Impact of a well-designed model

A well-designed model can have a significant impact on the efficiency, accuracy and overall success of an insurance company’s operations. Actuaries use these models to assess and manage risks, set pricing and reserving levels and inform business decisions.

Here are the top five considerations when designing an actuarial model:

Improved Efficiency: A well-designed model can streamline processes, automate repetitive tasks and reduce manual work. This can help actuaries to complete tasks faster and with fewer errors, which can ultimately improve overall efficiency.

Increased Accuracy: By automating certain tasks and reducing the need for manual data entry, the likelihood of errors decreases. This can help actuaries to make more accurate decisions and reduce the risk of costly mistakes.

Better Customer Experience: By automating certain tasks, insurance companies can reduce the time it takes to process claims and provide customers with more timely updates. This can help to improve customer satisfaction and retention rates.

Enhanced Risk Management: By automating the collection and analysis of data, actuaries can identify potential risks more quickly and accurately. This can help senior management to make more informed decisions and reduce the likelihood of financial losses.

Improved Compliance: By automating certain compliance tasks and tracking compliance activities, insurance companies can reduce the risk of non-compliance and potential fines.

To build an actuarial model that is both effective and efficient, it’s important to first have a clear understanding of the business needs and goals that the model is intended to address. This requires a thorough understanding of the company’s financial data, as well as an understanding of the broader economic and market conditions that may impact the business.

Once this foundational understanding is in place, the model can be built using a range of tools and techniques. These might include sophisticated statistical analysis, machine learning algorithms and advanced modelling software.

As the model is developed, it’s important to continually test and refine it, ensuring that the outputs and insights it generates are relevant and actionable for the business. This may require ongoing collaboration with key stakeholders across the organisation, as well as ongoing updates and adjustments to the model as new data becomes available or business conditions change.

Ultimately, an optimal actuarial model can be a powerful tool for driving insights and informing strategic decision-making, but achieving this level of sophistication requires a commitment to ongoing learning and development, as well as a willingness to invest the time and resources necessary to build and maintain an effective model over the long term.

Key design factors

Scalability

Scalability is an essential aspect of model design, especially in insurance companies, as they need to be able to handle large volumes of data and process complex calculations daily. Therefore, actuarial models must be designed to be easily scalable up or down as per the business requirements, without compromising on the model’s accuracy or performance.

To ensure scalability, model designers need to use robust data structures, algorithms and programming techniques that can handle large datasets efficiently. They should also consider the processing speed of the hardware on which the models will be running to ensure optimal performance.

In addition, it’s important to consider the impact of changes to the model design on scalability. For instance, if business requirements change, the model may need to be reconfigured or modified to handle the new data or calculations. The ability to adapt and scale the model quickly and effectively will help to ensure an organisation remains agile and responsive to changing market conditions.

Flexibility

Designing flexible actuarial models ensures they can be adapted to changing business requirements, new data sources and updated regulations.

To achieve flexibility, model designers should use modular design principles, to allow components of the model to be updated or replaced without affecting other parts of the model. They should also ensure that the model is easy to modify by adopting a structured and intuitive approach to coding.

Furthermore, model designers should focus on data quality, ensuring that the model can handle different types of data and sources.

This means that the model should be capable of incorporating new data sets or replacing outdated data with minimal disruption to the rest of the model.

It’s essential to establish a robust model governance process to monitor and review changes made to the model. This includes maintaining documentation, version control and testing to ensure that the model remains accurate and reliable.

Data Security

Insurance companies handle highly sensitive and confidential data related to their customers. That’s why designing actuarial models with data security in mind is critical. The consequences of a security breach can be devastating including damage to reputation, financial losses and legal liabilities.

Robust data security requires model designers to use industry-standard encryption protocols and secure data storage methods to protect the data. They also need to consider the access control mechanisms, ensuring that only authorised personnel have access to the model and data.

The model must be designed with secure coding practices, such as input validation and output filtering, to prevent data injection attacks.

Regular testing and monitoring of the model should also be conducted to identify and mitigate any security vulnerabilities.

It is essential to have a comprehensive data security policy and training programme in place for your employees. This includes educating employees on the importance of data security, defining data handling procedures and conducting regular security audits.

Integration with other systems

Designing actuarial models that can integrate with other systems is essential for insurance companies to operate efficiently and effectively. These systems can include data sources, reporting tools and other software applications used within the company.

To achieve seamless integration, model designers should use open architecture and standard data formats to ensure that the model can easily communicate with other systems. They should also ensure that the model can handle different types of data, such as structured and unstructured data, and be able to transform data formats where necessary.

