Covering Disruptive Technology Powering Business in The Digital Age

FICO, a leading digital decision platform company, recently announced patents for 13 different platforms utilising artificial intelligence (AI), machine learning (ML) and fraud and decision management. These latest patents underscore FICO’s commitment to helping its customers digitally transform their businesses by automating key business processes and decisioning with industry-leading innovations.

FICO now holds a total of 204 US and foreign patents and 85 pending patent applications. FICO has been responsible for multiple industry-changing innovations in AI, ML and other analytics methods. FICO’s rich portfolio of analytics and fraud solutions helps clients grapple with ever larger volumes and variety of data across the enterprise as well as protect businesses against the latest fraud in real time.

“FICO provides our customers the solutions they need at the moments they need them, and driving forward their success is what allows us to thrive as an organisation”, said Scott Zoldi, chief analytics officer, FICO. “We have created and nourished an environment that empowers my colleagues and myself to push the boundaries and continuously drive innovations that help customers succeed. It’s exciting to see our work receive that recognition”.

The patents awarded to FICO and their innovative executives include:

Explaining Machine Learning Models by Tracked Behavioral Latent Features. This invention by Scott Zoldi is a system and method to explain ML model behaviour, which can benefit not only those seeking to meet regulatory requirements when using models but also help guide users of models to assess and increase robustness associated with model governance processes.  This innovation is utilised in the FICO® Falcon® Fraud Manager and FICO® Falcon® X models.

Fast Automatic Explanation of Scored Observations. This patent by Gerald Fahner and Scott Zoldi relates to systems and methods for generating concise explanations of scored observations that strike good and computationally efficient trade-offs between rank-ordering performance and explainability of scored observations, based on a framework of partial dependence functions (PDFs), multilayered neural networks (MNNs), and Latent Explanations Neural Network Scoring (LENNS).

Detection of Compromise of Merchants, ATMS and Networks. This patent by Scott Zoldi relates to the generation of compromise profiles for financial merchants and accounts based on a comparison of reported fraud data with an account profile, account transaction profile, merchant device profile and merchant device account history profile to quickly identify when account information has been obtained by an unauthorised third party and when. The systems and methods claimed by the patent relate to FICO offerings for point of compromise and mass compromise detection.

System and Method for Linearizing Messages from Data Sources for Optimised High-Performance Processing in a Stream Processing System. This innovation by Shalini Raghavan and Tom Traughber relates to the processing of data objects by a distributed stream computing system, and more specifically, the linearised processing of data objects. This technology is integrated with FICO® Decision Management Platform Streaming.

Multilayered Self-Calibrating Analytics. This invention by Scott Zoldi presents multilayered, self-calibrating analytics for detecting fraud in transaction data without substantial historical data, including limited or no outcome data. In markets where transaction history data is not widely available, this invention enables an adaptive selection and grouping of variables relating to real-time transaction data and for processing by a number of independent self-calibrating models. The outputs of these models are combined for an accurate fraud score based on anomaly detection of discovered hidden latent features.

Behavioral Misalignment Detection within Entity Hard Segmentation Utilising Archetype-Clustering. This patent by Scott Zoldi and Joe Murray is an automated way of learning archetypes that capture many aspects of entity behaviour and assigning entities to a mixture of archetypes, such that each entity is represented as a distribution across multiple archetypes. Given those representations in archetypes, anomalous behaviour can be detected by finding a misalignment with a plurality of entities having archetype clustering within a hard segmentation. FICO® Anti-Financial Crime Solutions uses this technology.

Within the last 12 months, FICO was named a leader in digital decisioning as well as a leader in Innovation, AI Applications and Financial Crime-Enterprise Fraud by leading analyst firms.

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