation of the relation between risk tolerance scores and demographic characteristics. Journal of Multinational Financial Management, 13(4-5), 483-502.  

Hanna, S. D., Waller, W., & Finke, M. S. (2011). The concept of risk tolerance in personal financial planning.  

Henretta, J. C., & Campbell, R. T. (1978). Net Worth as an Aspect of Status. American Journal of Sociology, 83(5), 1204-1223.  

Huang, J.-T., Xu, X., & Chiang, T.-F. (2016). Household Expectations for Future Economy and Risk-Taking Attitudes. Journal of Financial Counseling and Planning, 27(1), 109-121.  

Kannadhasan, M. (2015). Retail investors' financial risk tolerance and their risk-taking behaviour: The role of demographics as differentiating and classifying factors. IIMB Management Review, 27(3), 175-184.  

Keller, C., & Siegrist, M. (2006). Investing in stocks: The influence of financial risk attitude and values-related money and stock market attitudes. Journal of Economic Psychology, 27(2), 285-303.  

Kim, J. (2016). Why household debt held by Korean seniors is problematic: An international comparison. Economics Bulletin, 36(4), 2080-2093.  

Kuzniak, S., Rabbani, A., Heo, W., Ruiz-Menivar, J., & Grable, J. (2015). The Grable and Lytton risk tolerance scale: A 15-year retrospective. Financial services review, 24, 177-192.

Li, W., & Yao, R. (2007). The Life-Cycle Effects of House Price Changes. Journal of Money, Credit and Banking, 39(6), 1375-1409.  

Mundra, K., & Uwaifo Oyelere, R. (2018). Homeownership trends among the never married. Housing Studies, 1-26.  

Nottage, L. (2013). Innovating for ‘Safe Consumer Credit’: Drawing on Product Safety Regulation to Protect Consumers of Credit. In T. Wilson (Ed.), International Responses to Issues of Credit and Over-indebtedness in the Wake of Crisis. Surrey, England: Ashgate. 

Ottaviani, C., & Vandone, D. (2011). Impulsivity and household indebtedness: Evidence from real life. Journal of Economic Psychology, 32(5), 754-761.  

Pennings, J. M. E., & Smidts, A. (2000). Assessing the Construct Validity of Risk Attitude. Management Science, 46(10), 1337-1348.  

Pfeiffer, D. (2017). No Place Like Home: Wealth, Community & the Politics of Homeownership, by Brian J. McCabe. Journal of the American Planning Association, 83(2), 221-222.  

Poterba, J. M., Venti, S. F., & Wise, D. A. (1994). Targeted Retirement Saving and the Net Worth of Elderly Americans. The American Economic Review, 84(2), 180-185.  

Rosen, K. T., Bank, D., Eckstein, A., Stern, M., & Tcheau, M. (2017). Homeownership in Crisis: Where are We Now?  

Roszkowski, M. J., & Grable, J. E. (2005). Estimating risk tolerance: The degree of accuracy and the paramorphic representations of the estimate. Journal of Financial Counseling and Planning, 16(2).  

Sahm, C. R. (2012). How Much Does Risk Tolerance Change? Quarterly Journal of Finance, 02(04), 1250020.  

Sahm, C. R. (2012). How much does risk tolerance change? The quarterly journal of finance, 2(04), 1250020.  

Sarlija, N., Bensic, M., & Zekic-Susac, M. (2009). Comparison procedure of predicting the time to default in behavioural scoring. Expert Systems with Applications, 36(5), 8778-8788.  

Shefrin, H. M., & Thaler, R. H. (1988). The behavioral life-cycle hypothesis. Quasi Rational Economics, 91-126.  

Spader, J., McCue, D., & Herbert, C. (2016). Homeowner Households and the U.S. Homeownership Rate: Tenure Projections for 2015-2035. The Harvard Joint Center for Housing Studies, December 2016.  

Teye, A. L., Haan, J. d., Elsinga, M. G., Bondinuba, F. K., & Gbadegesin, J. T. (2017). Risks in homeownership: a perspective on The Netherlands. International Journal of Housing Markets and Analysis, 10(4), 472-488.  

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.  

Viceira, L. M. (2007). Life-cycle funds. Cambridge, MA, USA: Harvard University. 

Wei, S.-J., Zhang, X., & Liu, Y. (2017). Home ownership as status competition: Some theory and evidence. Journal of Development Economics, 127, 169-186.  

Yook, K. C., & Everett, R. (2003). Assessing risk tolerance: Questioning the questionnaire method. Journal of Financial Planning, 16(8), 48.  

Zuckerman, M., Kolin, E. A., Price, L., & Zoob, I. (1964). Development of a sensation-seeking scale. Journal of Consulting Psychology, 28(6), 477-482.  

Zuckerman, M., & Neeb, M. (1980). Demographic influences in sensation seeking and expressions of sensation seeking in religion, smoking and driving habits. Personality and individual differences, 1(3), 197-206.  

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