Turning Reputation into a Score
Can a company's credibility be read solely from its balance sheet? Traditional credit scoring looks at numerical data. However, the reliability of an entity is also hidden in what is being said about it, who it is associated with, and how its perception changes over time. This R&D project, which we conducted for a financial institution, was born exactly to fill this gap. We built an analytics engine that extends credit scoring with digital footprints and public reputation signals. The study was successfully completed and its outputs were handed over to the relevant institution.
Featured Results
The system analyzes the entire footprint of a person, company, startup, or brand across social media, news, and open sources. Instead of a static score for each entity, it creates a live reputation profile that changes over time. This profile shows in which content, with whom, and in what context the entity is mentioned on a timeline. The model is positioned as an additional layer to credit scoring, adding digital perception and trust signals alongside financial metrics. Moreover, it goes beyond the standard positive or negative distinction. Users can define their own categories and analyze in which themes an entity is mentioned the most.
Who the Client Is and the World They Live In
We conducted this project as an R&D study for a financial institution and handed the outputs over to the institution. The classic problem with financial evaluation is that it is past-oriented and numerical. A credit score reads an entity's balance sheet and payment history, but it cannot see its current perception and the climate of trust around it. In the real world, however, the reputation of a firm or person is constantly being written and rewritten in news about them, the context in social media, and the other entities they are associated with. These signals reside in scattered, unstructured, and constantly changing sources. Gathering them and turning them into a meaningful score constitutes an R&D problem in itself. The aim of the project was to support financial evaluation not only with numerical data but also with these reputation signals.
Cross-Cutting Concepts and Architecture
The first idea running through this entire project is reading context through time. A reputation is not a photograph of a single moment, but a movie flowing through time; the system's true capability is capturing this movie. The system first extracts meaningful entities and the relationships between them from scattered text. The technical equivalent for this is natural language processing and entity association: entities like people, institutions, and brands are recognized from free text, and the connection between them is established. Because the system repeats this process continuously, a dynamic timeline is formed for each entity. The content in which the entity is mentioned, the people and institutions it is associated with, positive or negative context changes, and periodic densities become visible on this timeline. Ultimately, instead of a static grade, a live profile emerges showing how reliability evolves over time.
The second idea is flexible classification. Most text analysis tools divide the world into positive and negative; real evaluation, however, requires a much more nuanced perspective. That is why we built the system with a structure where users can define their own categories. Filtering can be done according to specific topics, and an analysis can be made of the themes in which an entity is mentioned most. This means shifting from a fixed-label model to a customizable classification engine. The benefit is adaptability: the same engine can be shaped according to the risk definitions of different sectors and different use cases.
How the Story Was Built
We designed the study as an engine that takes an entity at its center and constantly scans and associates all open-source data related to it. When a firm, startup, platform, or person is defined in the system, the system measures with which other entities and in what contexts that entity is mentioned across all written sources, and creates a timeline from this. Thus, the profile remains constantly updated. The score is not calculated and frozen once; it is dynamically updated as sources change. We positioned the model not as a replacement for credit scoring, but as an additional layer next to it.
Building Blocks of the Solution
The multi-source reputation analysis layer constantly scans and associates social media, news, newspapers, and public written sources related to an entity. The time-based entity profile layer creates a dynamic timeline for each entity from this data, visualizing content, relationships, context changes, and periodic densities. The extended scoring layer adds digital perception and trust signals as an additional evaluation dimension to credit scoring. The categorizable analysis layer allows for defining custom categories beyond the standard positive or negative distinction and enables theme-based filtering, making the engine adaptable to different scenarios.
A reputation isn't a photograph of a single moment, it's a movie flowing through time.
The Impact We Created
The project elevated financial evaluation from being a process that merely looks at the past and numbers, to a holistic analysis that also accounts for an entity's current perception and reputation trajectory over time. An entity's reliability level, risk profile, and public perception can now be read not with a single frozen grade, but with a live and multi-dimensional profile. Successfully completed as an R&D study and handed over to the institution, this engine became a concrete example of the new perspective AI adds to financial evaluation.