Credit scoring – Aavatar IT Solutions

Credit scoring prediction based on personality types

The Aavatar Machine Learning solution for credit scoring analyzes individuals’ digital footprints, identifying patterns in their behavior to predict psychological traits and personality types.

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Every personality type can be defined by a set of psychological traits that influence behavior in various situations. While these traits are generally believed to be genetically determined and remain stable over time, individuals can learn to manage their behaviors and how these traits manifest through socialization and interactions with others. Nevertheless, their inherent psychological type continues to drive their decision-making.

Interestingly, by analyzing a person’s digital footprint, we can accurately determine their personality type. The Aavatar scoring solution leverages this principle, providing insights into personality types for a range of business applications.

Business applications

Credit scoring to assess the likelihood of loan default.

Automated evaluation of candidates for specific roles.

Estimation of employee resignation likelihood and development of a retention strategy.

Enhancing employee engagement and fostering development opportunities.

Methodology of defining type of personality

There are more than 20 established methods for defining personality types. Among these, predictive analytics has demonstrated effective results using the OCEAN methodology, which evaluates five key personality traits:

Extraversion

Neuroticism

Openness to experience

Conscientiousness

Agreeableness

Automated psychological scoring achieves an impressive accuracy rate of 94%, with similar results attainable through the MBTI methodology.

Data sources for credit scoring

The IT infrastructures of large companies—such as corporate social media, retail loyalty programs, bank card operations, and fiscal data—house vast amounts of data on human behavior. Leveraging AI-driven techniques alongside the computational power of modern systems allows for in-depth analysis of these datasets, uncovering insights into an individual’s most likely behaviors and personality traits.