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The process of retirement planning aims to:
- Assess readiness-to-retire given a desired retirement age and lifestyle, i.e., whether one has enough money to retire
- Identify actions to improve readiness-to-retire
- Acquire financial planning knowledge
- Encourage saving practices
Obtaining a financial plan
Producers such as a financial planner or financial adviser can help clients develop retirement plans, where compensation is either fee-based or commissioned contingent on product sale. Such an arrangement is sometimes viewed[by whom?] as in conflict with a consumer's interest, and that the advice rendered cannot be without bias, or at a cost that justifies its value. Consumers can now elect a do it yourself (DIY) approach. For example, retirement web-tools in the form of a calculator, mathematical model or decision support system are available online. A web-based tool that allows client to fully plan, without human intervention, might be considered a producer. Key motivations of the DIY trend are many of the same arguments for lean manufacturing, a constructive alteration of the relationship between producer and consumer.
Modeling and limitations
Retirement finances touch upon distinct subject areas or financial domains of client importance, including: investments (i.e., stocks, bonds, mutual funds); real estate; debt; taxes; cash flow (income and expense) analysis; insurance; defined benefits (e.g., social security, traditional pensions). From an analytic perspective, each domain can be formally characterized and modeled using a different class representation, as defined by a domain's unique set of attributes and behaviors. Domain models require definition only at a level of abstraction necessary for decision analysis. Since planning is about the future, domains need to extend beyond current state description and address uncertainty, volatility, change dynamics (i.e., constancy or determinism is not assumed). Together, these factors raise significant challenges to any current producer claim of model predictability or certainty.
Monte Carlo method
The Monte Carlo method is a the most common form of a mathematical model that is applied to predict long-term investment behavior for a client's retirement planning. Its use helps to identify adequacy of client's investment to attain retirement readiness and to clarify strategic choices and actions. Yet, the investment domain is only a financial domain and therefore is incomplete. Depending on client context, the investment domain may have very little importance in relation to a client's other domains—e.g., a client who is predisposed to the use of real estate as primary source of retirement funding.
Contemporary retirement planning models have yet to be validated in the sense that the models purport to project a future that has yet to manifest itself. The criticism with contemporary models are some of the same levied against Neoclassical economics. The critic[who?] argues that contemporary models may only have proven validity retrospectively, whereas it is the indeterminate future that needs solution. A more moderate school believes that retirement planning methods must further evolve by adopting a more robust and integrated set of tools from the field of complexity science. Recent research has explored the effects of the elimination of capital income taxes on saving-for-retirement opportunities and its impact on government debt.