Major benefits of Big Data Analytics include elimination of complexities and overcoming the issues of information overload. Due to the Covid-19 pandemic the need for timely reporting has become more critical than ever before for the Insurance and Pensions Industry.
BY Joyce Benza
In a number of cases systems are not fully integrated, thereby presenting dissimilar sources of information needing to be interfaced or requiring imports/exports, in order to facilitate effective decision making. Traditionally, the database technology could not handle multiple continuous streams of data and in some cases, it could not modify the input data in real-time, but allowed for relational queries from the backend done by the IT personnel.
Major improvements were realized through Business Intelligence (BI) which is a process which analyses existing data to provide historical, current and predictive events of the business operations.
Big Data Analytics however came in with enhanced benefits and is the process of technologies and strategies used to continue exploring and extracting the insights and performance from the past information to drive successful future business planning.
It ensures that the data is made relevant, precise for intended use, easily accessible and is presented timely and holistically.
Big Data Sources
Big Data can be structured, unstructured or semi-structured.
This means that through the application of appropriate techniques and technologies, Big data Analytics structures and organises it to create required insights.
Sources of Big Data include organisations’ multiple on-premise databases, the data residing in the cloud, social networks and data from other external sources in the supply chain, such as the customers, brokers, banks and payment agents.
Big Data Analytics will enable Insurance companies and Pension Funds to present all their information in a consolidated and more meaningful view in real time.
Where customer data is involved a single view of the ‘truth’ is achieved resulting in effective customer relationship management (CRM).
A typical example could be in relation to Employee Benefits records, where the same customers also exist in Individual Business and Health databases and yet the information is residing on separate databases.
Additional interfaces may also be required to link to the corporate Finance package.
Similarly, for Independent Pensions Funds, the property management systems tend to be stand-alone and interfaces are required to link to the Finance and Investments systems. These processes could be slow, inefficient and have proven to be cumbersome.
Characteristics of Big Data
Before meaningful decisions can be made, an assessment of the attributes of data needs to be made. The “Vs’ of Big data include Volume, Variety, and Velocity.
Volume refers to the large volumes of low density and unstructured data, although in certain situations it could be structured. The Variety denotes the format which includes, text, video, audio and requires processing to facilitate meaningful decision making. Velocity is the pace at which the data streams, and a high rate enables instant decision making.
Proven Benefits of Big Data Analytics
For fraud and compliance, Big Data Analytics can identify patterns that indicate possible fraud and aggregate information to make regulatory reporting much easier. Through classifying past and current products and services attributes planning, launch and roll out of new products is made easy. Predictive maintenance for the Property Business Units can be made easy through analysing potential issues before the problem actually happens. From an innovation perspective, examples could be insights on decisions about financial planning considerations, future customer needs for new products and services and product pricing. Insights into production assessment, customer feedback and complaints, process outages and future demands can lead to operational efficiency. Insights into social networks, web visit data and other sources can lead to achievement of customer centricity and profitability. Insights into financial data can also lead to future profitability.
Big Data Analytics Perspectives
It is entirely up to the organisation to prioritise insights that make their critical business decisions more effective. These may include real-time financial forecasting and real time monitoring, mitigation of risks by optimising complex decisions, identification of causes of failures and problems real time, real time revaluation of risk portfolios, real time assessment of customer behaviors thereby enhancing customer relationship management (CRM).
It can also help in identifying bottlenecks in underwriting claims processing and influence the future performance corrective action. Actuarial Valuation insights could also enable efficiency in this process which tends to give the Industry headaches and sometimes results in several iteration.
The Industry and Global Perspective
Insights may include global perspectives where the companies are part of bigger group. Industry specific metrics, trends and profiles can also provide global in-depth knowledge of clients and operations. Industry specific insights can also enable accurate global predictions, projections and future trends.
Taking Immediate Action
The reality is that the data volumes and the variety continue to increase and therefore the need for the Industry players to start seriously considering the Big Data Analytics concepts and taking action sooner than later.
A number of tools to analyse the Big Data are available both as open source (free downloads) and proprietary ones, which are available at a cost.
They range from older tools to modern Artificial Intelligence driven ones. The availability of data to teach machines instead of writing programs, makes the process much easier and achievable. In order to run pilot projects, organisations could start with open source tools, to enable them to test their desired insights, before investing into more costly tools.
Rita Sallam, a Distinguished VP Analyst at Gartner has advised that “To innovate their way beyond the post covid-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts”.
