Overview

Begini’s scoring models transform behavioural data captured during the assessment into structured risk outputs.

Rather than relying on a single input or outcome, the scoring process analyses patterns across the entire assessment to produce a consistent and comparable risk signal.

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What the scoring model uses

The scoring model is built on behavioural data captured throughout the assessment, including:

• Decision-making patterns
• Response timing and variability
• Interaction sequences
• Consistency across tasks
• Engagement and completion behaviour

This data is analysed collectively to identify patterns that correlate with real-world financial behaviour.

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Pattern-based scoring

Begini does not score users based on individual answers alone.

Instead, the model evaluates:

• How users behave across multiple tasks
• Whether behaviours are consistent or contradictory
• How decisions change under different conditions

This creates a more stable and reliable signal than isolated responses.

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Multi-signal analysis

Each assessment generates a large number of behavioural signals.

The scoring model combines these signals to:

• Reduce noise from individual actions
• Identify meaningful behavioural trends
• Improve consistency across different users

This allows the model to produce a single, structured output from complex interaction data.

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Relative risk ranking

The output of the scoring process is a relative risk score.

This means the score is designed to:

• Rank users within a population
• Enable comparison across applicants
• Support segmentation into risk bands

The model is optimised for consistency of ranking rather than relying on absolute values in isolation.

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Calibration and performance

Begini’s scoring models are designed to align with real-world repayment behaviour.

This is achieved through:

• Ongoing model calibration
• Analysis of portfolio performance data
• Continuous refinement of behavioural signals

This ensures that the scoring remains relevant and predictive over time.

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Consistency across score bands

A key objective of the scoring model is to maintain clear separation between different risk levels.

This helps ensure that:

• Lower-risk groups perform better than higher-risk groups
• Score bands remain stable and interpretable
• Decision thresholds can be applied with confidence

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Handling variability

User behaviour can vary due to:

• Device differences
• Environmental factors
• Individual interaction styles

The scoring model is designed to account for this variability by focusing on underlying behavioural patterns rather than surface-level differences.

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Integration with other data

Begini scores are typically used alongside other data sources, such as:

• Traditional credit bureau data
• Application or demographic data
• Internal risk models

This allows lenders to build a more complete view of each applicant.

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What the model does not rely on

To maintain robustness and fairness, the scoring model does not rely solely on:

• Self-reported answers
• Single task outcomes
• Static questionnaire responses

Instead, it focuses on observed behaviour across the full assessment journey.

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Transparency and interpretation

While the full scoring model is proprietary, Begini provides:

• Behavioural insights to explain outcomes
• Trust or confidence indicators
• Consistent score structures for interpretation

This allows users to understand and apply results without needing access to the underlying model.

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Best practices

• Use scores as part of a broader decisioning framework
• Focus on relative ranking and segmentation
• Monitor performance across score bands
• Combine with trust indicators for reliability
• Continuously review outcomes and adjust thresholds

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Next steps

To understand how reliability and manipulation are handled:

• Fraud Detection & Trust Score
• Assessment Troubleshooting