There is a growing trend in social security to apply AI, particularly to improve customer services through automated 24/7 front-end support and also, more incipiently, automating back-end processes. 

Several social security institutions have implemented intelligent chatbots to improve online customer services through quality 24/7 availability in different branches and types of benefits. Intelligent chatbots can simulate human behaviour and are able to respond autonomously to users' inquiries. They are available 24/7, and can adapt to users' preferences.

The Superintendency of Occupational Risks (Superintendencia de Riesgos del Trabajo – SRT) of Argentina implemented an intelligent chatbot called "Julieta" to respond to inquiries about work injury benefits. The chatbot fulfilled the objectives of providing automated and personalised customer services by responding not only to the most frequent questions but also by inquiring about the status of costumers' operations such as enrolment and benefit applications. Some of the identified key factors include the development of a quality knowledge-base and the permanent training of the chatbot involving a multidisciplinary team.

In turn, the intelligent chatbot implemented by the Norwegian Labour and Welfare Administration (NAV) has enabled the response to an increased demand for information in the context of the COVID-19 crisis. Concretely, during the period March to May 2020, the chatbot responded to more than 8,000 daily inquiries, which compares to a pre-COVID number of 2,000. The key success factors were the chatbot training based on a daily updated knowledge-base, the focus on a specific type of information, and a seamless connection from the chatbot to a human expert. The chatbot is being extended to new topics, and notably to support employers and the self-employed.

In Uruguay, the Social Insurance Bank (Banco de Previsión Social – BPS) implemented an intelligent chatbot to respond to employers’ inquiries about the scheme for domestic workers. By applying natural language processing and dialogue management techniques, the chatbot understands the customer’s intentions and suggests appropriate actions. Deployed in January 2019, the intelligent chatbot currently enables to respond to 97 per cent of all inquiries while the remaining 3 per cent involve an expert staff. The implementation time was about one year, with six months devoted to training and testing. The identified key facilitating factors have been the permanent update of the knowledge-base as well as the development and operation through a multidisciplinary team.

The General Organization for Social Insurance (GOSI) of Saudi Arabia launched an experimental use of intelligent chatbots for service delivery. The objective was to develop an intelligent agent to respond to customers’ inquiries and to simplify certain services and transactions. The agent communicates with customers through different chat and social networking applications.

Some institutions are also using AI to improve back-end processes, notably to process large volumes of data comprising traditional databases as well as unstructured text and images of digitalized paper-based documents.

Employment and Social Development Canada (ESDC) applied AI to identify beneficiaries of the Guaranteed Income Supplement (GIS), which is a cash benefit targeting low-income old-age persons. In two months, Machine Learning models identified over 2000 vulnerable Canadians to be entitled to the GIS by processing more than 10 million records of unstructured text data. In order to maximize the coverage of vulnerable beneficiaries, the business experts of the GIS programme decided that the model should have a high degree of inclusion and intentionally accepted false positives that would have to be reviewed manually.

The experience showed the importance of using representative data and capturing nuances as well as determining the adequate metrics and thresholds for the business needs by building the training dataset together with business experts. As lessons learned, the ESDC highlighted that the quality of the underlying data is crucial and that AI projects require multidisciplinary teams with data scientists and business experts. The main identified risks comprised the selection of proper tools and the data literacy gaps among the staff in the organization.

Finland’s Social Insurance Institution is starting to apply AI in two ways: (i) improving customer services by combining e-services with intelligent chatbots, and (ii) using AI-based image recognition to automate administrative processes by recognizing documents.

Similarly, the National Social Security Institute of Brazil (INSS) is implementing an intelligent chatbot – called Helô – for providing automated responses 24/7 to customers’ inquiries in the context of the myINSS personalised e-services. A first version deployed in May 2020 has already processed about a million of inquiries. The INSS is also using AI to speed up beneficiary death detection, which allows for preventing undue payments.

Likewise, the Auxiliary Unemployment Benefits Fund of Belgium (Caisse auxiliaire de paiement des allocat

ions de chômage – CAPAC) carried out preliminary AI application to process paper forms through Optical Character Recognition (OCR), which however did not lead to satisfying results. Despite these difficulties, CAPAC keeps AI applications on the agenda and plans to develop an intelligent chatbot.

In turn, the Austrian Social Insurance (Dachverband der österreichischen Sozialversicherungsträger – SV) is applying AI for multiple purposes. Firstly, to deploy an intelligent chatbot – OSC Caro – which provides digital assistance to customers in various areas such as childcare allowances, sick pay and reimbursements. In addition, a voice recognition system supports call centre services by automatically forwarding customer inquiries to the corresponding offices. The system’s language model, which is based on AI, was trained to recognize specific terms. Furthermore, AI is also used to implement the automatic dispatching of emails to the corresponding departments with up to 93 per cent of accuracy. Finally, an ongoing project is implementing an AI-based semi-automatic reimbursement process of medical services fees. In this case, AI is applied to automate several tasks such as the recognition of the submitted documents, encoding diagnosis using the standard ICD-10, and extracting required data for the reimbursement (e.g. invoice amount, IBAN). This semi-automatic treatment enables to speed-up the reimbursement process as well as support the involved staff.

At the governmental level, several countries are defining national strategies on Artificial Intelligence. In particular, the Estonian strategy aims at enabling a proactive government based on a life-event service design and delivering personalized services with zero bureaucracy through an intensive application of AI.

The Estonian vision of AI-based digital public services is being put into practice through #KrattAI, which is an interoperable network of AI applications enabling citizens to use public services through voice-based interaction with virtual assistants. The more than 70 ongoing projects under this strategy, with 38 already operational, cover a wide range of areas including environmental applications, emergency support, cybersecurity and social services. In particular, an intelligent chatbot for customer services and processing long-term unemployment risk cases is applied in the context of unemployment insurance.

Artificial Intelligence is gradually becoming a key technology for social security organizations as it enables to increase administrative efficiency by automating processes as well as to assist staff in tasks requiring human decisions.

However, although positive developments can be observed, several challenges also arise. These relate especially to the limitations and risks of AI, and the trade-off between process automation versus human control. Furthermore, the methodological differences between AI and traditional software development pose challenges to institutions carrying out the projects.

Among the critical factors, data availability and quality are highlighted as a must in order to train the AI systems appropriately. Such "data needs" require establishing an organizational strategy to use internal data as well as potentially data from other organizations, and also involves assessing the compliance with data protection regulations.

AI adoption requires specific institutional capacities. Institutions need to have a detailed understanding of the goal of the project, select data that is representative of the real world, choose simple solutions, pay special attention to explainability of the algorithms used, choose models that not only have best results but also pass fairness standards that need to be carefully designed, and finally ensure transparency to ensure accountability.