Technical excellence for pricing sophistication, portfolio steering and smart pricing
In insurance, the challenge is to be strong in pricing sophistication in order to stay competitive. Proven and established actuarial models need to be combined with modern machine learning models to exploit the power of big data. The digitalization process has caused a deluge of structured and unstructured data, which can and should be used to enhance the pricing and underwriting process. Working closely with our clients we are continuously improving the set of tools, procedures and techniques to stay ahead of the curve.
Insurance pricing for personal lines
Our personal lines practice is founded on the principle that effective risk management needs to leverage on the right mix of experience, creativity and resources. Our team of consultants with direct experience in primary insurance help our clients throughout the long journey of rate calibration to suit their strategic needs. From model development, analyzing the impact of technical and commercial decisions, all the way to testing different ratemaking scenarios, our consultants will be there, whether you need support making better sense of the data you have, or if you want to co-develop an advanced tariff.
Our offer includes, but is not limited to:
- Key factor and starting point for all actuarial modelling is a deep understanding of the company’s portfolio.
- The descriptive analysis of a company’s historic data and a constant monitoring of actual numbers and KPI forms the backbone to stay competitive in the market.
- We help to identify, structure and clean the data. We combine information from single policies with the clients’ claims data base. We visualize insurance-specific KPIs in a univariate view, and we show trends and differences between the modalities of the provided rating factors or variables.
- A sound monitoring system makes sure that initiatives in the pricing process have the desired impact. Equally important, such a system ensures continuous control over all important KPIs, reflecting the health of the portfolio.
- We provide primary insurance companies with fully-fledged monitoring dashboards customized to their needs. Equally, our consultants assist to enhance existing monitoring systems and conceptualize add-ons accordingly.
- By supporting in modelling process and deriving a technical price, we help create a strong competitive advantage by pricing risks adequately. In turn, this reduces anti-selection in the portfolio and increases profitability.
- Based on historic portfolio data, risk models enable a proper prediction of the expected claims cost by considering the influence of relevant rating factors. These can be identified using generalized linear/additive modelling (GLM/GAM), the de facto standard in primary insurance’s technical excellence.
- In appropriately identified use cases, we implement a machine learning approach leveraging on top-notch expertise and infrastructure.
- Deployed into business, this forms the basis for strategic ratemaking decisions: it enables the identification of mis-priced customer segments, as well as yielding an improved risk selection through deeper understanding of the historical risk.
- Retention and conversion rate models are gaining more and more importance in the insurance sector. On a technical level, demand models predict a policyholder’s estimated probability to renew or to write a policy.
- Deployed into business, the main area of application is to explore and measure the impact of price changes. This ultimately leads to effective scenario testing and potentially to price optimization approaches.
- Furthermore, the model results can be used as a basis for strategic marketing decisions and customer lifetime value propositions.
- Although generalized linear models still represent a well consolidated technique in most markets, we have taken advantage of tree-based machine-learning algorithms (XG Boost and Random Forest) to improve the estimation of customer elasticity.
- The predictive risk cost models are a basic and fundamental part in the ratemaking process, but they cannot be considered exhaustive. The behavior of the customers at the point of quotation or renewal should be taken into account for a profitable growth.
- All profitability studies need to strike the right balance among revenue, costs and likelihood of acquiring / keeping a customer.
- Based on the client’s strategy, we then propose a set of pricing scenarios aimed at meeting the company’s target, simulating the renewal process and optimizing the cap and floor structure (soft optimization).
- Should the market allow, we can run the optimization at policy level (hard optimization) thus identifying the optimum premium for every customer.
- Clients can leverage on a tailored geographical categorization of the risk carried in their portfolios.
- Geo-spatial smoothing is based on the assumption that after adjusting for all other risk factors, the risk of policies in adjacent regions is more similar than the risk of distanced policies. Therefore, insured risks that are physically located close to each other should get similar technical rates, provided all other risk factors are held constant.
- This technique can be used to detect spatial patterns and model deficiencies. When applied in pricing. It can additionally be used to improve geographical risk segmentation.
- The vehicle-related data is among the most important risk drivers in the motor business.
- The vehicle is usually described by many variables e.g. vehicle age, fuel type, cubic capacity, ADAS features, etc., but ideally just as a stilled rating factor could carry most of the relevant information.
- We help reduce the complexity of the database, identify the independent vehicle-related variables and find the most significant vehicle groups to increase the model predictiveness.
- Many clients need a starting point for their first tariff generation, but struggle to orientate themselves in markets where such information is difficult to obtain.
- In advanced markets, established players which are active in the aggregators’ channel, find immediate uplift when they can assess their standing with respect to that of their peer group.
- Relative ranking is one of the fundamental drivers of customer behavior when purchasing a policy and therefore is a powerful ingredient of demand models.
- Optimal market positioning can be the key to success for MGAs.
Pricing for Commercial & Industrial Lines
Commercial lines generally have very different and unique challenges when compared to personal lines. Typically, data availability and quality are often scarce and limited. The diversity and the complexity of the type of risks written can bring about significant challenges in interpreting trends and projecting future outcomes. In addition, this segment is often subject to intense market cycles and competition, resulting in rate adequacy and top-line issues for many companies. These challenges would require different actuarial approaches, techniques and judgements supplemented by underwriting and claims expertise within Munich Re and with the client.
Our offer includes but not limited to:
- Subject to clients’ data availability, we help break down the clients’ historical performance into 3 distinct loss components; attritional, large and natural perils.
- Attritional and large losses are man-made losses (e.g. fire, water damage, theft, malicious damage), distinguished by a specified loss threshold. Natural perils are weather-related losses (e.g. Storm).
- We help display emerging claims trends by key segments via a suite of monitoring dashboards. These trends will be monitored against a set of target KPIs to raise clients’ awareness and attention to the emerging profitability trends.
- We adopt and utilize actuarial pricing techniques that are fit and appropriate for the clients’ profile. We complement our analysis with additional insights from our internal teams and close collaboration and engagement with the clients’ underwriting and claims personnel to understand market-specific trends and practices.
- Large losses require careful attention in pricing. Typically, large losses are infrequent high severity losses; therefore, the occurrence rate and key drivers of such losses (e.g. Occupancy type, hazard risk scores) need to be well understood and determined before targeted large loss loadings can be factored into technical pricing.
- Natural peril allowance needs to be carefully determined based on the underlying hazard exposure (as opposed to historical events). This often requires additional input from catastrophe modelling software or the cost of catastrophe reinsurance arrangements of the client.
- Some clients opt for certain reinsurance arrangements for their portfolios e.g. Quota Share and Surplus for capacity support. We help the client analyze the optimal reinsurance structure that is expected to deliver the best outcome, both profitability and volatility reduction.
- Profit and Loss statement reconstruction is a key deliverable for this exercise to help client understand the potential impact of various reinsurance options.
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