About us / History

History & Background

Statistical Decisions was formed in London in 1992 to demonstrate the true effectiveness of credit scoring and data-driven decisions in managing retail portfolios. Three decades on, the same argument runs through everything we do.

Founded 1992 London & Athens Member of the Tiresias group

The founding partners of Statistical Decisions were staff of Citibank's EMEA Group Credit Office. The partnership was set up around a single proposition: that retail portfolios are better managed with statistical evidence than with judgement alone — and that the evidence has to be explained, not asserted.

Three decades of modelling

1992 Founded

Statistical Decisions is formed in London. Its founding partners come from Citibank's EMEA Group Credit Office, and the partnership sets out to demonstrate the true effectiveness of credit scoring and data-driven decisions for managing retail portfolios.

Early 1990s Recession

A turbulent period for banking, as many economies suffered recession. Mechanical, data-driven approaches in the financial sector were challenged and calls were made to abandon credit scoring altogether.

Later 1990s

The expansion of retail banking, greater sophistication in modelling practice, advances in IT and the wide increase in computing power established credit scoring as best practice for managing large portfolios.

1996 Athens office

The Athens office is established. It would later become the company's main modelling hub.

2000s

Statistical Decisions is established as a leading practitioner of credit scoring techniques, advancing the efficiency of the markets it serves with tools for optimised, data-driven decision-making.

Mid-2000s Basel II

New regulatory frameworks on capital requirements placed credit scoring at the centre of managing and estimating credit risk for retail portfolios. The firm's score development techniques and documentation were acknowledged as compliant, exceeding supervisory requirements.

2010s Modelling hub

The Athens office becomes the company's main modelling hub, advancing local and regional market knowledge on scorecard development and use, and on retail banking practice more broadly.

2016 IFRS 9

The IFRS 9 standard on provisions introduced the requirement to use best estimates — a probabilistic approach. Credit scorecards were again the natural tool for risk classification and for deriving best estimates of default rates, and StatDec supported financial institutions in applying scorecards, alongside other model types, in this area.

2017 Renamed

With respect to its heritage, the company adopted the name many clients and staff already used unofficially, and Statistical Decisions became StatDec. The official name is still used occasionally.

The earlier Statistical Decisions logo: the words Statistical Decisions in navy capitals inside a thin rectangular frame, over a row of vertical rules
The earlier Statistical Decisions mark, carried by the company under its original name before the 2017 change to StatDec.
2020s Beyond banking

The availability and use of data in decision-making and portfolio management expanded beyond banking, into retail merchants, energy providers, telecommunications and other sectors.

September 2025 Tiresias group

StatDec becomes a subsidiary of Tiresias S.A., the credit bureau of Greece, and acts as the group's advisory and analytical arm.

What the Tiresias combination means

As the group's advisory and analytical arm, StatDec enables Tiresias to enter the field of advisory and consulting for the first time, and to offer financial organisations, businesses and individuals comprehensive support on risk management. It also extends Tiresias' existing scoring and analytics services with expertise and staff specialising in new technologies, reinforcing the reliability of the group's data and services with innovation and measurable impact.

More than 30 years of experience in processing large-scale datasets and developing advanced models, combined with Tiresias' data, market presence and solutions, gives the combination a renewed value proposition. Financial institutions, businesses and the public can make better-informed decisions using current modelling techniques — including machine learning and AI-driven transactional and behavioural models — across an expanding range of applications.

Put three decades of modelling to work

Tell us what you are building, validating or defending, and we will tell you how we would approach it.

Get in touch