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Underwriting Cycles[1]

In this case study for Complexity Science, we aim to show its value addition through agent based modeling of an important insurance problem, i. e, underwriting cycle. Underwriting in Property-Casualty/General Insurance is where insurance underwriting follows and mimics the Economy and its cycles of boom and busts. These soft and hard market swings are a key source of challenge and volatility to insurers. The main value addition of agent based modeling is that behavioral finance insights, expert opinion and observations from years of experience can be simulated in a serious game environment to assess their viability.

While Property-Casualty insurance is quite competitive, it has its share of collective irrationalities and unintended consequences of actions. The winner’s curse is where insurer continuously quote low premiums in a bid to gain greater market share. They then suffer heavy losses when claims start erupting and the premiums and hence reserves are inadequate to cover them.

Usually there are three tiers of insurers in the market. The first tier is the top 3 or top 5 insurers that dominate market share are market movers and have huge underwriting scales. First tier looks to its competition mainly within its own tier like an oligopoly.

Second tier is the middle level insurers. They have stable market standings and are dynamic. They do not have the advantage of scale like first tier but they are more stable than the third tier. They focus on growth to reach the first tier and avoid regressing through losses to the third tier.

Third tier is the newly established companies, companies with low capital and market share. They usually focus on niche marketing or specialized lines but new companies also focus on gaining larger market share. They are price takers in that they use the premiums made by first tier market movers as base standard.

While insurers regard overall market movements and trends, most of comparison and strategic vigilance in a game theory type of environment is within the tiers of their market standing respectively.

Such clustered behavior can lead to herd behavior. An instance is the case of pricing wars. Excessive competition over prices to secure greater market share leads to eventual worsening of underwriting performance. Executives eventually decry this trend in publications like surveys and trade magazines as well as in conferences. This signals to the market that higher prices need to be insisted upon now. It may take only a few market makers to raise prices as they are well established and source of comparison and transparency for all of the industry. Following this, every other insurer follows suit in raising premiums.

Another sociological reason for underwriting cycles is identified by Fitzpatrick (2004) as due to the “ebb and flow of bureaucratic influence" among different departments within an insurance company. Different departments have different roles, functions and objectives.  Insurance companies are ultimately composed of individuals and their judgment affects pricing decisions and this itself drives the cycles on insurance markets. Greed is promoted in times of greed and fear is adhered to in times of fear. Underwriters have the upper hand in profitable times and they push prices down to maximize sales and revenue. The more risk cautious claims adjusters and actuaries are more powerful at times when the insurer is in financial distress; they lobby to raise prices to restore profitability.

Agent based modeling undertaken by Owadally et al shows that this indeed is the case. Different interests of different key agents in a very simple insurance setting can lead to the complex notions of underwriting cycles.

Developing further on this line of inquiry, Ingram and Underwood (2010) and Ingram et al. (2012) map these attitudes to insurance functions. Underwriters are `individualists': they are risk-taking profit maximizers. Claims adjusters are `egalitarians': they are risk-averse conservators. Actuaries are `authoritarians': they are prudent risk managers. Other operations managers (for example in IT departments) are `fatalists': they are pragmatists. According to Ingram and Underwood (2010), when profits are high, profit-maximizers (underwriters) are significant and they diminish underwriting standards to compete for customers and maximize sales (and their own remuneration). When profits fall and the firm is under pressure, conservators (claims adjusters) take over and require stricter underwriting standards. Eventually risk managers (actuaries) gain more prominence as they aid the company restore its finances. At various points, the pragmatists help out, making alliances with all parties to enable the company to keep operating. Ultimately, profitability is restored, memory vanishes and the profit maximizers take over again which repeats the cycle again.

Prospect theory is an important tenant of behavioral finance for describing our behavior. Bromiley (1991) and Fiegenbaum and Thomas (1988) describe an extension of prospect theory to the firm. They argued that a firm’s aspirations serve as target or reference levels. Firms expecting returns below the relevant reference level will be risk seeking while those above will be risk averse. Palmer and Wiseman (1999) also highlight that when decision makers are faced with the prospect of failing to meet their objectives, they accept higher risk options that offer an opportunity to attain the objective and avoid the loss. In contrast, when decision makers think they will achieve their goals they will take the safer options that avoid jeopardizing the attainment of the goals. This can be seen by insurers focusing more on alternative investments in times of low interest to gain higher returns; insurers diversifying in other lines of business not previously considered when pricing wars in main lines reach unsustainable levels; insurers relaxing underwriting standards to achieve greater revenue; insurers investing more in technology and data science that disrupts traditional markets in order to create a newer market and so on.[2]


[1] Owadally et al CASS business school (Aug, 2015). The insurance industry as a complex social system: competition, cycles, and crises.

[2] N. Allan et al (2011); IFoA: A review of the use of complex systems applied to risk appetite and emerging risks in ERM practice

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Comment by Sione Palu on June 27, 2016 at 1:46am

Agent based is a popular topic in statistical physics & complex systems. The following pre-print is recent over the last couple of years.

"Statistical Mechanics of Competitive Resource Allocation using Agent-based Models"

Comment by Sione Palu on June 27, 2016 at 12:51am

"Minority Games - What happens when physicists start doing economics"

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