Batanai Matsika 
ONE of the discussion topics at the 43rd Organisation of Eastern and Southern Africa Insurers (OESAI) conference that was held from August 21-25, 2021 in Mombasa, Kenya was titled, “Leveraging Big Data and Data Sharing: Managing and Enhancing Stakeholder experiences in Insurance markets”.
The paper was presented by Tendai Karonga, Insurance Council of Zimbabwe (ICZ) chief executive. The key question was on how big data can assist insurance players in the accomplishment of SDGs.
During the OESAI discussion, it was highlighted that automation of information among African insurance players has been slow even though Covid-19 had accelerated digitisation. Due to the pandemic, there has been a rush to have more people operating on digital platforms in Sub-Saharan Africa. A key catalyst has also been the growth in mobile phone and Internet users. In addition, Africa has a youthful population with about 262 million young people who are the main users of digital technology and form the future base of clientele for the insurance sector.
A common trend is that Big data sharing in Africa is mostly occurring in groups with diversified operations that have mobile network operators. Examples include the likes of Safaricom in Kenya, Telkom in South Africa, and Econet in Zimbabwe. That said, there is a need for insurers to invest in technology that will enable the collection and processing of data beneficial to business growth.
The Impact of Big data and Artificial Intelligence (AI) in the Insurance Sector
The potential to applying big data and artificial intelligence (AI) in diverse aspects of business has caught the imagination of many. Big data and AI could customise business processes and decisions better suited to individual needs and expectations. The term “big data” refers to data that is so large, generated fast or complex that is difficult or impossible to process using traditional methods (manual, mechanical and simple electronic means of processing data).
The act of accessing and storing large amounts of information for analytics has been around since the invention of computers. Big data is generally found in three forms that are structured, semi-structured and unstructured.
Big data solutions, including AI, predictive analytics and machine learning, can sort through huge data sets and return commercially useful insights, which conventional technologies that use basic data comparison rules are unable to perform. According to a report from consulting firm Frost & Sullivan, the Middle East and Africa’s big data analytics market is forecast to grow by 28 percent every year until 2025, reaching revenues of US$68 billion.
Big data is anticipated to affect insurance in several ways and the most widely anticipated is data analytics. The second is underwriting and pricing, with different views on how big data could affect them. Distributions and sales are more obvious avenues given the way big data might enable better targeting and understanding of consumer behaviour. Claims handling and complaints could be streamlined using big data, and marketing could be more targeted with big data.
Data analytics is the science of drawing insights from raw information sources. It is a broad term that encompass many diverse types of data analysis. Essentially any type of information can be subjected to data analytic techniques to get insight that can be used to improve understanding and processes. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms. The four types of data analytics include;
Descriptive analytics – What happened over a given period (analysis of past data);
Diagnostic analytics – Why something happened, which involves more diverse data inputs and some hypothesising;
Predictive analytics – What is likely going to happen in the near future; and
Prescriptive analytics – Suggesting a course of action going forward.
Based on data analytic tools, insurers can take advantage of big data to apply diagnostic and predictive analytics to predict the behaviour of potential policyholders and act based on the outcomes. Another important aspect is data sharing, which is the practice of allowing multiple users and systems access to the same data resource.
This can be done within an organisation or industry such as the broader insurance sector. The major advantage is that shared data expands the amount of data that can then be analysed. In addition, resources that would have been required to source the data are thus saved and made available to other initiatives. Data and all insights derived from the analysis can then be shared within the organisation or industry leading to more engaging customer experiences and actionable decisions. All in all, below are some of the ways in which insurance can leverage big data;
1.Underwriting Capacity
Insurance companies are using Big data to create repositories of data that clearly outline the quantum and make-up of the risks in their portfolios.
Insurers can scientifically derive their risk exposure, accurately calculate the strength of their balance sheet against the possible claims they can incur. Organisations are then empowered to put in place requisite reinsurance programmes or pooling of resource by the industry to provide for risks requiring high capacity.
2.Customer Acquisition
Every business needs to acquire customers to generate revenue and if the process of acquisition can be made efficient, that would make things simpler. Under social media and the increased use of the Internet, every person generates massive amounts of data via social networks, emails, and feedback.
3.Customer Retention
An insurance business is successful if its customer retention rate is high. Based on customer activity, algorithms can predict the early signs of customer dissatisfaction. Working on the insights provided, companies can quickly react to improve their services and find a solution to the grievances of that customer.
4.Risk Assessment
The whole idea of insurance revolves around risk retention and risk spread. Insurers have always focused on the verification of customers’ information while assessing the risks.
