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.


Image source: Pixabay







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