book-based test «Spiral Dynamics:
Mastering Values, Leadership, and
Change» (ISBN-13: 978-1405133562)
Sponsors

The Application of Spiral Dynamics to Improve Risk Scoring Models Through Statistical Correlations in the VUCA World

Risk management is crucial yet challenging for lenders in an increasingly volatile, uncertain, complex, and ambiguous (VUCA) world. This article presents a conceptual methodology for integrating Clare W. Graves' Spiral Dynamics framework into statistical credit scoring models. The goal is to enable a more accurate and empathetic evaluation of borrowing risk profiles. However, the approach is hypothetical and does not imply fully proven techniques ready for adoption.


The exploratory methodology aims to uncover potential linkages between financial perspectives tied to different psychological worldviews and credit behaviors. Customized questionnaires for applicants could bring out such correlates for analysis. 


These psychographic factors may supplement traditional credit risk algorithms if significant explanatory connections emerge through responsible statistical modeling. However, real-world testing is needed to substantiate impacts before deployment.


The framework offers a principles-based conceptual demonstration for incorporating psychological factors into balanced AI decision-making. However, material impacts would rely on rigorous empirical validation through controlled trials.


This aspirational overview sets an agenda for future research into the feasibility of integrating social science with ethical data practices in financial infrastructure. Substantiating the hypothetical promise in this framework requires thorough evidence-gathering.


The conceptual approach provides a template for consciously co-evolving humanized digital systems. But speculation should be cautioned before lived results back up the premise. As the closing section emphasizes, "progress relies on rigorous empirical evidence" even while potential remains.


Introduction


Financial services face escalating uncertainty in the current climate. Yet existing algorithms may struggle to make contextual lending decisions. This article outlines an exploratory methodology for integrating developmental psychology models like Clare W. Graves’ Spiral Dynamics framework. The goal is to uncover potential behavioral insights across applicant segments.


The proposed approach may supplement credit scoring factors with tentative psychographic elements related to motivations and perspectives. Illustrative custom surveys could aid in uncovering such hypothesized correlates.


If responsible statistical modeling validates explanatory connections, these speculative psychographic factors might, after ethical reviews, supplement traditional algorithms. However, real-world testing would need to substantiate impacts before any deployment.


While appropriately incorporating psychological factors holds aspirational promise for balanced AI decision-making, efficacy relies on obtaining rigorous empirical evidence. Controlled trials remain imperative to progress beyond conceptual premises.


This section expands on the introductory overview without assuming proven techniques. The subsequent sections illustrate the proposed components of the framework while underscoring the imperative for extensive validation testing. As later sections emphasize – avoiding assumptions and gathering substantive proof determines impact.


Positioning with Alternative Credit Scoring


Supplementing financial data with alternative behavioral signals is an emerging trend in credit risk methodology. Common techniques include:


Alternative Data Scoring

  • Incorporating non-traditional data like rent payments, utility bills, and mobile usage to uncover creditworthiness signals beyond financial statements.


Behavioral Modeling 

  • Statistical algorithms identifying correlations between financial behaviors like online shopping habits, cash withdrawals, risk-seeking attitudes, and default risks.


While both expand data inputs for better assessment, this Spiral Dynamics approach differs in:

  1. Rooted in developmental psychology frameworks to capture evolutive stages that shape financial perspectives over time based on established models like Dr. Graves' work.  
  2. Seeks to integrate modeling with human intelligence, e.g., through loan officer guidance.
  3. Prioritizes ethical deployment through fairness testing and ongoing community review. 

There is promising synergy leveraging psychographic, alternative, and behavioral data in a balanced, explainable, and collaborative framework - upholding both probabilistic and human wisdom. 


Omni-channel information responsibly incorporated widens the risk-scoring aperture. Blending computational pattern findings with emotional intelligence and principles holds potential while mitigating biases.


Here is a framework comparison matrix distinguishing the Spiral Dynamics methodology on key dimensions against alternative credit scoring models:

FrameworkWorldview BasisHuman + Machine FocusEthical Priorities
 Spiral Dynamics Develop. psychology models (Graves)  Fairness testing, explainability, community review 
 Alternative Data Scoring  Non-financial data signals Data Privacy
 Behavioral Modeling Financial behavior patterns Transparency

The ✕ symbol indicates primary machine orientation (though they can incorporate human expertise), unlike Spiral Dynamic's overt human+machine emphasis.