Designers need to develop APIs or web services that expose the model’s functionality, allowing other systems to provide input, consume the model’s results or trigger the model to run based on certain events or data changes.

Testing and monitoring the integration between the model and other systems are also essential to ensure models function as expected and provide accurate and timely results. This includes conducting regression testing when changes are made to the model or other systems.

Finally, documentation is essential to ensure that other system users understand how to use the model and how it integrates with other systems. This documentation should be clear, concise and up-to-date.

In my next article, I’ll discuss the importance of having well-defined design principles and guidelines that incorporate best practices and consistent approaches across all models within an insurance company.

Models is one of the six enablers that form the Actuarial Performance Management (APM) Framework. To learn more about how we can help you design robust and agile actuarial models to improve efficiency in your organisation contact the team.

The post Unlocking Success: Best Practices for Designing Effective Actuarial Models appeared first on MBE.

In our APM Industry Benchmark Report 2022, Data Architecture was highlighted as one of the lowest-scoring APM enablers. This didn’t come as a surprise to us. We see in actuarial teams across the industry, data is being sourced, cleaned, validated, and transformed all within Excel, and these key data activities are intertwined with the calculations, summarisations or presentations that the Excel workbook was actually created for.

Many organisations have tried to solve this problem by looking for the silver-bullet software or system that will automatically unravel the complexity of their processes. This has led to numerous costly failed transformation projects and added to the statistics of the high failure rates of software implementations.

We believe that to solve the problem successfully, a clear and holistic understanding of the requirements is needed, and decisions on software or components should be delayed until a critical mass of requirements is available to inform the decision. We further believe that by using the software and components already in use, or at most introducing small proof-of-concept implementations of new components where appropriate, a small incremental change cycle can generate and surface invaluable details of the requirements and the constraints that lie within a set of processes.

Excel Power Query is well-positioned to bring about local, integrated, and automated improvements while fitting into a small incremental change cycle. Other options that are also worth considering, depending on the definition of the change cycle, include Alteryx Designer, Power BI, Power Automate, Python and R.

Our expectation is that Excel will still be around in actuarial processes in the foreseeable future. By tackling a change cycle in an innovative way, Excel Power Query can be used to create a very efficient and well-controlled process and, while implementing it, organisations can gather the requirements set that will contribute towards the consideration and implementation of future solutions.

Excel Power Query Key Benefits

1. Existing software which is available immediately
2. Easy to learn and use
3. Connect to data from a variety of sources including files, folders, databases, SharePoint and websites (e.g. xe.com, EIOPA)
4. Automate data process steps without VBA macros
5. Add audit checks and controls
6. Improve data validations with ease
7. Mature tool – has been part of MS Excel since Office 2013!

Get in touch if you would like more information about Excel Power Query and the advantages of adopting an incremental approach to your actuarial transformation.

The post How Excel Power Query can help you adopt an incremental approach to actuarial transformation appeared first on MBE.

Data architecture is the foundation upon which all data-related decisions and processes are built. It’s what ensures that data is properly collected, organised and stored so that it can be effectively used.

Defining a data architecture for the actuarial profession

Your data architecture should be designed in such a way that it meets the specific needs of the actuarial team. At MBE we have clearly defined how data architecture relates directly to the needs of the actuarial profession.

One of six enablers that makes up our APM Framework¹, Data Architecture defines the conceptual data model for risk and actuarial results data and describes how this data flows between applications as well as the storage, management and maintenance of this data.

The goal of data architecture is to translate business needs into data and system requirements, and to manage data and its flow through the organisation.

And to achieve actuarial excellence, we have identified that insurers must have a data architecture that enables efficient and granular analysis across all teams within the business to meet the organisation’s objectives and support the strategic vision.

Planning and executing your data architecture

Designing a successful data architecture is no easy task. But with careful planning and execution, it can be achieved. That’s why it’s important to:

Define your goals. Before you can start designing your data architecture, you need to know what you want to achieve with it. What are your specific goals? What data do you need to collect and store? How will you use that data? Answering these questions will give you the platform to set your goals.

Keep it simple. The more complex your data architecture is, the more difficult it will be to manage and maintain. Try to keep things as straightforward as possible.

Consider scalability. As your data requirements grow, your data architecture will need to be able to scale and be applied across other actuarial processes accordingly.

Be flexible. As data needs and requirements change over time, your data architecture should be able to adapt as well.

Ensure data quality. Make sure that your data architecture includes processes and controls for maintaining data quality.

We take these considerations a stage further when designing data architecture for actuarial teams.
Uppermost in our planning and structuring of a data architecture are these four key success factors:

1. Data flows are correctly optimised and applied across all risk management and actuarial processes.

2. Data consumers (actuaries) can access the data through self-service interfaces and tools that enable them to accelerate their work.

