Consulting for enhanced data analytics and pricing processes
Advanced solutions for pricing sophistication, portfolio steering and smart pricing
In this fast-changing environment, we act as your strong partner. Our Global Consulting experts combine their experience in primary insurance with their knowledge in data science and advanced analytics in order to offer support from A to Z throughout sound and modern pricing techniques.
Global Consulting is a trusted advisor in primary insurance and will support you in improving your business. We engage with our clients through open dialogue and ongoing collaboration that won’t leave you alone with implementation. Acting as a risk-sharing long-term partner, we will help you along the entire value chain to drive performance and to develop business models for existing and emerging risks.
Digitalization plays a tremendous role in industry. In comparison to only a few years ago, a multitude of structured and unstructured data is available. This brings the need for enhancements in infrastructure and computational power to store and handle this amount of data as well as sophisticated and modern algorithms for their analysis.
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.
By supporting in modelling process and deriving a technical price, we help to 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 modelling (GLM) and other machine learning techniques, a multivariate approach considered the de facto standard in primary insurance’s technical excellence.
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 rate as well as 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, we have taken advantage of tree-based machine-learning algorithms (XG Boost and Random Forest) to improve the estimation of the 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.
Making use of advanced cost models and demand models we help first in identifying the position of the company in the expected projected profitability and retention space. 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 us, we can run the optimization at policy level (hard optimization) thus identifying the optimum premium per each customer which maximizes the target function (volume or profit) under some constraints.
Portfolio analysis & portfolio steering
Measurements to improve the pricing process are important. However, their impact on the daily business needs to be ensured and reviewed constantly. A sound monitoring system helps make sure that initiatives in the pricing process have the impact desired. Equally important, such a system ensures continuous control over all important KPIs, reflecting the health of the portfolio.
We help in establishing monitoring systems on several levels. We are able to provide primary insurance companies with a fully-fledged monitoring dashboard customized to their needs. Equally, our consultants can assist to enhance existing monitoring systems and conceptualize add-ons accordingly.
Smart pricing solutions
The digitalization process has caused a deluge of structured and unstructured data, which can and should be used to enhance the underwriting and pricing process.
Working closely with our clients we are continuously improving the set of tools, procedures and techniques to stay ahead of the curve. Additionally, we liaise with MR’s unique data-hunting units, which allow us to retain our competitive advantage to other players, and enable solutions that would otherwise remain untapped.
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.
One of the tasks of technical departments is often that of offering novel approaches to marketing campaigns or, more generally, to identify unique selling points for clusters of customers. Cross and up-sell opportunities fall under the same broad umbrella term of “segmentation” The goal of these analyses is to find the next best product to propose to the final customer.
Another, more traditional, approach is that of moving from the known recency-frequency-monetary (RFM) segmentation of the customer base, to a multidimensional one that uses a bottom-up approach which finds structures (or clusters) in the wealth of unlabeled data. This approach leverages on unsupervised machine learning techniques.
Finally, if previous information on the behavior of customers confronted with offers or campaigns is available (in other words, if labelled data are available), a top-down approach is also possible. This supervised approach can identify the drivers of positive decisions, as well as calculate the probability of positive outcomes for each policyholder for similar, future campaigns.