Key Differentiators:

  • Worldview Basis: Rooted in established developmental psychology frameworks to capture evolutive stages shaping financial perspectives.    
  • Human+Machine: Seeks to synergize statistical modeling with nuanced human intelligence.
  • Ethical Priorities: Emphasizes responsible deployment through bias testing, explainability, and community participation. 

Compared to other emerging approaches supplementing traditional credit risk data, the structured comparative analysis sharpens the unique properties of leveraging psychosocial evolution models.


Suggestions to improve credit scoring models


Here are some suggestions to improve credit scoring models using Spiral Dynamics for assessing risk in lending decisions:

  1. Gather data on loan applicants' Spiral stage by administering validated questionnaires like the SDTEST® and mapping responses to corresponding color levels. Include vital demographic variables.
  2. Build statistical models correlating credit risk indicators (defaults, late payments) with Spiral stages. For example, does the safety-seeking Blue mindset have higher repayment consistency?
  3. Incorporate significant psychographic correlations into credit scoring algorithms in addition to traditional factors like income, employment history, etc. For instance, "Having a highly Blue risk aversion profile reduces the probability of delinquent borrowing."
  4. Real-world pilot testing pathways test the viability of developed models through carefully designed trials before they are adopted.
  5. Weight Spiral stage variables proportionately based on predictive capability in multiple regression scoring models. For example, "If score from Blue correlation > 15%, increase base score by 10 points" (illustrative).
  6. Retest the new scoring system against credit performance datasets. Equally important is testing for adverse impacts or biases against any group before adoption.  
  7. Implement dynamic scoring models with periodically refreshed insights from updated psychological and loan data. Build a feedback loop for improving model accuracy.
  8. Loan officers should be provided with customized psychographic information to augment application assessments, especially in complex cases. These insights help structure packages to suit applicant mindsets, such as stricter repayment schedules for Blue.
  9. Expand scope by developing separate psychographic models for lending products (credit cards, mortgages, business banking). People may show divergent financial behavior across contexts aligned with Spiral stage expression.   

In summary, meticulously incorporating Spiral correlations can lead to more prudent, ethical, and tailored lending risk models if rigorously validated. The enhanced psychosocial perspective allows lenders to move from purely economic to human-centered capital evaluation.


1. Gather Data on Loan Applicants' Spiral Stage


An essential prerequisite for building credit risk models that incorporate Spiral Dynamics is gathering data on applicants' psychological development levels based on Dr. Graves' framework. 

One validated psychometric questionnaire is the SDTEST®, which assigns color categories to people based on their worldviews, motivations, and values. Out of 80937 results from 169 countries, results clustered into the 7821 unique motivational patterns. SDTEST® has 28 different VUCA polls that calculate the 9,191 correlation values between stages of development according to the theory of Spiral Dynamics and answer options of these 28 polls. People evolve through different colors as coping systems to handle increasing complexity.

Lenders can customize the standard SDTEST® by selecting about 25 core questions most relevant to financial attitudes and administering them to loan applicants online. For example:
  • I set strict budgets for discretionary expenses 
  • I explore creative future possibilities in my financial planning
  • Tradition guides my monetary principles 
After collecting responses, have them mapped by certified analysts to corresponding colors like Purple, Orange, Green, etc, on the Spiral spectrum. Most applicants get distributed among 2-3 prominent colors with varying saturation.

Additionally, it captures key demographic attributes like age, income range, education level, and risk tolerance. 

This primary Spiral assessment data, credit bureau information, and financial KYC provide the foundation for the next phase - statistical modeling to quantify relationships between developmental levels and repayment behaviors.

Appropriate data privacy practices should govern the use of psychographic information. Transparency over protocols can enable equitable, ethical integration into credit risk algorithms.

2. Build Statistical Models


Once applicant psychographic data has been collected through customized questionnaires mapped to Spiral colors, credit risk analysts can build models quantifying correlations between developmental stages and financial behaviors.

The target variables to predict would be existing credit performance metrics indicating repayment abilities or challenges for each applicant:
  • Number of days past due 
  • Default history
  • Bankruptcy flags
  • Credit utilization 
  • Payment to income ratios
Based on questionnaire responses, a multivariate correlation analysis can identify relationships between these credit indicators and dominant Spiral stages associated with each applicant. 