3. Data is well-organised, up to date, cleansed and validated, with a common understanding of data definitions across the organisation.

4. Data models and data flows used in your risk management and actuarial processes are properly documented.

To meet these four success factors here are some tips to help you devise a data architecture that is fit for purpose and will support the actuarial team in its work.

Data is made a shared asset. A modern data architecture must eliminate departmental data silos and give all stakeholders a complete view of the company.

Users/Actuaries are given easy access to data. Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs.

Security is considered paramount. A modern data architecture must be designed with security uppermost, and built to support data policies and access controls directly from the raw data.

Common vocabularies ensure common understanding.  Shared data assets, such as product catalogues, fiscal calendar dimensions, and KPI definitions, require a common vocabulary to help avoid disputes during analysis. Use terminology that everyone understands and produce a definition guide.

Data should be curated. By this we mean that the data collected from different sources needs to be correctly organised and integrated to ensure the value of the data is maintained and that it remains available for reuse. Therefore, it’s worth investing in core functions that perform data curation (such as modelling important relationships, cleansing raw data and curating key dimensions and measures).

Data flows should be optimised for agility. Reducing the number of times data needs to be moved will reduce cost, increase data freshness and optimize enterprise agility.

Communicate with the IT team. Developing better, more effective communications with IT will help enrich your strategy.

If you would like support with assessing your current data architecture or help with developing one that is specific to your strategic actuarial goals, contact the team.

¹ The Actuarial Performance Management (APM) Framework is MBE’s unique and proprietary methodology for transforming actuarial performance. The Framework is a comprehensive, structured approach that underpins our actuarial transformation projects.

Using the APM Framework, we can assess your current actuarial performance relative to six Enablers to identify what and how you need to improve to achieve Actuarial Excellence.

The post How to ensure your data architecture is fit for the modern actuarial team appeared first on MBE.

A large UK insurance company required support in developing a strategy that would improve its actuarial performance by releasing capacity, reducing costs, and streamlining processes.

The leadership team had identified that there was a need to make improvements to certain areas of their actuarial operations including data, processes and systems but were unclear as to where to start, and where to focus their attention.   

In addition, the leadership team required a partner who understood both the insurance sector and the actuarial function at a granular level and was able to take a holistic view of their operations.

Challenge

The client needed to continue to deliver business as usual and meet their ever-demanding reporting and valuation cycles. This left the leadership team with little time to focus on devising a strategy to drive change.

Faced with the broad remit to improve actuarial performance the client’s actuarial leadership team were unclear as to what was involved to achieve this including what their goals were, how to identify the issues and what areas to focus on first.

Approach

Using our blended approach of actuarial, systems and process expertise we have developed the Actuarial Performance Management (APM) Framework. The APM is a structured approach that helps insurers improve their actuarial performance across six enablers: People Roles & Responsibilities, Performance Management, Data Architecture, Actuarial Applications, Data Lifecycle and Processes & Methods.

From the APM Framework, we designed the APM Assessment. The Assessment enables us to undertake a complete analysis of the performance of insurers’ actuarial operations in an efficient, structured and measurable way.  

Phase One

Our Actuarial Performance Management (APM) Assessment was completed by everyone within the client’s actuarial department. The participants were asked to assess their organisation against different success factors that we have identified, to produce an overall score for each of the six APM enablers.

Phase Two

We analysed the results to provide a comprehensive diagnostic of the client’s current actuarial performance across the six APM enablers.

Using our analysis, we gained insights at a granular level as to which enablers (and the related success factors) were having a positive or negative impact on our client’s actuarial performance.

By examining each enabler and the individual results for the success factors, we identified the areas that required attention and with improvement would deliver the greatest impact on actuarial performance. In conjunction with our client’s needs and ambitions we recommended their initial focus should be on processes and methods and data architecture.

Using the quantitative and qualitative outcomes of the assessment we developed a road map for the client that gave them a clear route with quantifiable objectives and strategies on how to improve actuarial performance in the short, medium, and long term.

The team at MBE Consulting, by using the APM Assessment, were able to cut through the complexities that were hampering our thinking and provided us with a straightforward path to making achievable, productive changes to our actuarial performance.

Head of Product & Pricing

Next steps

With a three year strategic roadmap for delivering improvements to the client’s actuarial performance in place, we have been asked to support with the next phase – the design and implementation of a performance improvement programme across their two most underperforming areas of processes and methods and data architecture.

Learn more about how our Actuarial Performance Management Framework could help transform your strategy. Contact us for your complimentary APM assessment.

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