The Role of Predictive Analytics in the Strategy of an Insurance Company


Predictive analytics is slowly making its way into the insurance sector but progress is different in every country.  Nevertheless, there is a global trend across the industry to acquire data analytics capability.

Insurers with an analytics team are already enjoying the benefits of superior underwriting, pricing, and fraud detection with only a few companies currently using analytics in more than one of these areas.

This is expected to change and extended use of analytics is on the horizon[1].  However, once all insurers have a data analytics capability, something extra will be required to keep a competitive advantage.  The way an insurer uses its analytical resources will determine whether the company has a lasting advantage.  Data analysts are in high demand[2] so insurance companies need to ensure their analysts focus on the most important tasks for the business.

In addition, an analytics team will perform better when there are specific objectives and a clear sense of purpose.

Data analytics – enabling the company’s strategy?

Instead of adopting a silo approach, an insurer should use data analysts to support its business plan.  Analytics should help the business achieve its strategic goals to:  increase market size, achieve a better product per customer ratio, reduce fraud, etc.

The company’s strategy contains the business’ most important goals, so the data analytics department should focus on achieving those goals.  This can only happen when an insurer fully embeds analytics in the organisation rather than leaving it to act as an isolated team with little contact with the rest of the company.

XL and AIG have already seen this as key to success and they have taken steps accordingly[3].  Deloitte’s John Lucker considers that an appropriate analytics strategy is the first component to successful execution.  Christian Moe, Senior Analytical Consultant at SAS, also sees the alignment of analytics and business strategy as the first step to succeed in the implementation of data analytics[4].

But not all insurers are there yet:  a Deloitte’s 2015 survey reported that fewer than 50% of US Health companies (which includes Health insurers) had a clear analytics strategy[5].  US healthcare is often quoted as undergoing a data analytics revolution[6], putting the strategic gap in perspective.

There is a clear opportunity in the US Health Insurance market for companies that can formulate a good analytics strategy.  This applies to other insurers too.

Predictive analytics – driving the strategy?

Some argue that rather than supporting the company’s strategy, predictive analytics should dictate the strategy.  In today’s digital, fast paced world, the ability to identify new or changing trends and anticipate developments could be the key to success[7].

Oracle’s white paper ‘Driving Strategic Planning with Predictive Modelling‘ already identified this in 2008[8].  The paper expects a shift in the focus of planning sessions after adopting predictive modelling:  ‘…from debating arbitrary point estimates toward reaching consensus on the key underlying assumptions with the greatest impact on the results’.

Focusing the analytics team on helping the business achieve desired outcomes for those key assumptions is a great way to contribute to the success of the strategy and its execution.

Oracle considers that the change in planning focus is possible because predictive analytics enables a business to ‘identify and evaluate risk and uncertainty in strategic decisions’.  Could insurers benefit from using predictive analytics to drive their strategy?

To an extent, insurance companies are already doing this.  Claims projections, stress and scenario testing, asset and liability matching and other forms of predictive modelling are already embedded in insurance, with actuaries making excellent use of these techniques.

This is good news for insurers but there is a wider range of methods in predictive analytics that would be a useful addition to the traditional actuarial skill set.  There is also scope for application in non-actuarial parts of the business such as Sales or Claims Management.

Analytics, an independent team with a supporting role

A company’s strategy will focus on different areas, sometimes on several at the same time.  Because of this, it is important that the analytics team is not part of a team with another specific focus such as Actuarial or Marketing.  Analysts need to be able to work with different business functions, understand how these operate and move on with the business strategy.

To be fully effective, an analytics team must be independent, ideally reporting to the CEO.  This is to ensure that it best serves the company’s interests rather than those of a particular department.  A recent survey by Towers Watson found that conflicting priorities is the third most significant challenge US P&C insurers face in the US, following only the lack of available talent and difficulty in data capture[9].

An insurance company with an autonomous analytics team, staffed with data analysts and business experts, will always be in a better position to help the company achieve its desired business outcomes than one that depends on a specific business unit and only counts with data analysts among its members.

A dynamic approach to analytics, aligning it with the company’s strategy, is a long-term winning combination that will provide the necessary competitive edge when data analytics is widespread across the industry.


Image source: Omer Yousief / Pixabay











From Solvency 2 to Machine Learning

hand-982059_1920Machine learning and predictive analytics are driving significant changes in financial services.  However, this is not limited to banks and insurers; regulators all over the world are embracing data analytics as a powerful tool to process ever-increasing amounts of data and extract useful insight to prioritise their actions.