For example, preliminary insights may reveal that individuals aligned strongly with the order-seeking Blue meme have lower adverse indicators like lower late fee instances. This may indicate more discipline in repayment schedules.

In contrast, the unrestrained Red impulsivity stage may demonstrate higher credit utilization or income ratios. The innovative yet volatile Orange may take more risks, increasing bankruptcies.

Cluster analysis can also combine people with similar Spiral stage concentrations into segments for analysis. Comparisons can uncover further group-level patterns.

These explanatory models would need rigorous testing on broader datasets before extracting signals for credit algorithms. Initially, identifying correlations allows quantifying risks and opportunities associated with developmental mindsets.

3. Incorporate Significant Correlations 


Lenders' most common credit scoring models rely on demographic, financial, and bureau data features. These include age, income, debts owned, payment histories, industry volatility risk, etc. 

Psychographic elements related to mindsets and motivations uncovered through analysis can serve as additional explanatory features for algorithmic learning.

The most significant relationships can be integrated based on correlation strength and predictive power. For example, the model may discover:

"Applicants with a high Blue profile concentration (score >0.7) indicating rules-based financial traditionalism have a 15% lower likelihood of 90+ day delinquency across lending products".

This can translate to:

IF Applicant_Spiral_Blue_Score > 0.70  
   THEN Base_Risk_Score = Base_Risk_Score - 15

Here, the solid Blue correlation deducts penalty points from the base risk score to reduce the likelihood of default. The exact algorithm would require multivariate regression analysis to model more comprehensive datasets accurately.

Similar equations can encode positive risk correlations, such as higher bankruptcy tendencies for unrestrained Red profiles. The coefficients quantitatively link psychographic patterns to financial behaviors.

Incorporating Spiral factors to supplement existing scoring factors can enable better contextualization of credit behaviors. However, teams must prevent unfair biases, ensure explainable models, and analyze enough behavioral data samples for robust correlations.

4. Real-world pilot testing pathways


Their feasibility requires validation through controlled experimental trials before fully integrating revised psychographic credit scoring models.

A randomized controlled pilot can assess the impact of incorporating psychological factors on lending risk calibration. Hypothetical steps:  
  • Select a randomized subsample of past loan applicants spanning the credit spectrum 
  • Retroactively develop Spiral scoring models for this pilot subsample based on theoretical constructs 
  • Compare outputs between existing vs Spiral Dynamics integrated algorithms
  • Gauge whether enhanced models improve predictive accuracy on historical defaults
  • Importantly, evaluate if score improvements arise equivalently across demographic groups
The controlled experiment allows testing core viability in a contained environment across vintage applicants. 

Key assessments include:
  1. Overall risk prediction capacity uptake  
  2. Fairness metrics across customer segments
  3. Operational implementation viability
  4. Client experience acceptability  
Before advancing frameworks, gathering evidence from controlled studies guards against unfounded optimism. If feasibility is substantiated, wider deployment can be planned with greater confidence.

Iterative pilots help crystallize operational and architectural adjustments needed to optimize integrity and performance before scale investment. Responsible experimentation couples conceptual promise with tangible diligence.

5. Weight Variables by Predictive Capability


In multiple regression models, the coefficient values signify the predictive strength of that variable. Higher absolute values imply a more significant influence on the target variable.

For instance, after regression analysis, the Spiral scoring model may yield coefficients (illustrative):

Blue: -0.15  
Red: +0.03
Green: +0.10

Here, Blue has the highest coefficient, suggesting it best explains variances in repayment consistency. Green also contributes significantly.

These coefficients can be directly applied as weights in risk-scoring equations:

If Applicant_Spiral_Blue_Score > 0.75
  Then:  
     Risk_Score = Base_Score - (0.15) * Applicant_Spiral_Blue_Score

If Applicant_Spiral_Green_Score > 0.60  
  Then:
     Risk_Score = Risk_Score + (0.10) * Applicant_Spiral_Green_Score 

The negative Blue coefficient reduces overall risk progression by deducting higher points for Blue applicants.

The positive Green coefficient analogously increments scores for highly Green applicants proportionately.

