Artificial intelligence and the profession of actuary 4.0

The insurance sector is currently undergoing a profound transformation : the emergence of new risks (climate change, cyber and epidemic risks) combined with the rise in interest rates have led to the rise of a new generation of insurers. In an ultra-competitive context, customer protection and non-compliance risks are increasing in an environment in full regulatory inflation (Pacte Law / Eckert Law / RGPD / IFRS 17 / KYC, LCB-FT: 5th directive / SAPIN 2 / DDA / CSR…). Here, artificial intelligence could facilitate regulatory compliance and in particular fraud detection. In order to gain a competitive advantage, AI projects could be generalized to pricing, customer experience and coverage personalization. Finally, we must not neglect the possible time savings in the settlement of claims. For our part, we are going to focus on the profession of actuary 4.0.

Since the Covid pandemic, new risks and digitalization have reinforced the transformation of the sector and accelerated the transformation of certain professions. It is certain that the health crisis has accelerated the digitalization of all sectors of the economy and innovations in AI[1]. The insurance sector is no exception. A number of challenges have arisen from this crisis, to which AI provides an adequate response. In this respect, we observe for example the development of insurance for use (its acceleration), on demand, behavioral, parametric… We also see the development of the integration of connected objects for the prevention and evaluation of claims, improving the customer experience, data, automation, dematerialization (simplification, speed, instantaneousness, transparency). AI around cybercrime with a well-integrated CISO (Information Systems Security Manager) is also being strengthened. The insurer is also becoming more and more of an insurer that “accompanies” moments of life, thanks in particular to Insurtechs. The whole ecosystem movement as a whole has gained momentum. To this must be added the significant role of open insurance, with the opening of information systems via APIs (Application Programming Interface) to create open, interoperable platforms and develop partner ecosystems. With RPA[2]there is also an acceleration in the automation of high-volume, low-value tasks. In the field of continuous improvement, it is necessary to improve and digitize the processes and the tactical deployment of RPA solutions. We also see the development of the use of voice and applications with extended / virtual reality to improve the customer experience as well as the amplification of the development of services with connected objects for the prevention and assessment of claims, in particular , and the massive integration of the blockchain (products and services, fraud, compensation, etc.). All of these aspects have particularly accelerated since 2020. We are going to focus here on the profession of actuaries.

In the field of insurance, we will in particular decipher a profession: that of actuaries. Actuaries thanks to AI will be able to have access to better data visualizations, for example by also using BI tools. BI tools will change and accelerate the way actuaries diagnose and understand results and communicate information to stakeholders.

Graphical data allow them to present the results of complex analyzes to audiences unaccustomed to actuarial techniques. Data visualization also offers the possibility of detecting trends in an environment where the amount of information increases significantly. In the field of Machine Learning (ML)/IA, with data structured and used via R, Python, they are used in pricing, underwriting, for carrying out experience studies, predicting policyholder behavior, calculation of provisions. When it comes to Low – Code ETL & Low Code Programming, e.g. Alteryx, Azure Data Factory, these tools will be useful where traditional ETL tools requiring IT support are not sufficient for business needs (eg. too difficult for users to learn quickly) or when IT is not able to provide the data quickly enough. We can also make cloud computing and storage more efficient. For example, Microsoft Azur or Amazone Web Services. Several factors are at the root of this increased use: need for agility, efficiency gains, increase in computing power and storage. Cloud technology therefore allows actuaries to access virtual machines in a cost-effective and efficient way by reducing execution times significantly.

So how will our actuary be enriched with uses currently under development? As far as Unstructured Data is concerned, via R, Python as we have already mentioned, the use cases are more concentrated in research and development, analytics and Pricing, Underwriting and Product Development in comparison with use cases on structured data. Also for the ML/IA documentation generator and as the use of R/Python becomes more prolific among actuaries, the ability to simultaneously generate documentation and reports for the applications and processes being developed will gain in importance. . Finally, when it comes to data governance, for example Collibra, companies see this technology as providing great value to actuaries. The increased use of data, especially in ML/AI applications, will require companies to be more involved in preventing and managing the risks of data misuse and evolution over time, this may be achieved through the use of governance tools. Here Application Programming Interfaces (APIs), as ML/AI models need to be deployed in real time to downstream applications, are likely to gain in importance.

Finally, we must add strong potential in two areas:

  • Robotic Process Automation (RPA):

The direct use of RPA by actuaries is very limited due to other types of automation already in place or because IT implements RPA without actuaries using it practically. However, if several different applications are used in a process, RPA could be useful to streamline these applications.

  • Data Protection Management Enhancement Technology:

For example, Privitar – As regulations on data use evolve, these tools could potentially become more useful to actuaries, indirectly, by helping them use data in a regulatory-compliant way. As such, the digital revolution allows a massive collection of data (big data) that insurance companies must process, store and analyze.

[1]

[2] Automation of robotic processes

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