Imagine you have a beautiful and fast supercar that you paid for with your own money… but you can’t drive it. You know all the routes, all the best places to visit; you’re like Google Maps, but the interior of the car looks like a private jet. A load of buttons, screens, indicators, needles, and sensors… and only a few people know how to drive it.
Imagine being able to drive at 200 mph with a V8 roar of the engine like a primal, epic beast, but every time you want to go to the supermarket you have to hire a chauffeur. An expensive chauffeur.
We know running a company that relies on technology can feel like this with the car. This is where rules engines come in. They are a normal car on top of the complicated, incomprehensible interface of the supercar.
Flexibility in Insurance Operations
The insurance industry is undergoing a fundamental shift towards automation and efficiency. Actuarial software and complex coding is creating unnecessary barriers between business users and their goals. In a data centric world, insurance companies need to adapt their processes to stay competitive without requiring technical expertise from their teams.
Business rules are the backbone of insurance operations, controlling everything from policy management to claims.
But the complex data preparation process forces you to manage extensive data pipelines which creates bottlenecks in daily operations. When business users want to change rules or adjust policies they hit barriers that require technical knowledge or IT department intervention.
Actuarial modeling software requires specialized programming skills, creating a dependency that slows down business innovation. Simple changes to business rules means reviewing complex programming language and syntax, turning simple updates into long projects. This technical barrier prevents teams from optimizing resource allocation and responding to market demands.
The monthly valuation process is particularly affected by this. Actuarial modelling leaders focused on strategy are bogged down by technical requirements instead of focusing on the business. The transformation and mapping logic for daily operations becomes a barrier not an enabler of efficient processes.
Cloud computing can solve these problems but only when combined with tools that business users can use. Modern data analytics requires flexibility and speed - qualities that traditional systems with their requirement for deep programming knowledge can’t deliver. Insurance companies need tools that do financial reporting without requiring every user to be a technical expert.
Non-Technical Teams in Insurance
Non-technical teams in insurance companies face daily challenges from complex systems. Business users who know the insurance products inside out often can’t do data manipulation or change business rules without coding skills. This disconnect creates a bottleneck in operations when teams need to analyze data or modify existing processes.
Actuarial financial modeling
This is a harsh reality. Teams responsible for actuarial process optimization struggle with assumption repository databases and standardized reporting formats. While many organizations employ optimized processing languages, the technical complexity of these tools often creates barriers for business users.
Without technical expertise, even small changes to actuarial model logic is a task that requires IT intervention. Actuarial modeling leader focused on strategic planning may find themselves constrained by technical limitations, unable to streamline complex modeling tasks effectively.
Optimizing actuarial processes can unlock huge potential.
Data quality control
Business users need to simplify complex data and maintain robust reporting capabilities, but traditional actuarial software is getting in the way. The modelling tasks that should be routine become complex projects requiring technical expertise, which is potentially slowing business innovation and unnecessary delaying in product development.
Data quality remains a critical concern, with 40% of banks acknowledging that they do not have a consistent tech-driven strategy to manage data effectively. Automating data processing enables data preparation and validation processes that were previously manual and error-prone. Let's be honest – it's hard to "optimize" a human being.
No time to learn
Statistical learning techniques with advanced and predictive modeling is out of reach for many team members. Instead of focusing on analytical capabilities, professionals are spending too much time managing large data pipelines and wrestling with syntax or complex logic. The traditional way of data processing creates a barrier between business expertise and technical implementation.
Over 90% of actuaries rely on Excel for their modeling needs, despite its limitations in managing complex datasets effectively. The Casualty Actuarial Society's survey revealed that over 80% of actuaries identified time constraints as the primary barrier to learning new programming skills necessary for more advanced data manipulation.
Multiple data sources
Data sources are multiplying, making it even more complex to handle and streamline complex modeling tasks. A survey indicated that 57% of finance professionals identified collating and standardizing financial data from various sources as their biggest challenge. Many finance teams report spending excessive time resolving data discrepancies, with 51% of respondents highlighting the time spent "fighting over data" as a significant issue.
Teams need to do data manipulation and financial reporting but are hindered by systems that require reviewing complicated programming language for simple changes. This technical barrier prevents teams from using their actuarial toolkits to the full, so actuaries are forced to code instead of applying their expertise to business problems.
Key Features of Simple Rule Management Tools
Modern rule management platforms change the way business users interact with complex data. Higson’s approach to business rules management shows how simple interfaces can replace complex coding. The decision tables shows how business users can change rules without coding skills.
Automated data processing allows for transformation and mapping logic. Instead of wrestling with complex coding implementation, teams can use visual interfaces to manage their data sources and do data manipulation. This automation extends to assumption repository databases so it’s easier to maintain and update critical business information.
As an example, Audi Japan streamlined its finance department's Request for Approval process, resulting in a 75% reduction in processing time, saving approximately 60 hours per week.
Data quality control is at the heart of these systems. Built-in mechanisms ensures accuracy and supports financial reporting. The standardized reporting format allows teams to produce same output without requiring technical expertise. Data visualization tools helps to understand complex data and turn it into actionable insights.
Speed and Accuracy in Rule Changes
These simple tools simplify complex modelling tasks that are requiring extensive technical expertise. Business users can now enhance analytical capabilities without coding skills. The systems support advanced and predictive modelling while maintaining transparency for model governance.
Statistical learning is available through simple interfaces. Teams can add predictive capabilities without coding. The impact on actuarial model logic is huge - what used to take weeks to code now takes hours.
Data manipulation efficiency changes the way teams work. Business users can allocate resources and focus on strategic decisions, not technical implementation. This allows for faster reaction to market changes and business innovation.
Summary
The future of insurance is about enabling non-technical teams. With the right tools business users can focus on what they do best - making strategic decisions and driving the business forward. The time to do this is now as companies that delay will be left behind by more agile competitors.
By 2024, it is expected that 65% of all application development will be performed using low-code/no-code technologies. Approximately 72% of users can develop applications with low-code tools in three months or less. Currently, about 79% of businesses are using citizen development approaches to build web applications within a year.
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