The relative weighting ensures alignment with predictive capability - factors with more explanatory power have a higher influence.

Of course, the actual algorithm requires extensive testing across demographic segments to avoid unfair biases. But this illustrative example shows one approach to align with the statistical strengths of correlations.

6. Retest Models Before Adoption


Comprehensive testing is imperative before deployment once the credit risk algorithms integrate the most predictive Spiral correlations.

The updated models with psychographic factors should be backtested on at least 6-12 months of historical applicant data. 

The simulated risk scores are compared with the actual subsequent credit performance of those consumers to quantify accuracy improvements.

Testing across varied demographic segments on parameters like:
  • Vintage analysis: Model efficacy across generations
  • Geography: Consistent reliability across regions?  
  • Gender: Fairness for men, women, and non-binary identities

Equally importantly, bias testing frameworks must rigorously assess if any group faces adverse impacts from the new algorithms. Techniques include: 
  • Comparing score distributions across segments
  • Auditing conditions leading to rejection rate deviations
  • Testing sub-groups conform equally to score patterns

All contradictions that surface during testing are addressed before activating the models. No performance efficiency gains justify unfairness risks.

The layered testing funnel - encompassing backtesting, bias mitigation, external audit, and consumer surveys establishes holistic trust in updated systems before initiating change.

7. Continual Model Refreshment 


While initially incorporating Spiral correlations can enhance default probability calibration, financial behaviors evolve along with societal developmental shifts.

Hence, the psychographic questionnaires assessing applicant mindsets and values should be refreshed every 24-36 months.

As the economy goes through cycles of volatility, individual motivations also adapt in response. Capturing these contemporary perspectives requires a periodical re-deployment of surveys.

Refreshed questionnaire data matrices can be inputted into templates for automated reconstruction of correlation models and scoring algorithms.

In parallel, as new loan data accrues, performance time series are appended to back-testing datasets for re-validation across demographic facets.

Ideally, the rebuild iteration also runs new trials around bias testing, explains ability, external audit, and worst testing to uphold equity standards, as constraints may also flux with time. 

Beyond scheduled rebuilds, early warning trigger indicators should be instituted for closer monitoring. 

For example, a spike in population defaults beyond Six Sigma variance across 3 out of 5 regions can trigger an alert for intervention analysis in case systemic shifts are underway. Proactive pattern diagnosis facilitates the development of pre-emptive strategies. 

Together, continually revitalizing questionnaires, forward-looking performance data feeds, and real-time vigilance systems can sustain model relevance amidst turbulence. The living benchmarks also aid in understanding VUCA's root causes.

8. Enable Human-Machine Intelligence 


While algorithmic systems have advantages in speed and automation, human intelligence still exceeds AIs in nuanced aspects like empathy, exception handling, and trust building.

Hence, the Spiral Dynamics scoring model outputs can serve as supplemental guides for loan officers when evaluating applications, rather than as absolute decrees.

The psychographic profiles, based on customised surveys, highlight applicant worldviews, values, and motivations regarding financial planning horizons, debt perspectives, and stability orientation.

In complex cases like borderline application decisions or tailored repayment structure needs, these Spiral insights assist officers in making tailored decisions aligned with the borrower's mindset.

For example, the models may indicate an applicant's high traditionalist Blue profile. This signifies strong habits and fiscal discipline but also inflexibility. Hence the officer can structure a favorable loan package with fixed repayment schedules through automatic debits suited to the personality.

On the other hand, an entrepreneurial Red profile suggests a desire for urgency and risk-taking. Therefore, an adjustable "progressive" interest rate plan allows dynamic alignment to their volatile cash flows while preventing overleverage through built-in checks on amounts.

Such human-empowered assessment of psychographic profiling enables consultative, win-win structuring of lending products, unlocking credit access experiences that ratings alone may miss. The combination balances both rigor and empathy.

9. Customized Models by Lending Vertical


While an overarching psychographic scoring framework can serve as the foundation, nuanced financial behaviors manifest differently across lending contexts. 

Hence it is proposed to develop specialized Spiral scoring algorithms vertically across the following:
  1. Credit Cards 
  2. Auto Loans
  3. Personal Loans
  4. Mortgages
  5. Small Business Lending
Tailored questionnaires for each product cohort can map mindsets and motivations specific to that domain while retaining base template consistency.