Customers are segmented into different risk classes based on their data to facilitate the decision to either retain or reinsure. Big data technology can increase the efficiency of the whole process of risk assessment.
5.Fraud Prevention and Detection
Big data can be used to save insurance companies against such frauds. Using predictive modeling, insurers can compare a person’s data against past fraudulent profiles and identify cases that require more investigation.
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6.Cost Reductions
Cost-cutting is one of the many benefits of leveraging technology. The increased role of machines in the industry increases efficiency which eventually leads to cost reductions. Big data technology can be leveraged to automate manual processes, making them more efficient and reducing the costs spent on underwriting, claims and administration.
7.New product Development
Customer tastes and needs are always changing. Society evolves through generations. The everchanging environment in which we live shapes what appeals to us. Big Data tracks the ever-changing customer needs. Insurance companies use the customer feedback to enhance current products or design new products that satisfy the new identified customer needs. An organisation that provides customer’s requirements is guaranteed a market for its products.
8.Effects on internal processes
The implementation of Big Data algorithms can help increase the efficiency of most of the processes that require deep brainstorming. Big Data technology allows insurers to work quickly on a customer’s profile. They can check their history, decide on a suitable risk class, form a pricing model, automate claims processing, and deliver the best services. A study on automation by McKinsey and Company (January 2017) shows that automation saves 43% of the time of insurance employees
9.Artificial Intelligence
In the insurance sector, there are several ways in which AI could be adopted to improve the efficiency of transactions and business processes. Some examples that have been previously examined include advisory services. Robo-advice is being developed for investment management and to provide quotes with automated advice and offerings calculated through algorithms.
Big data also has a key role in achieving Sustainable Development Goals (SDGs). These are a set of goals that are at the centre of the 2030 Agenda for Sustainable Development, adopted by all United Nations member states in 2015. It should be highlighted that critical data for global, regional, and national development policymaking is still lacking. Many governments still do not have access to adequate data on their entire populations. Below is an outline of how Big Data can be leveraged by the insurance sector to the advantage of several SDGs;
SDG 1: No Poverty
Insurance companies should develop products such as microinsurance that encourage economic participation of the marginalised. The Zambian government farming scheme is an example of harnessing big data to uplift the lives of people on the lower end of the scale. Widening of internet access to the public is one way of assisting in poverty reduction using handsets.
SDG 2: Zero Hunger
The backbone of food provision in Africa is small scale agriculture. The insurance industry should put in place agriculture focused programmes and pools that offer products linked to crop farming and livestock. Small scale farmers will get assistance from big data initiatives that track weather, crop and livestock data.
SDG 3: Good Health and Well-Being
The use of collective health care data will result in health insurance products that respond to the several pandemics facing the continent for example Covid 19. The products will support Africa’s health delivery systems.
SDG 4: Quality Education
The education sector is critical for the development of the continent’s professionals. Big Data records that quantify uptake rates, skills needs and education costs will produce education investment insurance products that ensure parents can provide education for their children.
SDG 8: Decent Work and Economic Growth
The majority of Africa’s economic activity is in the informal sector. The insurance industry should provide microinsurance products that support this trade by reducing the risks the informal traders face. Subsistence farmers should benefit from the development of appropriate products. Such products will reduce risk and encourage farming productivity.
SDG 13: Climate Action
Farmers are exposed to the impact of climate change in their operations. The insurance sector should create products or pools that protect farmers from the effects of climate change. By applying big data analytics, a weather index for drought can be developed thus providing a cushion for low-income groups and subsistence farmers.
Overall, there is need for investment by governments and development partners in technological capacity to deepen the effect of Big Data and Data Sharing. Another approach would be for governments to create a conducive environment where pension and long term insurance funds contribute significantly to data infrastructure and related national development projects. We maintain a strong view that the benefits that can be gained from use of Big Data and AI could potentially be wide ranging and high. Thus, it is important that opportunities are available to develop potential innovations that can be brought to insurance production.
Regulatory sandboxes and innovation hubs could facilitate such developments. While there are indeed risks to the technology as well as potential unintended consequences, a balanced but vigilant approach is necessary to ensure maximum benefits for all stakeholders. That said, being conscious of the potential social costs of using Big Data and AI, including privacy and ethical concerns, and being proactive when issues are arising is a key role that policymakers should play.
Matsika is the head of research at Morgan & Co, and Founder of piggybankadvisor.com. He can be reached on +263 78 358 4745 or batanai@morganzim.com / batanai@piggybankadvisor.com
