Overview

The Begini Psychometric Assessment captures behavioural data by analysing how users interact with a series of structured tasks, rather than relying on what they say about themselves.

This approach focuses on observed behaviour, allowing Begini to generate more reliable and consistent signals for risk assessment.

What makes behavioural data different

Traditional assessments often rely on self-reported answers, where users consciously choose responses.

Behavioural data is different.

It is based on how users act in real-time, including:

• How they make decisions
• How quickly they respond
• How consistent they are
• How they react under changing conditions

This reduces reliance on subjective answers and makes the data harder to manipulate.

Types of behavioural signals captured

During the assessment, Begini captures multiple layers of interaction data.

Decision patterns

• Choices made during tasks
• Risk-taking behaviour
• Trade-offs under uncertainty

These help identify how users approach decisions.

Timing and response behaviour

• Time taken to respond
• Variability in response speed
• Hesitation or rapid answering patterns

Timing data can indicate confidence, uncertainty or inconsistency.

Interaction sequences

• Order of actions taken
• Changes in behaviour across tasks
• Repeated or corrected actions

This helps build a picture of behavioural consistency.

Attention and engagement

• Task completion behaviour
• Signs of distraction or disengagement
• Drop-off patterns

These signals help assess whether the user is genuinely engaging with the assessment.

Session-level behaviour

• Total time to complete
• Flow through the assessment
• Interruptions or irregular patterns

This provides context for interpreting the overall session.

Data beyond inputs

Begini does not only capture final answers or outcomes.

It captures the full interaction layer, including:

• Pre-decision behaviour
• In-task interactions
• Post-action adjustments

This creates a much richer dataset than traditional questionnaires or surveys.

Why this matters for risk assessment

Behavioural signals captured during the assessment have been shown to correlate with real-world financial behaviour.

By analysing these signals, Begini can:

• Differentiate between similar-looking applicants
• Identify higher-risk behavioural patterns
• Improve segmentation across score bands
• Provide insight where traditional data is limited

This allows lenders to make more informed decisions, especially in thin-file environments.

Resistance to manipulation

Because Begini captures how users behave rather than what they claim, the system is more resistant to manipulation.

Indicators of potential manipulation include:

• Random or inconsistent responses
• Extremely fast or uniform answer patterns
• Repeated attempts
• Assisted or coached behaviour

These signals contribute to trust or confidence indicators within the final output.

For more detail, see:

• Fraud Detection & Trust Score

How data feeds into scoring

All captured behavioural data is processed through Begini’s scoring models.

The models analyse patterns across:

• Individual task performance
• Cross-task consistency
• Behavioural trends over time

This results in structured outputs such as risk scores and behavioural insights.

For more detail, see:

• How Scoring Works

Best practices

To ensure high-quality behavioural data:

• Encourage users to complete the assessment in one session
• Provide clear instructions before starting
• Avoid interruptions during the assessment
• Ensure a stable device and connection
• Use consistent identifiers across sessions

High-quality input leads to more reliable outputs.

Next steps

Now that you understand how behavioural data is captured, the next step is to understand how results are presented and used.

See:

• Understanding Assessment Results
• How Scoring Works
• Fraud Detection & Trust Score
• Assessment Troubleshooting