A study by Orgvue revealed that organizations with access to the right data can make faster decisions, resulting in an average 16% higher profit growth opportunity.
The same research found that 71% of executives regretted making decisions too slowly, with many reporting negative impacts on operational efficiency (35%), employee engagement (34%), and customer satisfaction (29%) due to delays.
Depending on the type of decision-making in your organization, different tools, approaches, and best practices might be required.
Let's dive into the different types of decision-making, and how to utilize business rules engines in making them even better.
Strategic Decision-Making: Setting Long Term Direction
Strategic decisions decide an organization’s direction and scope over a long period. These big decisions impact resource allocation, market positioning and competitive advantage.
Senior executives make strategic decisions by analyzing market trends, competitive landscape and internal capabilities. For example, when an insurance company decides to enter new market segments or develop new products, they’re making strategic decisions that will impact operations for years to come.
Strategic decisions require:
- Deep market analysis and data interpretation.
- Multiple stakeholder considerations.
- Long term resource commitment.
- Understanding of broad economic factors.
A financial services company might consider entering the digital banking space. This strategic decision involves analyzing technological capabilities, regulatory requirements, market demand and potential return on investment. The decision affects everything from IT infrastructure to hiring practices.
Tactical Decisions: Implementing Strategy
Tactical decisions turn strategic vision into actionable plans. Middle managers make these decisions, focusing on medium term goals and departmental objectives.
For example, after a strategic decision to launch a new insurance product, tactical decisions might be:
- Choosing distribution channels.
- Determining pricing structures.
- Allocating department resources.
- Setting performance metrics.
These decisions balance strategic goals with operational realities. A regional manager might decide how to restructure their sales team to support a new product launch, considering both corporate objectives and local market conditions.
Tactical decision makers work with established procedures while having flexibility to adapt to changing circumstances. They bridge strategic direction and day to day operations to ensure smooth implementation of bigger organizational goals.
Operational Decisions: Managing Daily Activities
Operational decisions focus on daily activities that keep the organization running. These decisions happen frequently and often follow standard procedures.
In an insurance company, operational decisions might be:
- Processing policy applications.
- Handling customer service inquiries.
- Managing daily workflow distributions.
- Addressing routine maintenance issues.
Front line managers and supervisors make these decisions based on established guidelines and immediate circumstances. For example, a claims processing supervisor might decide how to re-allocate workload when team members are absent.
Operational decisions require quick thinking and practical problem solving skills. Although they seem less impactful than strategic decisions, their cumulative effect has a big impact on organizational efficiency and customer satisfaction.
Programmed vs. Non-Programmed Decisions
Programmed Decisions
Programmed decisions follow established rules and procedures. These structured decisions happen regularly and have clear parameters for resolution.
Examples in financial services include:
- Credit limit adjustments based on customer history.
- Standard policy renewal processes.
- Regular maintenance schedules.
- Monthly reporting procedures.
Organizations often automate programmed decisions through rules engines and workflow systems. This automation ensures consistency and reduces processing time while maintaining accuracy.
Non-Programmed Decisions
Non-programmed decisions address unique or complex situations without predetermined solutions. These decisions require creative thinking and analysis.
Financial institutions face non-programmed decisions when:
- Responding to unexpected market changes.
- Dealing with unprecedented risk scenarios.
- Addressing unique customer situations.
- Developing new financial products.
Decision makers must gather relevant information, weigh multiple options and consider consequences. For example, an insurance company’s response to a new type of cyber risk would require non-programmed decision-making, combining risk assessment with innovative coverage solutions.
Decision-making styles in insurance and finance
Decision-making styles in insurance and finance vary depending on the complexity of the situation and the individuals or teams involved. These styles impact how decisions are approached, evaluated and executed.
Directive decision-making style
The directive style focuses on speed and clarity. It works best in situations that require quick decisions. In insurance, this style is often used in claims processing where pre-defined rules and procedures guide decisions.
For example, a claims manager might approve or decline a claim based on specific thresholds without much deliberation. This approach ensures consistency and speed, especially in high volume environments.
Conceptual decision-making style
The conceptual style emphasizes long term thinking and innovation. It suits scenarios where multiple variables and potential outcomes need to be considered.
In finance, this style is common in investment strategy development. A board member evaluating a new market entry might analyze economic trends, regulatory environment and competitive dynamics to craft a comprehensive plan. This style encourages creativity and breadth of thinking, making it ideal for strategic initiatives.
Group decision-making in complex scenarios
Group decision-making brings together diverse expertise to address multi faceted challenges. In insurance underwriting, committees often use this approach to assess high value or unusual policies. Each member contributes specialized knowledge such as actuarial analysis, legal compliance or market insights.