One example is the US Financial Industry Regulatory Authority (FINRA), which has recruited a data analytics team to improve their ability to oversee the vast amount of data collected and, as FINRA’s Chief Risk Officer and Head of Strategy puts it: “… see things they couldn’t have seen or understood as well before.[1]

The insurance industry lags behind banking in this respect.  Nevertheless, the recent adoption of Solvency 2 in the EU will quickly bring insurance up to speed given the regular data submissions insurers must make to their supervisors.  These regulators will have to change their approach to supervision in order to cope with the massive amount of information they are set to receive.  In addition to quantitative data in the form of Quantitative Reporting Templates (QRTs), there are also narrative reports such as the Stability and Financial Condition Report (SFCR) and the Regular Supervisory Report (RSR).

Regulators face the challenge of extracting insight from vast amounts of data

A 2014 Deloitte’s report[2] identifies the ability to extract analytical insights as one of the key challenges for banking supervisors.  The ability to design early warning mechanisms and predictive models that allow regulators to anticipate issues is of special importance.  This would allow regulators to prevent rather than react, playing a more proactive role in the prevention of crisis than before.

Given the similarities between Solvency 2 and Basel 3 (the regulatory regime for banking), the challenges banking supervisors face are also relevant for insurance regulators.

A combination of supervised and unsupervised machine learning techniques could help make sense of all the data insurance companies submit in their QRTs and other reports.  These techniques could include:

  • Pattern detection, to find relevant trends in the industry.
  • Predictive modelling, to identify insurers likely to get in trouble.
  • Anomaly or outlier detection, to see companies deviating from what is the norm in the market or detect potential cases of fraud or money laundering.
  • Clustering techniques, to group insurers in pools of similar entities and facilitate their comparison.
  • Correlation analysis with external factors such as interest rates, economic growth, etc. This is useful when building predictive models that try to anticipate how changes in the economic environment affect insurers.
  • Text mining techniques, to automatically explore narrative reports (SFCR and RSR) and prioritise supervisory work.

Investors can apply machine learning to explore insurers’ public regulatory returns

In addition to regulators, investors focused on the European insurance market may also benefit from using machine learning to make sense of the large amount of data that is becoming available.

Machine learning can help identify potential investments based on risk, solvency and other financial data included in the regulatory returns alongside the narrative reports European insurers must make public.  Investors could develop predictive models to estimate the future profitability and solvency of insurance companies in a range of scenarios.

For example, Blackrock is an investment firm betting on data analytics to gain a competitive edge[3] in its stock picking.  It is only a matter of time before investment firms take full advantage of the potential that machine learning and public Solvency 2 reporting represent for large-scale investment analysis.

Machine learning techniques can help investors and insurance supervisors process large amounts of data, generating actionable insight that could drive investment decisions or prioritise regulatory action.  This can be a game changer for regulators, who would be able to predict issues at regulated entities and have a data driven approach to prioritising their work, making optimum use of scarce resources.  Investors, on the other hand, could benefit from better knowledge of the sector and the specific exposure to different risks each insurer has, outsmarting rival investment firms.


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Data Analytics and Capital Management

coins-1015125_1920Solvency 2 came into effect on 1st January 2016.  This regulatory framework plays a very important role in how European insurance companies are managed, with risk management  at the core of Solvency 2, imposing capital requirements for the risks that affect insurers.  This article explains how insurance companies can use predictive analytics to improve risk management and reduce capital requirements under Solvency 2.

Data analytics for entity specific risk parameters

The calculation of capital requirements requires the use of risk parameters that calibrate the risk models.  Solvency 2 allows for a range of options such as a full internal model, a partial internal model or a Standard Formula initially calibrated by EIOPA but that accepts entity specific parameters known as USPs (Undertaking Specific Parameters)[1]

Companies using entity specific parameters, in full or partial internal models or by tailoring the Standard Formula with USPs, need to justify the use of these with their own data.  This is an area where predictive analytics can give a competitive advantage.  The more information you can extract from your data, the more appropriate your risk parameters will be.  Furthermore, a better understanding of risk could reduce its volatility, decreasing capital requirements, as deviations from expected values would be smaller or less probable[2].

For example, predictive modelling provides complementary reserving methods that may perform better than traditional actuarial techniques[3], helping to reduce Reserve risk.  Markus Gesmann, Manager of Analysis at Lloyds, highlights the advantages of a Bayesian approach to communicate uncertainty with credibility intervals[4].  Given the complexity of some solvency models, anything making the message easier to understand is a welcome addition.

The goal is not to replace traditional actuarial reserving practice but to consider additional methods that contribute with a different way to look at the risk.  Most winning solutions at the data analytics website Kaggle[5] are an ensemble of models.  Rarely a single method is good enough to cover all scenarios and a combination of models works better in the same way that a team usually performs better than an individual.  However, there must be a balance between accuracy and practicality.