Context-tuned stats modeling correlates attitudes and repayment behaviors more precisely to shape unique algorithms.

For instance, an entrepreneur may take aggressive risks in business financing while being conservative on fixed home loans. Or a traditionalist prioritizes paying off mortgages before credit card balances. 

Specialized scoring layered atop product suites thus accounts for the multi-dimensional complexity in borrowing decisions people make by situation. Unlocking this granularity improves predictive validity while avoiding assumptions.

Over time, adding emerging lending instruments like "Buy Now Pay Later" (BNPL) allows for continually updating models to address new volatility dimensions with contextual rigor.

Risks and Mitigations


Deploying psychographic techniques in credit risk models poses ethical challenges if mishandled:  

Public Perception Risks
  • Questionable biases degrading certain groups may spark public distrust, backlash, and reputation damage 
Mitigation: Rigorously vet models for fairness gaps pre and post-launch through bias testing toolkits. Create consumer redressal channels. 


Explainability Risks
  • Inability to interpret scoring factors can undermine trust and regulator acceptance
Mitigation: Maintain the highest documentation standards. Enable intuitive visibility into influential variables.  


Data Reliability Risks
  • Survey fatigue impacting applicant psychometric input quality 
  • Confirmation bias in human mapping of responses to Spiral stages  
Mitigation: Evaluate iterative questionnaires to refine relevance and simplicity. Audit mappings for consistency.

Proactively identifying critical ethical, explainable, and accurate AI challenges allows preemptive mitigation planning through governance frameworks and pilot testing. Prioritizing trust and transparency is foundational. Ongoing community review boards assessing model impacts can address emergent issues.

Here is a risk mitigation hierarchy ordered by priority level and mapping appropriate strategies:

Priority LevelRiskMitigation
 High Public perception backlash from model biases  Rigorous pre and post-launch bias testing audits. Enable fair consumer grievance redressal 
 Moderate  Survey quality issues causing data reliability risks  Iteratively refine questionnaire formats based on applicant feedback. Audit data labelling quality. 
 Low Explainability challenges Enhance model documentation protocols. Build intuitive visualizations of scoring factors

The prioritization considers public perception risks highly likely and impactful if questionable biases emerge. Data quality issues are a moderate priority, given the reliance on applicant surveys. Explainability gaps have lower immediate damage risk.

This relative rating allows proportional resource allocation - with maximum oversight and mitigation investments into bias vetting and ethical transparency safeguards.

The tiered hierarchy sets the foundation for optimized, risk-calibrated project planning and phase-wise mitigation response as the methodology progresses from conceptual to real-world trials.

Regulatory implications


Here is an overview of key regulatory considerations for responsibly operationalizing psychographic techniques in lending:

1. Reporting Protocols
  • Submit algorithmic scoring methodology documents detailing data sources, analytical processes, variable weights, and business logic to regulators.  
  • Disclose psychographic factors used, their correlations, and their proportional impact on credit decisions to ensure transparency.
  • Provide bias testing audits, adverse impact analysis reports, and explainability benchmarks as part of compliance packages.

2. Compliance Audits 
  • Flag higher-risk applicants impacted by psychographic scoring for selective credit file audits.
  • Conduct regular default and rejection rates analysis across customer demographics to catch emerging disparities. 
  • External algorithmic audits before launch and periodically after activation.

3. Community Participation
  • Create customer grievance redressal channels accessible to applicants.
  • Constitute advisory review boards with independent financial, technical, and community experts to evaluate model fairness. 

Proactive regulatory collaboration, external algorithmic audits, community participation, and transparent disclosures establish guardrails for ethically integrating psychographic approaches in lending. Responsible innovation requires higher-order accountability.

Examples of applying specific correlations


Here are examples of applying these specific correlation findings from the survey on financial attitudes to the credit risk modeling context.

The poll Biggest problems facing my country discussed in detail in the article Applying of Spiral Dynamics in Understanding the Biggest Problems Facing the Country Through Statistical Correlations

Biggest problems facing my country

Country
Language
-
Mail
Recalculate
All questions
All questions
The biggest problems facing my country are
1
The biggest problems facing my country are
Answer 1
17%
Answer 2
8%
Answer 3
7%
Answer 4
6%
Answer 5
10%