Similarly, in finance, credit committees evaluate large loan applications collectively, balancing risk and opportunity. This collaborative approach reduces blind spots and improves decision quality.
Management decision-making in dynamic environments
Management decision-making requires adaptability and precision, especially in dynamic environments. Changing conditions, such as market fluctuations or regulatory updates, demand decisions that are both timely and well-informed.
Use data analytics to monitor trends and predict outcomes, ensuring your choices align with organizational goals. Implement structured frameworks to evaluate options quickly without sacrificing accuracy. Involve cross-functional teams to incorporate diverse perspectives, reducing blind spots.
Equip yourself with tools like business rules engines to automate routine decisions, freeing time for strategic thinking. In dynamic settings, effective decision-making ensures resilience and sustained growth, even amidst uncertainty.
Best practices in decision-making
Structured approaches and modern tools ensure decisions align with business goals and regulatory requirements.
Define decision parameters
Start with a clear understanding of the decision scope and objective. For example, when launching a new insurance product define the target audience, pricing and distribution channels. This prevents misalignment and focuses effort on measurable outcomes.
Use data driven insights
Base decisions on data, not intuition. Analyze historical trends, current metrics and predictive models. For example, use customer segmentation data to tailor financial products or adjust insurance premiums. Data driven decisions reduce uncertainty and improve results.
Balance speed and thoroughness
While some decisions need to be made quickly others benefit from more time. Establish protocols to differentiate between routine and complex scenarios. Automate routine decisions with BREs and reserve human input for high risk or non-standard cases.
Monitor and adapt
Review decision outcomes regularly to identify areas for improvement. In finance track loan repayment rates to refine credit policy. In insurance review claims processing times to improve customer satisfaction. Continuous monitoring ensures processes stay effective and responsive to change.
Future of decision-making
Emerging technologies and changing business needs will change decision-making in insurance and finance.
AI powered personalization
AI will enable hyper-personalized products and services. Insurers will offer policies tailored to individual risk profiles, financial institutions will offer bespoke investment portfolios. This will increase customer satisfaction and loyalty.
Real time decision-making
As data processing speeds up, real time decision-making will become the norm. For example, insurers will assess claims in real time using IoT data from connected devices. Financial institutions will adjust portfolios in real time based on live market conditions.
Regulatory compliance automation
Automation will simplify compliance with complex regulations. BREs and AI will monitor transactions and flag potential violations and generate audit trails. This will reduce compliance costs and mitigate legal risk.
Collaborative ecosystems
Partnerships between insurers, banks and tech providers will create integrated ecosystems. These will streamline processes, improve data sharing and deliver seamless customer experiences. For example, joint platforms could combine insurance underwriting with financial planning tools.
The role of business rules engines in decision-making
Business rules engines (BREs) transform decision-making by automating the application of predefined rules, ensuring compliance, speed, consistency, data driven decisions and fewer errors. They are essential for industries like insurance and finance where precision and compliance is non-negotiable.
Compliance with regulations
BREs embed regulatory requirements into decision-making processes so every action aligns with legal and policy standards. In the financial sector for example, compliance with frameworks like Basel III or GDPR is mandatory. A business rules engine automates these checks, reducing the risk of non-compliance penalties.
Use case: A European bank used a BRE to manage GDPR compliance. When regulations were updated the bank changed its rules in days not months and remained fully compliant without disruption to business. This agility saved time and avoided fines.
Speeding up decision-making
BREs make decisions instantly based on pre-defined rules, eliminating manual delays. This is critical in industries like insurance, where quick responses to claims or policy applications improves customer satisfaction and operational efficiency.
Use case: Higson’s business rules engine processes insurance policy applications in real time. It evaluates risk factors such as age, location, and cover type and issues approvals or rejections instantly. This automation reduces processing time from days to seconds.
Consistency across decisions
BREs apply the same rules across all processes so decisions are the same regardless of who or what makes them. This is especially important in industries where variability can lead to errors or customer dissatisfaction.
Use case: In loan approvals Decerto’s BRE ensures every application is evaluated against the same criteria, such as credit scores and debt to income ratios. This consistency builds trust with customers and regulators.
Error reduction in decision-making
Manual processes are error-prone, especially in complex scenarios. BREs eliminate this risk by automating rule application and validating inputs before making decisions. This is critical in sectors like healthcare and finance where mistakes can have severe consequences.
Use case: In claims processing a healthcare provider used a BRE to automate eligibility checks and payment calculations. The engine applied rules consistently and reduced errors in claims approvals.
Do you need to enhance your decision-making?
Contact us and request a demo. We'll see how Higson can improve decisions in your company.
