Static consumer data profiles consist of fixed attributes such as demographic information and account details and they can be utilized in risk modeling, particularly in industries like finance and insurance.
The models used, typically assess risk based on static attributes without incorporating transactional or behavioral data but when you combine the two, you land on a more dynamically calculated set of results.
The challenge, is that most Master Data Management system don’t hold and retain transactional and behavioural data, so the question, is whether there is a way that you can benefit from both?
Risk Models Using Static Data
In the implementation of Static Risk Models, there is a often a reliance on attributes such as Customer or Client type, an attribute that identifies the nature of the individual. Another attribute will be the Geographic location which would be often used to assess risks based on region specific factors.
Risk models aggregate attributes and their underlying meaning to derive an overall risk score, this might be used for compliance, regulatory reporting, and basic risk assessment, but it can also be used to identify customer health factors, churn risk and other types of risk. Static data oftens lack the nuance that comes from dynamic data inputs, leading to potential misclassifications or oversights in risk evaluation. There are ways that this can be addressed when data is consolidated via other methods.
Advantages of Using Static Data
Static models have long been utilized in various fields, including marketing, finance, and risk assessment. However, their reliance on data at a point in time, can lead to significant shortcomings, particularly in dynamic environments where consumer behavior is constantly evolving. If you’re updating customer data profiles regularly then some of these issues are mitigated.
It is worth considering that static data has some dimensions that lend it to being at least a consistent yardstick for assessment, including the fact that the data remains relatively unchanged over time, providing a stable foundation for analysis. Static data also tends to be quite simplistic which makes it easier to manage and implement compared to dynamic models that require continuous updates and monitoring.
Static data also pretty categorically meets minimum regulatory requirements for KYC (Know Your Customer) processes without the complexities of ongoing data changes, there is no inference and no extrapolation from behavior. Of course another factor is cost-effectiveness. Static customer profiles reduces the need for extensive data collection and analysis resources associated with transactional data.
Limitations Compared to Transactional Data
Static models do not account for recent behavior or changes in consumer patterns, which can lead to outdated assessments. Static models are also limited in their ability to capture seasonal variations and sudden shifts in market behavior. Static models do not adapt to new influences or changes in the economic landscape, leaving assessment vulnerable to missed opportunities and unforeseen challenges. Relying exclusively on stale static data may lead to a narrow view of current market conditions, which would be particularly problematic in an era where consumer preferences can change rapidly. This is why allowing consumers to self serve on their own data can be a differentiator.
Pros and Cons of Different Data Types
| Data Type | Pros | Cons |
|---|---|---|
| Static Data | – Consistent and reliable – Simple implementation – Cost-effective compliance | – Lacks context – Misses trends – Outdated insights |
| Transactional Data | – Captures real-time data – Reflects trends – Provides detailed insights | – High variability – Requires constant updates – Complexer management |
| Static + Transactional | – Comprehensive view – Stability with dynamism – Better risk assessment | – More resource-intensive – Complex integration |
Industry Alignment
Certain industries may benefit more from static such as Financial Services where there is often a reliance on a combination of both static and transactional data for KYC and credit risk assessments. Here, Static data provides foundational insights while transactional data helps in detecting anomalies and patterns indicative of financial crime or credit risk. In the Insurance industry, static profiles are often used for underwriting but increasingly incorporates transaction data to refine risk assessments based on policyholder behaviors. In Retail, utilizing static demographic information alongside transaction history allows one to tailor marketing strategies and more appropriately manage inventory.
So, static consumer data profiles can serve as a useful tool in risk modeling, their effectiveness is significantly enhanced when combined with transactional data. A hybrid approach allows your organization to maintain a balance between stability and responsiveness to changing consumer behaviors. Consider then, how you might bring a union of transactional data together with deduplicated customer data, to a single customer view that can inform and empower your tactical and strategic organizational objectives.
To learn more, contact us at Pretectum today.