PRODUCT

FICO® Score

FICO Score is Revolutionising Financial Inclusion in Kenya.

In Kenya’s evolving financial landscape, lenders need data-driven solutions to keep pace with shifting market dynamics and customer behaviour.

Developed in partnership, the FICO® Score is powered by TransUnion® data and is the first of its kind in Kenya. This locally tailored, globally trusted credit risk model enhances lending decisions, financial inclusion and portfolio growth. It leverages Kenyan credit data, reflecting market-specific lending behaviours and conditions.

Whether for traditional loans, microlending or digital lending, it provides greater accuracy, efficiency and predictive strength in assessing borrower risk.

A global standard solution optimised for Kenya

  • Tailored for Kenya: The only FICO Score built using Kenyan consumer credit data for local lending needs
  • Rigorous risk assessments: A single, consistent risk measure covering microlending and traditional lending, eliminating the need for multiple scores
  • Proven predictive power: Built using millions of Kenyan credit records, offering greater accuracy in allocating borrower risk
  • Seamless integration: Supports real-time credit decisioning across all lending sectors, including mortgages, personal loans, credit cards, microlending and auto finance
  • Stronger decision-making: Helps lenders approve credit faster, reduce defaults and improve portfolio performance

How it works

In Kenya’s evolving financial landscape, lenders need data-driven solutions to keep pace with shifting market dynamics and customer behaviour.

Developed in partnership, the FICO® Score is powered by TransUnion® data and is the first of its kind in Kenya. This locally tailored, globally trusted credit risk model enhances lending decisions, financial inclusion and portfolio growth. It leverages Kenyan credit data, reflecting market-specific lending behaviours and conditions.

Whether for traditional loans, microlending or digital lending, it provides greater accuracy, efficiency and predictive strength in assessing borrower risk.

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