Better understanding of risk may lead to lower capital requirements

In a more specific example, the Standard Formula’s calibration for Premium and Reserve risk in Medical Expenses assumes a standard deviation of 5% for Reserve Risk.  If predictive analytics helped reduce the standard deviation to 4%, the capital charge for Premium and Reserve Risk could decrease by 10%[6], as large deviations from the best estimate of technical provisions would be smaller and less probable.  The capital requirement reflects the severity of potential adverse developments in reserving. Thus the smaller the volatility, the smaller the capital requirement.

A similar change in the standard deviation for Premium risk could reduce the capital charge by 15% and the joint effect would be a 35% reduction in the capital requirement for Premium and Reserve risk.  EIOPA has rules defining what proportion of these savings insurers can acknowledge if using USPs[7].  Insurer’s relying on the Standard Formula may want to check the list of risks for which they can use USPs.

The list of Standard Formula parameters that can be replaced by USPs is short but represents the most significant risks for some businesses.  Entities using partial or full internal models may enjoy greater potential to leverage predictive analytics.  Risk Dynamics, recently acquired by McKinsey, already provides validation services to financial institutions whose risk models are based on predictive analytics[8].

Increasing an insurer’s capital efficiency is not usually in mind when discussing data analytics but it is a secondary benefit worth considering.

Predictive analytics can help insurers understand and manage their risks better, with a clear impact to their bottom line.  With some extra work, insurers can enjoy the benefits of lower risk uncertainty and increased capital efficiency.   This would enable the company to be more profitable, provide more affordable products and increase dividends to shareholders.


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[1] See EIOPA’s advice on USPs, in page 18:

[2] An application in Long Term Care:

[3] Bayesian Analysis in Forecasting Insurance Loss Payments:

[4] See Markus’ blog, “mage’s blog”


[6] Different undertakings could see different results depending on their specific figures. This is just an illustration.



Using Data Analytics to Increase Cross-selling

calculator-385506_1280One of the many advantages of a composite insurer is the company-wide customer base that can be targeted with a range of product offerings.  A customer who purchased a life insurance policy may also be interested in motor or home insurance.

However, it is often difficult for insurance  companies to leverage the potential of cross-selling.  As of 2015, the insurance industry averages 2 products per customer.  Leading insurance companies are prioritising cross-selling, with Aviva announcing a target of 3 products per customer (Aviva currently averages 1.7 per customer).[1]

In comparison, some banks have reached excellent levels of cross-selling.  Wells Fargo is a good example of a company that is successfully capitalising on this opportunity.  In 2012 they averaged 5.9[2] products per customer in retail banking when, to put it in perspective, three years before, USAA was at the top of the cross-selling league with 3.9 products per customer[3].

Wells Fargo has accomplished this by changing the company culture[4] but this is a big task for anyone who wants to replicate that success.  In contrast, some banks have embraced data analytics as a faster, cost-effective means to achieve the same result.

Spanish banks BBVA and Bankinter are great examples of the value that analytics can add to an organisation.

Bankinter has embedded data analytics in their customer relationship management[5].  Revenue increased by more than 18% from 2001 to 2004 with 14% more customers, increasing profit per customer.  The number of products per customer reached 6.1 from a previous level of 5.5.  In addition, customers reported increased loyalty and satisfaction.

Similarly, analytics enabled BBVA to provide 4% more credit to small and medium business with no increase in default rates.  BBVA’s data analytics centre generates actionable insight that is useful not just to the bank but also to retailers.  By sharing this data with retailers, the bank is expecting to indirectly generate more business for the bank[6].

Anyone who has bought something at Amazon has seen data analytics for cross-selling in action and will be familiar with “customers who bought this item also bought…”  That is Amazon’s way to present you with products their data suggests you may also buy.  In fact, Amazon reported in 2006 that 35% of their sales were due to cross-selling.[7]

An insurance company can adopt customer analytics and have similar success by leveraging its customer base and finding opportunities for cross-selling.

Predictive modelling allows an insurer to identify customers likely to switch to a competitor or those at particular stages in their life cycle who are likely to purchase an additional product.  It can also identify the characteristics of those policyholders holding more of the company’s products and detect customers with whom you may form a long-term and profitable relationship.

The more products you sell to individuals, the more profitable those customers are and the more likely they are to remain your customers in the long term.  Data analytics is a cost-effective tool to help increase sales from your existing customer base in a competitive environment where winning market share is increasingly difficult or can only be achieved at the expense of profitability.

If you found this interesting, you may also want to check this presentation Predictive Analytics for Customer Targeting – A Banking Telemarketing Example


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[1] See:

[2] See Forbes magazine:

[3] See American Banker:

[4] See Forbes magazine:



[7] See: