Do you actually know why some insurance companies seem to effortlessly make profitable decisions, while others continue to grope in the dark despite massive investments in technology?
The answer lies hidden in the way organisations handle their most valuable resources – and these resources are long since no longer just capital or employees, but the sheer endless streams of information that flow through systems and processes every day. In an era when Big Data, Smart Data and Data Intelligence have become a strategic imperative for decision-makers, the wheat is no longer separated from the chaff solely by market position or tradition, but by the ability to truly derive actionable insights from volumes of data. The insurance and financial services sector stands at a unique turning point here, as historically grown structures meet disruptive technological possibilities, and this tension harbours both opportunities and significant risks.
From data deluge to strategic resource
Insurance companies have historically collected data on a large scale. Claims reports, policy details, customer histories and risk assessments fill archives and databases. Yet the mere availability of information does not in itself create a competitive advantage. It is only through the intelligent linking and analysis of these data streams that well-founded decisions are enabled. A medium-sized property insurer, for example, processes thousands of claims reports every day from a wide variety of channels. Without suitable analytical tools, valuable patterns remain unrecognised and cases of fraud undetected.
The challenge lies in extracting those insights from the sheer mass of information that are actually relevant to the business and enable operational improvements. Big Data, Smart Data and Data Intelligence for decision-makers in this context means that executives no longer merely consume reports, but must actively understand which questions they can and should ask of their data. Life insurers are already using predictive models to forecast cancellation rates and initiate targeted retention measures. Health insurers are analysing treatment patterns in order to both optimise costs and improve the quality of care. Reinsurers, in turn, are modelling complex catastrophe scenarios to protect their portfolios against extreme events.
Best practice with a AIROI customer
A traditional composite insurer approached us with the challenge that, despite substantial investments in modern analysis systems, decision-making quality in underwriting had not noticeably increased. The specialist departments reported frustration because, although they had access to extensive dashboards, they did not know which metrics were actually relevant to their daily decisions. As part of our transruptions support, we first worked with the management team to identify the critical decision points in the underwriting process. We then developed a training concept that not only imparted technical competence, but above all fostered strategic thinking in data structures. The employees learned to independently formulate hypotheses and test them using the available tools. After six months, the company reported a marked improvement in risk assessment for commercial policies because the underwriters were now able to recognise connections between seemingly unrelated variables and incorporate them into their evaluations.
The transformation from reactive to proactive action
Traditionally, insurance companies worked predominantly reactively. Claims were reported, processed and settled. Risks were assessed on the basis of historical empirical data. This approach is no longer sufficient today. Modern analytical methods enable proactive action to an unprecedented extent. Telematics tariffs in motor insurance illustrate this change particularly impressively. Sensors in the vehicle continuously transmit data on driving behaviour. This information feeds into dynamic risk models. Insurers can thus not only calculate individual premiums, but also initiate preventive measures.
Similar developments can be seen in property insurance, where connected sensors can detect water damage at an early stage and automatically initiate countermeasures before major damage occurs. In health insurance, wearables and health apps enable the continuous recording of vital data, allowing risk factors to be identified earlier and prevention programmes to be designed more specifically. Income protection insurance benefits from analyses of occupational health trends in order to better assess risk groups and optimally schedule rehabilitation measures.
Big Data, Smart Data and Data Intelligence for Underwriting Decision-Makers
Underwriting forms the heart of every insurance company. This is where decisions are made regarding which risks are accepted under which conditions. Traditionally, this decision was based on standardised questionnaires and the empirical knowledge of underwriters. Today, additional data sources are available that enable a significantly more precise risk assessment. Publicly available geospatial data, for example, allows detailed assessments of natural hazards for individual locations. Satellite data supports the evaluation of agricultural risks. Financial metrics and industry analyses are incorporated into the assessment of commercial risks.
The trick is to integrate this diverse information sensibly without neglecting the human factor, because while automated systems can recognise patterns and generate suggestions, the final decision-making responsibility must remain with experienced specialists who can also take into account contextual factors that are not recorded in any database. Industrial insurers, for example, use drone footage and image recognition software to evaluate production facilities, whilst simultaneously experienced engineers on site assess the actual safety standards.
Claims management in the digital age
Claims handling ties up significant resources in insurance companies. At the same time, it offers enormous potential for efficiency gains through the intelligent use of data. Automated initial assessments can process simple claims within minutes. More complex cases are prioritised using algorithms and assigned to the appropriate claims handlers. Fraud detection systems identify suspicious patterns before payouts are made.
Hail damage following a severe storm typically generates thousands of simultaneous claims. Intelligent systems can automatically estimate the cost of the damage, set priorities and deploy claims adjusters in an optimal way. Image recognition software analyses submitted photos and identifies both the extent of the damage and potential attempted fraud. Mobile claim submissions via apps accelerate the entire process while simultaneously increasing customer satisfaction. Electronic health records and digital invoice submission are fundamentally transforming claims processing in health insurance.
Best practice with a AIROI customer
A medium-sized health insurance company approached us because it was having difficulty realising the potential of its newly implemented analytics platform in benefits management. The management board had formulated high expectations for cost savings, but the operational teams felt overwhelmed rather than supported by the new tools. During our support, we initially focused on developing a shared understanding between IT, business departments and executive management. We facilitated workshops in which specific use cases were identified and prioritised. A particularly successful project involved the analysis of treatment pathways for chronic diseases. By linking various data sets, patterns were identified that indicated suboptimal care. The company subsequently developed a case management programme that actively supports policyholders in navigating the healthcare system. Feedback from the customers concerned was predominantly positive, and the cost trajectory in the relevant cases also improved measurably.
Customer centricity through intelligent data utilisation
Customer expectations of their insurers have changed fundamentally. Digital experiences from other industries shape the demands regarding accessibility, speed and personalisation. Insurers must meet these expectations to remain competitive. Intelligent use of data enables personalised offers and individual communication on a large scale. Customer segmentation goes far beyond demographic characteristics.
Behaviour-based models take actual usage behaviour across different channels into account. Predictive analytics identify customers with an increased likelihood of churn so that targeted retention measures can be initiated. Cross-selling algorithms recognise which additional products might be relevant for individual customers. Chatbots and virtual assistants answer simple enquiries around the clock while simultaneously relieving the burden on service centres for more complex concerns. The challenge lies in using these opportunities responsibly without compromising customer trust, as data privacy concerns and the feeling of being monitored can quickly lead to rejection.
Regulatory requirements and ethical dimensions
The use of data in the insurance industry is subject to strict regulatory requirements. The General Data Protection Regulation sets tight limits on the processing of personal information. Insurance supervision regulations demand transparency in algorithm-based decisions. Anti-discrimination laws prohibit the use of certain characteristics for risk assessment or pricing.
Big Data, Smart Data and Data Intelligence for decision-makers this also means knowing and complying with these framework conditions. Compliance requirements must be integrated into data projects from the outset. Obligation to document algorithmic decisions requires traceable processes. The use of external data sources must be checked for legal admissibility. Genetic information, for example, is generally prohibited for insurance purposes in many jurisdictions. Social media data exists in a legal grey area, the use of which must be carefully weighed up.
Beyond the legal requirements, ethical questions also arise which decision-makers cannot ignore, because the social acceptance of data-driven business models depends on them being perceived as fair and transparent, and this is where corporate management has a special responsibility to develop and enforce clear guidelines.
Organisational transformation and capability building
Technical capabilities alone do not generate added value. What is crucial is the organisation's ability to leverage these capabilities. This requires both structural adjustments and the development of new competencies at all levels. Data science teams must work closely with business departments. Leaders require a fundamental understanding of analytical methods. Employees in operational roles must learn to integrate data-driven insights into their daily work.
Many insurance companies have set up specialised analytics units in recent years. The challenge now is to disseminate this expertise throughout the entire organisation. Decentralised data skills in the business departments complement central centres of expertise. Training programmes impart the basics of data analysis to broad groups of employees. Agile working methods foster collaboration between technical and business experts. Change management initiatives address resistance and fears that may be associated with the transformation.
Best practice with a AIROI customer
A multi-line insurance group faced the challenge of developing and implementing a group-wide data strategy. The various subsidiaries possessed differing levels of analytical maturity and, in some cases, incompatible system landscapes. As part of our advisory service, we initially supported the development of a shared vision at executive board level. We then facilitated a strategy process that involved representatives from all divisions, defining both common standards and flexibility for division-specific requirements. A central element was the establishment of a data governance structure that clearly regulated responsibilities while simultaneously enabling innovation. We also supported the design of a cross-divisional competence programme that imparted analytical thinking to managers and subject matter experts alike. After about eighteen months, those responsible reported a significantly improved collaboration between the divisions and initial successful projects built on the shared data infrastructure.
My AIROI Analysis
The insurance industry stands at a decisive turning point in its history. The ability to use data intelligently will determine the success or failure of companies in the coming years. This is not about technology as an end in itself, but about the fundamental question of how organisations make decisions and create value for their customers.
From my consulting practice, I know that the biggest hurdles are rarely technical in nature. The systems are available, the algorithms work. What is frequently missing is the strategic understanding at leadership level as to which questions should be asked in the first place. Many decision-makers report feeling overwhelmed by the possibilities and uncertain about where they should begin. This is where we step in with transruptions coaching, to guide leaders in developing their own data literacy and steering their organisations through the transformation.
Practical examples show that successful data projects are always driven by a clear business benefit. They do not originate in the ivory tower of the IT department, but at the interface between technical expertise and business know-how. The insurers who invest in these capabilities today will be the market leaders tomorrow. Big Data, Smart Data and Data Intelligence for decision-makers is no longer an option, but a strategic necessity, the implementation of which, however, requires guidance and support in order to avoid the many pitfalls that lurk along the way.
Further links from the text above:
[1] German Insurance Association – Digitalisation
[2] BaFin – Digitalisation in insurance supervision
[3] McKinsey – Insurance Insights
[4] Accenture – Insurance Research
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