Pricing excellence solutions
Where prediction meets the business
In P&C 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.
The next generation of pricing actuaries
How automated machine learning is changing the role of the pricing actuary
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Our insurance 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 pricing and underwriting to suit their strategic needs. The Munich RE pricing excellence framework will allow our clients to grow profitably, enhancing their pricing and analytics capabilities with a strong focus on the implementation. From model development, analyzing the impact of technical and commercial decisions, all the way to testing different 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 new products.
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 the 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 of 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, behavioral 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 behavioral models.
- Optimal market positioning can be the key to success for MGAs.
- Modeling the conversion probability i.e. the elasticity of a customer at the point of sale, we help you optimize your competitive positioning on aggregators and price comparison websites.
- Thanks to the Pricing Sophistication Assessment Tool we benchmark your pricing processes and methodology against your peers and the market.
- Based on the outcome, we identify the areas of improvement and we help clients improving the pricing governance and pricing flow.
- We set up pricing and underwriting committees to help insurtech and digital players in defining the best pricing strategy and portfolio steering.
- Thanks to the Munich RE platforms we provide our clients with the most advanced cloud-based monitoring solution to follow the growth of the portfolio in real time and act quickly.
- We help insurers to build their pricing team, identifying the right profiles based on the client needs.